Engineering
engineering
Dr Nicoletta Nicolaou
The PhD in Medical Sciences: The University of Nicosia Medical School offers the degree PhD in Medical Sciences. The degree is awarded to students who successfully complete an independent research programme that breaks new ground in the chosen field of study. The PhD programme aspires to empower students to become independent researchers, thus advancing innovation and development. The Research Project: We are currently inviting application through a competitive process for high calibre candidates to apply for one PhD Scholarship in the fields of Neuroscience and Biomedical Engineering. The successful candidate will enrol on the PhD programme in Medical Sciences and will work under the Supervision of Dr Nicoletta Nicolaou with expertise in the fields of Neuroscience and Biomedical Engineering at the University of Nicosia Medical School. Project Description: Title of research project: Development of a closed-loop controller for automatic administration of anaesthetic and analgesic agents during surgery using machine learning methods. Background and Rationale: Current practice of anaesthesia during surgery involves administration of a “cocktail” of drugs (anaesthetics, analgesics, myorelaxants) to achieve the desired state of surgical anaesthesia. During surgery the patient is connected to a number of sensors that monitor vital signs (e.g. cardiovascular parameters, breathing etc.). The anaesthesiologist monitors these vital signs (visually on the monitoring device) and makes manual adjustments to the dosages of the different agents (anaesthetics, analgesics, muscle relaxants). In this open-loop approach the anaesthesiologist is effectively the one who manually closes the loop. The disadvantages of this open-loop approach are related mainly to the fact that the anaesthesiologist monitors the vital signs and is required to make a judgement call based on these visual observations as to whether or not adjustments are required to the dosages of the agents administered. These vital signs provide clues as to the underlying patient state, but they are not considered to be reliable indicators of the underlying “level of consciousness” or “depth of anaesthesia”. In a closed-loop system, the loop is closed automatically: the patient state is estimated from the patient vital signs, and the dosages of agents are adjusted automatically by the device. The anaesthesiologist is not part of the automated closed loop, but still has the ability to bypass this automation and intervene manually. Closed-loop (CL) systems provide better stability of cardiovascular parameters (longer duration of heart rate and mean arterial pressure control), better performance and faster recovery compared to open-loop systems. The development of a CL anaesthetic administration system is a very complex process that must integrate information from a number of biological signals coming from the central and autonomic nervous systems. To date there are only a handful of CL systems that have been developed, but not yet routinely available for commercial use in routine surgery. Aims and Objectives: In this PhD Research Project, a CL system for automatic agent administration during surgery under general anaesthesia will be developed and simulated, using machine learning methods. The system will utilize features from the central and autonomic nervous systems (CNS and ANS respectively) for discrimination between awareness, anaesthesia and different levels of anaesthesia (light, surgical, deep anaesthesia). The system will offer improved anaesthetic experience that will be individualized, leading to a better experience (e.g. maintenance at surgical anaesthetic level, stability of cardiovascular activity, less time in recovery, minimal side effects from over-anaesthesia, faster release from hospital). The main aims and objectives of this PhD research project are: 1. Characterize the relationships of real brain and brain-cardiovascular data recorded during surgeries under general anaesthesia using machine learning methods, as well as the relationships between these physiological signals and concentration of anaesthetic and analgesic agents. 2. Develop a closed-loop controller that utilizes the developed machine learning models to automatically modify the volume of anaesthetics and analgesics to achieve and maintain a desired level of (un)consciousness. 3. Develop a simulation that maps an observed or desired anaesthetic state to specific anaesthetic and analgesic dosages. 4. Test the performance of the developed machine learning controller on automatically modifying the anaesthetic and analgesic dosages to maintain a desired level of (un)consciousness as defined in the simulated data. The Scholarship: The Scholarship will have a duration of three to four years and will cover: • The tuition fees for the PhD programme which are €13,500 in total for the first 3 years and €1,500 for year 4. Application for the PhD Scholarship: Candidates should submit an online application through this link (https://www.med.unic.ac.cy/education/phd-in-medical-sciences/?utm_source=PhD-Scholarships-2022) and upload the following supporting documents: • A cover letter clearly stating that they apply for the PhD Scholarship in the fields of Neuroscience and Biomedical Engineering for the PhD Research Project ‘Development of a closed-loop controller for automatic administration of anaesthetic and analgesic agents during surgery using machine learning methods.’ • Copies of the applicant’s qualifications/degree(s) – the application can be assessed with scanned copies, but certified true copies must be provided if the candidate is successful and prior to enrolment on the PhD programme. • Copies of the applicant’s transcript(s) - the application can be assessed with scanned copies, but certified true copies must be provided if the candidate is successful and prior to enrolment on the PhD programme. • Proof of English language proficiency such as IELTS with a score of 7 overall and with a minimum score of 7 in writing or TOEFL iBT with a score of 94 overall and a minimum score of 27 in Writing. Other internationally recognized English language qualifications might be considered upon review. Students from the UK, Ireland USA, Canada (from English speaking provinces), Australia and New Zealand are exempt from the English language requirement. • Two reference letters, of which at least one should be from an academic. • A full Curriculum Vitae (CV). Applications should be submitted by Friday, July 29, 2022 at 5pm. Only fully completed applications, containing all necessary supporting documents will be reviewed. Please use reference number C2 next to your surname when you start your application. Only candidates who are shortlisted will be contacted and invited to an interview.
Eero Simoncelli, Ph.D.
The Center for Neural Science at New York University (NYU), jointly with the Center for Computational Neuroscience (CCN) at the Flatiron Institute of the Simons Foundation, invites applications for an open rank joint position, with a preference for junior or mid-career candidates. We seek exceptional candidates that use computational frameworks to develop concepts, models, and tools for understanding brain function. Areas of interest include sensory representation and perception, memory, decision-making, adaptation and learning, and motor control. A Ph.D. in a relevant field, such as neuroscience, engineering, physics or applied mathematics, is required. Review of applications will begin 28 March 2021. Further information: * Joint position: https://apply.interfolio.com/83845 * NYU Center for Neural Science: https://www.cns.nyu.edu/ * Flatiron Institute Center for Computational Neuroscience: https://www.simonsfoundation.org/flatiron/center-for-computational-neuroscience/
Kerstin Bunte
We offer a postdoctoral researcher position within the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence at the University of Groningen, The Netherlands. The position is funded by an NWO Vidi project named “mechanistic machine learning: combining the explanatory power of dynamic models with the predictive power of machine learning“. Systems of Artificial Intelligence (AI) and Machine Learning (ML) gained a tremendous amount of interest in recent years, demonstrating great performance for a wide variety of tasks, but typically only if they are trained on huge amounts of data. Moreover, frequently no insight into the decision making is available or required. Experts desire to know how their data can inform them about the natural processes being measured. Therefore we develop transparent and interpretable model- and data-driven hybrid methods that are demonstrated for applications in medicine and engineering. As a postdoc, you will work together with Kerstin Bunte and her team within the Intelligent Systems group, as well as a network of interdisciplinary collaborators in the UK and Europe from various fields, such as Computer Science, Engineering and Applied Mathematics.
Bei Xiao
The RA is to pursue research projects of his/her own as well as provide support for research carried out in the Xiao lab. Possible duties include: Building VR/AR experimental interfaces with Unity3D, Python coding for behavioral data analysis, Collecting data for psychophysical experiments, Training machine learning models.
Felipe Tobar
The Initiative for Data & Artificial Intelligence at Universidad de Chile is looking for Postdoctoral Researchers to join a collaborative team of PIs working on theoretical and applied aspects of Data Science. The role of the postholder(s) is twofold: first, they will engage and collaborate in current projects at the Initiative related to statistical machine learning, natural language processing and deep learning, with applications to time series analysis, health informatics, and astroinformatics. Second, they are expected to bring novel research lines affine to those currently featured at the Initiative, possibly in the form of theoretical work or applications to real-world problems of general interest. These positions are offered on a fixed term basis for up to one year with a possibility for a further year extension.
Prof. Dr. Tobias Rose
The selected candidate will investigate the 'Encoding of Landmark Stability and Stability of Landmark Encoding'. You will study visual landmark encoding at the intersection of hippocampal, thalamic, and cortical inputs to retrosplenial cortex. You will use cutting-edge miniature two-photon Ca2+ imaging, enabling you to longitudinally record activity in defined, large neuronal populations and long-range afferents in freely moving animals. You will carry out rigorous neuronal and behavioral analyses within the confines of automatized closed-loop tasks tailored for visual navigation. This will involve the application of advanced tools for dense behavioral quantification, including multi-angle videography, inertial motion sensing, and egocentric recording with head-mounted cameras for the reconstruction of retinal input. Our aim is to gain a comprehensive understanding of the immediate and sustained multi-area neuronal representation of visual landmarks during unrestricted behavior. We aim to elucidate the mechanisms through which stable visual landmarks are encoded and the processes by which these representations are stabilized to facilitate robust allocentric navigation.
Max Garagnani
The project involves implementing a brain-realistic neurocomputational model able to exhibit the spontaneous emergence of cognitive function from a uniform neural substrate, as a result of unsupervised, biologically realistic learning. Specifically, it will focus on modelling the emergence of unexpected (i.e., non stimulus-driven) action decisions using neo-Hebbian reinforcement learning. The final deliverable will be an artificial brain-like cognitive architecture able to learn to act as humans do when driven by intrinsic motivation and spontaneous, exploratory behaviour.
Dr. Nicholas Hatsopoulos
A postdoctoral position is available beginning in 2023 to help develop a brain-machine interface for dexterous control of a cortically-controlled robotic arm and hand. The approach involves creating 1) decoding strategies from electrical signals in motor cortex that enable the user to not only control the movements of the arm and hand but also the forces transmitted through the hand and 2) encoding models to convey tactile sensations to the user through intracortical microstimulation of somatosensory cortex.
Arlindo Oliveira
The Machine Learning and Knowledge Discovery Group of INESC-ID is looking for qualified applicants for three fully funded PhD student positions on topics related with the application of deep learning techniques to problems with societal impact. These positions are funded by a large-scale research project in responsible AI, supported by the Resiliency and Recovery Facility. The successful candidates will pursue a PhD degree in Computer Science and Engineering at Instituto Superior Técnico, in Lisbon Portugal. The broad topics of research are: 1 - Normalization of geolocation records using deep learning techniques, 2 - High confidence information retrieval and question answering, 3 - Application of reinforcement learning methods to the generation of efficient algorithms.
Rainer Stiefelhagen
The Cooperative Graduate School Accessibility through AI-based Assistive Technology (KATE - www.kate.kit.edu) is a new cooperative and interdisciplinary graduate school between the Karlsruhe Institute of Technology (KIT) and the Hochschule Karlsruhe (HKA). The program revolves around investigating state-of-the-art artificial intelligence methods in order to improve the autonomy and participation of persons with special needs. Different dissertation topics ranging from AI-based methods for text, audio, and multimedia document processing, AI methods for interactive training and assistance systems, to investigating the consequences and ethical, legal, social, and societal implications of AI systems for people with disabilities will be offered. The sponsored persons will work on a selected topic scientifically in depth within the framework of their doctorate and will receive an overall view of all relevant topics - including medical causes as well as their effects, the needs of the target groups, AI-based approaches, ethics, technology assessment, and societal aspects - through the exchange within the doctoral college for this purpose.
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New York University is seeking exceptional PhD candidates with strong quantitative training (e.g., physics, mathematics, engineering) coupled with a clear interest in scientific study of the brain. Doctoral programs are flexible, allowing students to pursue research across departmental boundaries. Admissions are handled separately by each department, and students interested in pursuing graduate studies should submit an application to the program that best fits their goals and interests.
Boris Gutkin
A three-year post-doctoral position in theoretical neuroscience is open to explore the mechanisms of interaction between interoceptive cardiac and exteroceptive tactile inputs at the cortical level. We aim to develop and validate a computational model of cardiac and of a somatosensory cortical circuit dynamics in order to determine the conditions under which interactions between exteroceptive and interoceptive inputs occur and which underlying mechanism (e.g., phase-resetting, gating, phasic arousal,..) best explain experimental data. The postdoctoral fellow will be based at the Group for Neural Theory at LNC2, in Boris Gutkin’s team with strong interactions with Catherine Tallon-Baudry’s team. LNC2 is located in the center of Paris within the Cognitive Science Department at Ecole Normale Supérieure, with numerous opportunities to interact with the Paris scientific community at large, in a stimulating and supportive work environment. Group for Neural Theory provides a rich environment and local community for theoretical neuroscience. Lab life is in English, speaking French is not a requirement. Salary according to experience and French rules. Starting date is first semester 2024.
Geoffrey J Goodhill
An NIH-funded collaboration between David Prober (Caltech), Thai Truong (USC) and Geoff Goodhill (Washington University in St Louis) aims to gain new insight into the neural circuits underlying sleep, through a combination of whole-brain neural recordings in zebrafish and theoretical/computational modeling. A postdoc position is available in the Goodhill lab to contribute to the modeling and computational analysis components. Using novel 2-photon imaging technologies Prober and Truong are recording from the entire larval zebrafish brain at single-neuron resolution continuously for long periods of time, examining neural circuit activity during normal day-night cycles and in response to genetic and pharmacological perturbations. The Goodhill lab is analyzing the resulting huge datasets using a variety of sophisticated computational approaches, and using these results to build new theoretical models that reveal how neural circuits interact to govern sleep.
KongFatt Wong-Lin
The successful candidate will develop and apply computational modelling, and theoretical and analytical techniques to understand brain and behavioural data across primate species, and to apply biologically based neural network modelling to elucidate mechanisms underlying perceptual decision-making. The duration of the position is 24 months, from January 2024 till end of 2025. The personnel will be based at the ISRC in Ulster University, working with Prof. KongFatt Wong-Lin and his team, while collaborating closely with international collaborators in the USA and the Republic of Ireland.
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New York University is seeking exceptional PhD candidates with strong quantitative training (e.g., physics, mathematics, engineering) coupled with a clear interest in scientific study of the brain. Doctoral programs are flexible, allowing students to pursue research across departmental boundaries. Admissions are handled separately by each department, and students interested in pursuing graduate studies should submit an application to the program that best fits their goals and interests.
Professor Geoffrey J Goodhill
The Department of Neuroscience at Washington University School of Medicine is currently recruiting investigators with the passion to create knowledge, pursue bold visions, and challenge canonical thinking as we expand into our new 600,000 sq ft purpose-built neurosciences research building. We are now seeking a tenure-track investigator at the level of Assistant Professor to develop an innovative research program in Theoretical/Computational Neuroscience. The successful candidates will join a thriving theoretical/computational neuroscience community at Washington University, including the new Center for Theoretical and Computational Neuroscience. In addition, the Department also has world-class research strengths in systems, circuits and behavior, cellular and molecular neuroscience using a variety of animal models including worms, flies, zebrafish, rodents and non-human primates. We are particularly interested in outstanding researchers who are both creative and collaborative.
Prof. Trung Thanh Nguyen
LJMU's Freeport and Net Zero Transport Thematic Doctoral Pathways (FTDP) programme is offering six full PhD scholarships (UK/international) to develop two PhD cohorts who will apply their skills in AI, operational research, engineering, and science to tackle transport/logistics decarbonisation, with a focus on UK freeports. This call is for the first cohort (3 PhD scholarships) and is open to both UK and international candidates. The three topics for the first cohort are: Optimising the planning and operations of alternative fuel vehicles, Machine learning or material analyses using intelligent sensors for Net Zero, Digitalisation and cyber security for freeports.
Carl Rasmussen, Bernhard Schölkopf
The University of Cambridge Machine Learning Group and the Max Planck Institute for Intelligent Systems Empirical Inference Department in Tübingen are two of the world’s leading centres for machine learning research. In 2014, we launched a new and exciting initiative whereby a small group of select PhD candidates are jointly supervised at both institutions. The principal supervisors are Carl Rasmussen, Neil Lawrence, Ferenc Huszar, Jose Miguel Hernandez-Lobato, David Krueger, Adrian Weller and Rika Antonova at Cambridge University, and Bernhard Schölkopf and other research group leaders at the Max Planck Institute in Tübingen. This program is specific for candidates whose research interests are well-matched to both the principal supervisors in Cambridge and the MPI for Intelligent Systems in Tuebingen. The overall duration of the PhD will be four years, with roughly three years spent at one location, and one year spent at the other location, including initial coursework at the University of Cambridge. Successful PhDs will be officially granted by the University of Cambridge.
Prof. Sacha Jennifer van Albada
PhD and postdoc opportunities with a focus on the simulation of large-scale biological neural networks are available in the Theoretical Neuroanatomy group at Jülich Research Center, Germany. The projects will advance a research program that centers on the full-scale simulation of thalamocortical networks using the simulator NEST. The postdoc position is available in the context of the Henriette Herz Scouting Program of the Humboldt Foundation, and will be offered to a female candidate. The program is particularly aimed at candidates from countries underrepresented in the Humboldt Foundation. We will jointly define a research project and the selected candidate will receive a Humboldt Research Fellowship. The position is available for 24 months for postdocs up to 4 years after the PhD defense and for 18 months for experienced researchers 4-12 years after the PhD defense. The PhD defense should not be more than 12 years ago, and candidates should not have previous or existing links to Germany in terms of study, research stays, or citizenship. Due consideration will be given to any gaps in the CV due to family care or other personal circumstances. The PhD position is open to candidates regardless of gender. The candidate should have a background in physics, mathematics, computer science, biology (or specifically neuroscience), or engineering. Excellent quantitative and analytical skills are highly valued. We offer a structured program guiding doctoral researchers through the PhD work and plenty of opportunities for local and international collaboration. The researchers will be embedded in a vibrant research institute and have links to the University of Cologne, so that candidates can gain teaching/tutoring experience.
Orly Segev
The International M.Sc. Program at The Sagol School of Neuroscience, 2024-25, at Tel Aviv University is a two-year program designed to provide interdisciplinary thinking and knowledge to join the next generation of world-leading neuroscientists. The program is held at the renowned Sagol School of Neuroscience and will train students in the latest cutting-edge neuroscience fields related to biology, psychology, engineering, and other related fields.
Jörn Diedrichsen
We are looking to recruit a new postdoctoral associate for a large collaborative project on the anatomical development of the human cerebellum. The overall goal of the project is to develop a high-resolution normative model of human cerebellar development across the entire life span. The successful candidate will join the Diedrichsen Lab (Western University, Canada) and will work with a team of colleagues at Erasmus Medical Center, the Donders Institute (Netherlands), McGill, Dalhousie, Sick Kids, and UBC (Canada).
I-Chun Lin
The Gatsby Unit is seeking applications for a postdoctoral training fellowship under Dr Agostina Palmigiano, focused on developing theoretical approaches to investigate the mechanisms underlying sensory, motor, or cognitive computations. Responsibilities include the primary execution of the project, opportunities for co-supervision of students, presentation of results at conferences and seminars, and publication in suitable media. The post is initially funded for 2 years with the possibility of a one-year extension.
Dr. Amir Aly
The University of Plymouth has several available positions in Computer Science.
Zoran Tiganj, PhD
The College of Arts and Sciences and the Luddy School of Informatics, Computing, and Engineering at Indiana University Bloomington invite applications for multiple open-rank, tenured or tenure-track faculty positions in one or more of the following areas: artificial intelligence, human intelligence, and machine learning to begin in Fall 2025 or after. Appointments will be in one or more departments, including Cognitive Science, Computer Science, Informatics, Intelligent Systems Engineering, Mathematics, and Psychological and Brain Sciences. We encourage applications from scholars who apply interdisciplinary perspectives across these fields to a variety of domains, including cognitive science, computational social sciences, computer vision, education, engineering, healthcare, mathematics, natural language processing, neuroscience, psychology, robotics, virtual reality, and beyond. Reflecting IU’s strong tradition of interdisciplinary research, we encourage diverse perspectives and innovative research that may intersect with or extend beyond these areas. The positions are part of a new university-wide initiative that aims to transform our understanding of human and artificial intelligence, involving multiple departments and schools, as well as the new Luddy Artificial Intelligence Center.
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New York University is home to a thriving interdisciplinary community of researchers using computational and theoretical approaches in neuroscience. We are interested in exceptional PhD candidates with strong quantitative training (e.g., physics, mathematics, engineering) coupled with a clear interest in scientific study of the brain. A listing of faculty, sorted by their primary departmental affiliation, is given below. Doctoral programs are flexible, allowing students to pursue research across departmental boundaries. Nevertheless, admissions are handled separately by each department, and students interested in pursuing graduate studies should submit an application to the program that best fits their goals and interests.
Scaling Up Bioimaging with Microfluidic Chips
Explore how microfluidic chips can enhance your imaging experiments by increasing control, throughput, or flexibility. In this remote, personalized workshop, participants will receive expert guidance, support and chips to run tests on their own microscopes.
“Development and application of gaze control models for active perception”
Gaze shifts in humans serve to direct high-resolution vision provided by the fovea towards areas in the environment. Gaze can be considered a proxy for attention or indicator of the relative importance of different parts of the environment. In this talk, we discuss the development of generative models of human gaze in response to visual input. We discuss how such models can be learned, both using supervised learning and using implicit feedback as an agent interacts with the environment, the latter being more plausible in biological agents. We also discuss two ways such models can be used. First, they can be used to improve the performance of artificial autonomous systems, in applications such as autonomous navigation. Second, because these models are contingent on the human’s task, goals, and/or state in the context of the environment, observations of gaze can be used to infer information about user intent. This information can be used to improve human-machine and human robot interaction, by making interfaces more anticipative. We discuss example applications in gaze-typing, robotic tele-operation and human-robot interaction.
Relating circuit dynamics to computation: robustness and dimension-specific computation in cortical dynamics
Neural dynamics represent the hard-to-interpret substrate of circuit computations. Advances in large-scale recordings have highlighted the sheer spatiotemporal complexity of circuit dynamics within and across circuits, portraying in detail the difficulty of interpreting such dynamics and relating it to computation. Indeed, even in extremely simplified experimental conditions, one observes high-dimensional temporal dynamics in the relevant circuits. This complexity can be potentially addressed by the notion that not all changes in population activity have equal meaning, i.e., a small change in the evolution of activity along a particular dimension may have a bigger effect on a given computation than a large change in another. We term such conditions dimension-specific computation. Considering motor preparatory activity in a delayed response task we utilized neural recordings performed simultaneously with optogenetic perturbations to probe circuit dynamics. First, we revealed a remarkable robustness in the detailed evolution of certain dimensions of the population activity, beyond what was thought to be the case experimentally and theoretically. Second, the robust dimension in activity space carries nearly all of the decodable behavioral information whereas other non-robust dimensions contained nearly no decodable information, as if the circuit was setup to make informative dimensions stiff, i.e., resistive to perturbations, leaving uninformative dimensions sloppy, i.e., sensitive to perturbations. Third, we show that this robustness can be achieved by a modular organization of circuitry, whereby modules whose dynamics normally evolve independently can correct each other’s dynamics when an individual module is perturbed, a common design feature in robust systems engineering. Finally, we will recent work extending this framework to understanding the neural dynamics underlying preparation of speech.
Brain Emulation Challenge Workshop
Brain Emulation Challenge workshop will tackle cutting-edge topics such as ground-truthing for validation, leveraging artificial datasets generated from virtual brain tissue, and the transformative potential of virtual brain platforms, such as applied to the forthcoming Brain Emulation Challenge.
Brain Emulation Challenge Workshop
Brain Emulation Challenge workshop will tackle cutting-edge topics such as ground-truthing for validation, leveraging artificial datasets generated from virtual brain tissue, and the transformative potential of virtual brain platforms, such as applied to the forthcoming Brain Emulation Challenge.
Toward globally accessible neuroimaging: Building the OSI2ONE MRI Scanner in Paraguay
The Open Source Imaging Initiative has recently released a fully open source low field MRI scanner called the OSI2ONE. We are currently building this system at the Universidad Paraguayo Alemana in Asuncion, Paraguay for a neuroimaging project at a clinic in Bolivia. I will discuss the process of construction, important considerations before you build, and future work planned with this device.
Mathematical and computational modelling of ocular hemodynamics: from theory to applications
Changes in ocular hemodynamics may be indicative of pathological conditions in the eye (e.g. glaucoma, age-related macular degeneration), but also elsewhere in the body (e.g. systemic hypertension, diabetes, neurodegenerative disorders). Thanks to its transparent fluids and structures that allow the light to go through, the eye offers a unique window on the circulation from large to small vessels, and from arteries to veins. Deciphering the causes that lead to changes in ocular hemodynamics in a specific individual could help prevent vision loss as well as aid in the diagnosis and management of diseases beyond the eye. In this talk, we will discuss how mathematical and computational modelling can help in this regard. We will focus on two main factors, namely blood pressure (BP), which drives the blood flow through the vessels, and intraocular pressure (IOP), which compresses the vessels and may impede the flow. Mechanism-driven models translates fundamental principles of physics and physiology into computable equations that allow for identification of cause-to-effect relationships among interplaying factors (e.g. BP, IOP, blood flow). While invaluable for causality, mechanism-driven models are often based on simplifying assumptions to make them tractable for analysis and simulation; however, this often brings into question their relevance beyond theoretical explorations. Data-driven models offer a natural remedy to address these short-comings. Data-driven methods may be supervised (based on labelled training data) or unsupervised (clustering and other data analytics) and they include models based on statistics, machine learning, deep learning and neural networks. Data-driven models naturally thrive on large datasets, making them scalable to a plethora of applications. While invaluable for scalability, data-driven models are often perceived as black- boxes, as their outcomes are difficult to explain in terms of fundamental principles of physics and physiology and this limits the delivery of actionable insights. The combination of mechanism-driven and data-driven models allows us to harness the advantages of both, as mechanism-driven models excel at interpretability but suffer from a lack of scalability, while data-driven models are excellent at scale but suffer in terms of generalizability and insights for hypothesis generation. This combined, integrative approach represents the pillar of the interdisciplinary approach to data science that will be discussed in this talk, with application to ocular hemodynamics and specific examples in glaucoma research.
Computational and mathematical approaches to myopigenesis
Myopia is predicted to affect 50% of all people worldwide by 2050, and is a risk factor for significant, potentially blinding ocular pathologies, such as retinal detachment and glaucoma. Thus, there is significant motivation to better understand the process of myopigenesis and to develop effective anti-myopigenic treatments. In nearly all cases of human myopia, scleral remodeling is an obligate step in the axial elongation that characterizes the condition. Here I will describe the development of a biomechanical assay based on transient unconfined compression of scleral samples. By treating the scleral as a poroelastic material, one can determine scleral biomechanical properties from extremely small samples, such as obtained from the mouse eye. These properties provide proxy measures of scleral remodeling, and have allowed us to identify all-trans retinoic acid (atRA) as a myopigenic stimulus in mice. I will also describe nascent collaborative work on modeling the transport of atRA in the eye.
Generation of Natural Killer Cells from Human Expanded Potential Stem Cells
Feedback control in the nervous system: from cells and circuits to behaviour
The nervous system is fundamentally a closed loop control device: the output of actions continually influences the internal state and subsequent actions. This is true at the single cell and even the molecular level, where “actions” take the form of signals that are fed back to achieve a variety of functions, including homeostasis, excitability and various kinds of multistability that allow switching and storage of memory. It is also true at the behavioural level, where an animal’s motor actions directly influence sensory input on short timescales, and higher level information about goals and intended actions are continually updated on the basis of current and past actions. Studying the brain in a closed loop setting requires a multidisciplinary approach, leveraging engineering and theory as well as advances in measuring and manipulating the nervous system. I will describe our recent attempts to achieve this fusion of approaches at multiple levels in the nervous system, from synaptic signalling to closed loop brain machine interfaces.
Computational models and experimental methods for the human cornea
The eye is a multi-component biological system, where mechanics, optics, transport phenomena and chemical reactions are strictly interlaced, characterized by the typical bio-variability in sizes and material properties. The eye’s response to external action is patient-specific and it can be predicted only by a customized approach, that accounts for the multiple physics and for the intrinsic microstructure of the tissues, developed with the aid of forefront means of computational biomechanics. Our activity in the last years has been devoted to the development of a comprehensive model of the cornea that aims at being entirely patient-specific. While the geometrical aspects are fully under control, given the sophisticated diagnostic machinery able to provide a fully three-dimensional images of the eye, the major difficulties are related to the characterization of the tissues, which require the setup of in-vivo tests to complement the well documented results of in-vitro tests. The interpretation of in-vivo tests is very complex, since the entire structure of the eye is involved and the characterization of the single tissue is not trivial. The availability of micromechanical models constructed from detailed images of the eye represents an important support for the characterization of the corneal tissues, especially in the case of pathologic conditions. In this presentation I will provide an overview of the research developed in our group in terms of computational models and experimental approaches developed for the human cornea.
From spikes to factors: understanding large-scale neural computations
It is widely accepted that human cognition is the product of spiking neurons. Yet even for basic cognitive functions, such as the ability to make decisions or prepare and execute a voluntary movement, the gap between spikes and computation is vast. Only for very simple circuits and reflexes can one explain computations neuron-by-neuron and spike-by-spike. This approach becomes infeasible when neurons are numerous the flow of information is recurrent. To understand computation, one thus requires appropriate abstractions. An increasingly common abstraction is the neural ‘factor’. Factors are central to many explanations in systems neuroscience. Factors provide a framework for describing computational mechanism, and offer a bridge between data and concrete models. Yet there remains some discomfort with this abstraction, and with any attempt to provide mechanistic explanations above that of spikes, neurons, cell-types, and other comfortingly concrete entities. I will explain why, for many networks of spiking neurons, factors are not only a well-defined abstraction, but are critical to understanding computation mechanistically. Indeed, factors are as real as other abstractions we now accept: pressure, temperature, conductance, and even the action potential itself. I use recent empirical results to illustrate how factor-based hypotheses have become essential to the forming and testing of scientific hypotheses. I will also show how embracing factor-level descriptions affords remarkable power when decoding neural activity for neural engineering purposes.
How Children Design by Analogy: The Role of Spatial Thinking
Analogical reasoning is a common reasoning tool for learning and problem-solving. Existing research has extensively studied children’s reasoning when comparing, or choosing from ready-made analogies. Relatively less is known about how children come up with analogies in authentic learning environments. Design education provides a suitable context to investigate how children generate analogies for creative learning purposes. Meanwhile, the frequent use of visual analogies in design provides an additional opportunity to understand the role of spatial reasoning in design-by-analogy. Spatial reasoning is one of the most studied human cognitive factors and is critical to the learning of science, technology, engineering, arts, and mathematics (STEAM). There is growing interest in exploring the interplay between analogical reasoning and spatial reasoning. In this talk, I will share qualitative findings from a case study, where a class of 11-to-12-year-olds in the Netherlands participated in a biomimicry design project. These findings illustrate (1) practical ways to support children’s analogical reasoning in the ideation process and (2) the potential role of spatial reasoning as seen in children mapping form-function relationships in nature analogically and adaptively to those in human designs.
Exploring the Potential of High-Density Data for Neuropsychological Testing with Coregraph
Coregraph is a tool under development that allows us to collect high-density data patterns during the administration of classic neuropsychological tests such as the Trail Making Test and Clock Drawing Test. These tests are widely used to evaluate cognitive function and screen for neurodegenerative disorders, but traditional methods of data collection only yield sparse information, such as test completion time or error types. By contrast, the high-density data collected with Coregraph may contribute to a better understanding of the cognitive processes involved in executing these tests. In addition, Coregraph may potentially revolutionize the field of cognitive evaluation by aiding in the prediction of cognitive deficits and in the identification of early signs of neurodegenerative disorders such as Alzheimer's dementia. By analyzing high-density graphomotor data through techniques like manual feature engineering and machine learning, we can uncover patterns and relationships that would be otherwise hidden with traditional methods of data analysis. We are currently in the process of determining the most effective methods of feature extraction and feature analysis to develop Coregraph to its full potential.
Engineering an inhibitor-resistant human CSF1R variant for microglia replacement
Preclinical fMRI: Why should we care and what it's useful for
Wave-front shaping and circuit optogenetics
INC Day 2022: Neuroethics
Organized by the INC in partnership with the BioMedical Engineering Paris international Master’s program and the NeuroParis Master’s programs and is supported by the Faculty of Sciences of Paris Cité University and the Graduate school Psychological science.
Computational Imaging: Augmenting Optics with Algorithms for Biomedical Microscopy and Neural Imaging
Computational imaging seeks to achieve novel capabilities and overcome conventional limitations by combining optics and algorithms. In this seminar, I will discuss two computational imaging technologies developed in Boston University Computational Imaging Systems lab, including Intensity Diffraction Tomography and Computational Miniature Mesoscope. In our intensity diffraction tomography system, we demonstrate 3D quantitative phase imaging on a simple LED array microscope. We develop both single-scattering and multiple-scattering models to image complex biological samples. In our Computational Miniature Mesoscope, we demonstrate single-shot 3D high-resolution fluorescence imaging across a wide field-of-view in a miniaturized platform. We develop methods to characterize 3D spatially varying aberrations and physical simulator-based deep learning strategies to achieve fast and accurate reconstructions. Broadly, I will discuss how synergies between novel optical instrumentation, physical modeling, and model- and learning-based computational algorithms can push the limits in biomedical microscopy and neural imaging.
Exploration-Based Approach for Computationally Supported Design-by-Analogy
Engineering designers practice design-by-analogy (DbA) during concept generation to retrieve knowledge from external sources or memory as inspiration to solve design problems. DbA is a tool for innovation that involves retrieving analogies from a source domain and transferring the knowledge to a target domain. While DbA produces innovative results, designers often come up with analogies by themselves or through serendipitous, random encounters. Computational support systems for searching analogies have been developed to facilitate DbA in systematic design practice. However, many systems have focused on a query-based approach, in which a designer inputs a keyword or a query function and is returned a set of algorithmically determined stimuli. In this presentation, a new analogical retrieval process that leverages a visual interaction technique is introduced. It enables designers to explore a space of analogies, rather than be constrained by what’s retrieved by a query-based algorithm. With an exploration-based DbA tool, designers have the potential to uncover more useful and unexpected inspiration for innovative design solutions.
Feedforward and feedback processes in visual recognition
Progress in deep learning has spawned great successes in many engineering applications. As a prime example, convolutional neural networks, a type of feedforward neural networks, are now approaching – and sometimes even surpassing – human accuracy on a variety of visual recognition tasks. In this talk, however, I will show that these neural networks and their recent extensions exhibit a limited ability to solve seemingly simple visual reasoning problems involving incremental grouping, similarity, and spatial relation judgments. Our group has developed a recurrent network model of classical and extra-classical receptive field circuits that is constrained by the anatomy and physiology of the visual cortex. The model was shown to account for diverse visual illusions providing computational evidence for a novel canonical circuit that is shared across visual modalities. I will show that this computational neuroscience model can be turned into a modern end-to-end trainable deep recurrent network architecture that addresses some of the shortcomings exhibited by state-of-the-art feedforward networks for solving complex visual reasoning tasks. This suggests that neuroscience may contribute powerful new ideas and approaches to computer science and artificial intelligence.
Membrane mechanics meet minimal manifolds
Changes in the geometry and topology of self-assembled membranes underlie diverse processes across cellular biology and engineering. Similar to lipid bilayers, monolayer colloidal membranes studied by the Sharma (IISc Bangalore) and Dogic (UCSB) Labs have in-plane fluid-like dynamics and out-of-plane bending elasticity, but their open edges and micron length scale provide a tractable system to study the equilibrium energetics and dynamic pathways of membrane assembly and reconfiguration. First, we discuss how doping colloidal membranes with short miscible rods transforms disk-shaped membranes into saddle-shaped minimal surfaces with complex edge structures. Theoretical modeling demonstrates that their formation is driven by increasing positive Gaussian modulus, which in turn is controlled by the fraction of short rods. Further coalescence of saddle-shaped surfaces leads to exotic topologically distinct structures, including shapes similar to catenoids, tri-noids, four-noids, and higher order structures. We then mathematically explore the mechanics of these catenoid-like structures subject to an external axial force and elucidate their intimate connection to two problems whose solutions date back to Euler: the shape of an area-minimizing soap film and the buckling of a slender rod under compression. A perturbation theory argument directly relates the tensions of membranes to the stability properties of minimal surfaces. We also investigate the effects of including a Gaussian curvature modulus, which, for small enough membranes, causes the axial force to diverge as the ring separation approaches its maximal value.
Reverse-engineering Drosophila behavior
Measuring the Motions of Mice: Open source tracking with the KineMouse Wheel
Who says you can't reinvent the wheel?! This running wheel for head-fixed mice allows 3D reconstruction of body kinematics using a single camera and DeepLabCut (or similar) software. A lightweight, transparent polycarbonate floor and a mirror mounted on the inside allow two views to be captured simultaneously. All parts are commercially available or laser cut
2nd In-Vitro 2D & 3D Neuronal Networks Summit
The event is open to everyone interested in Neuroscience, Cell Biology, Drug Discovery, Disease Modeling, and Bio/Neuroengineering! This meeting is a platform bringing scientists from all over the world together and fostering scientific exchange and collaboration.
2nd In-Vitro 2D & 3D Neuronal Networks Summit
The event is open to everyone interested in Neuroscience, Cell Biology, Drug Discovery, Disease Modeling, and Bio/Neuroengineering! This meeting is a platform bringing scientists from all over the world together and fostering scientific exchange and collaboration.
Spatial alignment supports visual comparisons
Visual comparisons are ubiquitous, and they can also be an important source for learning (e.g., Gentner et al., 2016; Kok et al., 2013). In science, technology, engineering, and math (STEM), key information is often conveyed through figures, graphs, and diagrams (Mayer, 1993). Comparing within and across visuals is critical for gleaning insight into the underlying concepts, structures, and processes that they represent. This talk addresses how people make visual comparisons and how visual comparisons can be best supported to improve learning. In particular, the talk will present a series of studies exploring the Spatial Alignment Principle (Matlen et al., 2020), derived from Structure-Mapping Theory (Gentner, 1983). Structure-mapping theory proposes that comparisons involve a process of finding correspondences between elements based on structured relationships. The Spatial Alignment Principle suggests that spatially arranging compared figures directly – to support correct correspondences and minimize interference from incorrect correspondences – will facilitate visual comparisons. We find that direct placement can facilitate visual comparison in educationally relevant stimuli, and that it may be especially important when figures are less familiar. We also present complementary evidence illustrating the preponderance of visual comparisons in 7th grade science textbooks.
Finding needles in the neural haystack: unsupervised analyses of noisy data
In modern neuroscience, we often want to extract information from recordings of many neurons in the brain. Unfortunately, the activity of individual neurons is very noisy, making it difficult to relate to cognition and behavior. Thankfully, we can use the correlations across time and neurons to denoise the data we record. In particular, using recent advances in machine learning, we can build models which harness this structure in the data to extract more interpretable signals. In this talk, we present two such methods as well as examples of how they can help us gain further insights into the neural underpinnings of behavior.
NMC4 Keynote: A network perspective on cognitive effort
Cognitive effort has long been an important explanatory factor in the study of human behavior in health and disease. Yet, the biophysical nature of cognitive effort remains far from understood. In this talk, I will offer a network perspective on cognitive effort. I will begin by canvassing a recent perspective that casts cognitive effort in the framework of network control theory, developed and frequently used in systems engineering. The theory describes how much energy is required to move the brain from one activity state to another, when activity is constrained to pass along physical pathways in a connectome. I will then turn to empirical studies that link this theoretical notion of energy with cognitive effort in a behaviorally demanding task, and with a metabolic notion of energy as accessible to FDG-PET imaging. Finally, I will ask how this structurally-constrained activity flow can provide us with insights about the brain’s non-equilibrium nature. Using a general tool for quantifying entropy production in macroscopic systems, I will provide evidence to suggest that states of marked cognitive effort are also states of greater entropy production. Collectively, the work I discuss offers a complementary view of cognitive effort as a dynamical process occurring atop a complex network.
The wonders and complexities of brain microstructure: Enabling biomedical engineering studies combining imaging and models
Brain microstructure plays a key role in driving the transport of drug molecules directly administered to the brain tissue as in Convection-Enhanced Delivery procedures. This study reports the first systematic attempt to characterize the cytoarchitecture of commissural, long association and projection fiber, namely: the corpus callosum, the fornix and the corona radiata. Ovine samples from three different subjects have been imaged using scanning electron microscope combined with focused ion beam milling. Particular focus has been given to the axons. For each tract, a 3D reconstruction of relatively large volumes (including a significant number of axons) has been performed. Namely, outer axonal ellipticity, outer axonal cross-sectional area and its relative perimeter have been measured. This study [1] provides useful insight into the fibrous organization of the tissue that can be described as composite material presenting elliptical tortuous tubular fibers, leading to a workflow to enable accurate simulations of drug delivery which include well-resolved microstructural features. As a demonstration of the use of these imaging and reconstruction techniques, our research analyses the hydraulic permeability of two white matter (WM) areas (corpus callosum and fornix) whose three-dimensional microstructure was reconstructed starting from the acquisition of the electron microscopy images. Considering that the white matter structure is mainly composed of elongated and parallel axons we computed the permeability along the parallel and perpendicular directions using computational fluid dynamics [2]. The results show a statistically significant difference between parallel and perpendicular permeability, with a ratio about 2 in both the white matter structures analysed, thus demonstrating their anisotropic behaviour. This is in line with the experimental results obtained using perfusion of brain matter [3]. Moreover, we find a significant difference between permeability in corpus callosum and fornix, which suggests that also the white matter heterogeneity should be considered when modelling drug transport in the brain. Our findings, that demonstrate and quantify the anisotropic and heterogeneous character of the white matter, represent a fundamental contribution not only for drug delivery modelling but also for shedding light on the interstitial transport mechanisms in the extracellular space. These and many other discoveries will be discussed during the talk." "1. https://www.researchsquare.com/article/rs-686577/v1, 2. https://www.pnas.org/content/118/36/e2105328118, 3. https://ieeexplore.ieee.org/abstract/document/9198110
From Sensors to Health Data Analysis
Talks and panel discussions around the LifeQ process of moving from the embedded engineering of sensors on edge devices to big health data analysis in the cloud.
Embodied Artificial Intelligence: Building brain and body together in bio-inspired robots
TBC
Neural mechanisms of altered states of consciousness under psychedelics
Interest in psychedelic compounds is growing due to their remarkable potential for understanding altered neural states and their breakthrough status to treat various psychiatric disorders. However, there are major knowledge gaps regarding how psychedelics affect the brain. The Computational Neuroscience Laboratory at the Turner Institute for Brain and Mental Health, Monash University, uses multimodal neuroimaging to test hypotheses of the brain’s functional reorganisation under psychedelics, informed by the accounts of hierarchical predictive processing, using dynamic causal modelling (DCM). DCM is a generative modelling technique which allows to infer the directed connectivity among brain regions using functional brain imaging measurements. In this webinar, Associate Professor Adeel Razi and PhD candidate Devon Stoliker will showcase a series of previous and new findings of how changes to synaptic mechanisms, under the control of serotonin receptors, across the brain hierarchy influence sensory and associative brain connectivity. Understanding these neural mechanisms of subjective and therapeutic effects of psychedelics is critical for rational development of novel treatments and for the design and success of future clinical trials. Associate Professor Adeel Razi is a NHMRC Investigator Fellow and CIFAR Azrieli Global Scholar at the Turner Institute of Brain and Mental Health, Monash University. He performs cross-disciplinary research combining engineering, physics, and machine-learning. Devon Stoliker is a PhD candidate at the Turner Institute for Brain and Mental Health, Monash University. His interest in consciousness and psychiatry has led him to investigate the neural mechanisms of classic psychedelic effects in the brain.
In vitro bioelectronic models of the gut-brain axis
The human gut microbiome has emerged as a key player in the bidirectional communication of the gut-brain axis, affecting various aspects of homeostasis and pathophysiology. Until recently, the majority of studies that seek to explore the mechanisms underlying the microbiome-gut-brain axis cross-talk relied almost exclusively on animal models, and particularly gnotobiotic mice. Despite the great progress made with these models, various limitations, including ethical considerations and interspecies differences that limit the translatability of data to human systems, pushed researchers to seek for alternatives. Over the past decades, the field of in vitro modelling of tissues has experienced tremendous growth, thanks to advances in 3D cell biology, materials, science and bioengineering, pushing further the borders of our ability to more faithfully emulate the in vivo situation. Organ-on-chip technology and bioengineered tissues have emerged as highly promising alternatives to animal models for a wide range of applications. In this talk I’ll discuss our progress towards generating a complete platform of the human microbiota-gut-brain axis with integrated monitoring and sensing capabilities. Bringing together principles of materials science, tissue engineering, 3D cell biology and bioelectronics, we are building advanced models of the GI and the BBB /NVU, with real-time and label-free monitoring units adapted in the model architecture, towards a robust and more physiologically relevant human in vitro model, aiming to i) elucidate the role of microbiota in the gut-brain axis communication, ii) to study how diet and impaired microbiota profiles affect various (patho-)physiologies, and iii) to test personalised medicine approaches for disease modelling and drug testing.
Reverse-Engineering the Cortical Architecture for Controlled Semantic Cognition
Reverse engineering Hydra
Hydra is an extraordinary creature. Continuously replacing itself, it can live indefinitely, performing a stable repertoire of reasonably sophisticated behaviors. This remarkable stability under plasticity may be due to the uniform nature of its nervous system, which consists of two apparently noncommunicating nerve net layers. We use modeling to understand the role of active muscles and biomechanics interact with neural activity to shape Hydra behaviour. We will discuss our findings and thoughts on how this simple nervous system may self-organize to produce purposeful behavior.
Collective Construction in Natural and Artificial Swarms
Natural systems provide both puzzles to unravel and demonstrations of what's possible. The natural world is full of complex systems of dynamically interchangeable, individually unreliable components that produce effective and reliable outcomes at the group level. A complementary goal to understanding the operation of such systems is that of being able to engineer artifacts that work in a similar way. One notable type of collective behavior is collective construction, epitomized by mound-building termites, which build towering, intricate mounds through the joint activity of millions of independent and limited insects. The artificial counterpart would be swarms of robots designed to build human-relevant structures. I will discuss work on both aspects of the problem, including studies of cues that individual termite workers use to help direct their actions and coordinate colony activity, and development of robot systems that build user-specified structures despite limited information and unpredictable variability in the process. These examples illustrate principles used by the insects and show how they can be applied in systems we create.
Swarms for people
As tiny robots become individually more sophisticated, and larger robots easier to mass produce, a breakdown of conventional disciplinary silos is enabling swarm engineering to be adopted across scales and applications, from nanomedicine to treat cancer, to cm-sized robots for large-scale environmental monitoring or intralogistics. This convergence of capabilities is facilitating the transfer of lessons learned from one scale to the other. Cm-sized robots that work in the 1000s may operate in a way similar to reaction-diffusion systems at the nanoscale, while sophisticated microrobots may have individual capabilities that allow them to achieve swarm behaviour reminiscent of larger robots with memory, computation, and communication. Although the physics of these systems are fundamentally different, much of their emergent swarm behaviours can be abstracted to their ability to move and react to their local environment. This presents an opportunity to build a unified framework for the engineering of swarms across scales that makes use of machine learning to automatically discover suitable agent designs and behaviours, digital twins to seamlessly move between the digital and physical world, and user studies to explore how to make swarms safe and trustworthy. Such a framework would push the envelope of swarm capabilities, towards making swarms for people.
Storythinking: Why Your Brain is Creative in Ways that Computer AI Can't Ever Be
Computer AI thinks differently from us, which is why it's such a useful tool. Thanks to the ingenuity of human programmers, AI's different method of thinking has made humans redundant at certain human tasks, such as chess. Yet there are mechanical limits to how far AI can replicate the products of human thinking. In this talk, we'll trace one such limit by exploring how AI and humans create differently. Humans create by reverse-engineering tools or behaviors to accomplish new actions. AI creates by mix-and-matching pieces of preexisting structures and labeling which combos are associated with positive and negative results. This different procedure is why AI cannot (and will never) learn to innovate technology or tactics and why it also cannot (and will never) learn to generate narratives (including novels, business plans, and scientific hypotheses). It also serves as a case study in why there's no reason to believe in "general intelligence" and why computer AI would have to partner with other mechanical forms of AI (run on non-computer hardware that, as of yet, does not exist, and would require humans to invent) for AI to take over the globe.
Flow singularities in soft materials: from thermal motion to active molecular stresses
The motion of passive or active agents in soft materials generates long ranged deformation fields with signatures informed by hydrodynamics and the properties of the soft matter host. These signatures are even more complex when the soft matter host itself is an active material. Measurement of these fields reveals mechanics of the soft materials and hydrodynamics central to understanding self-organization. In this talk, I first introduce a new method based on correlated displacement velocimetry, and use the method to measure flow fields around particles trapped at the interface between immiscible fluids. These flow fields, decomposed into interfacial hydrodynamic multipoles, including force monopole and dipole flows, provide key insights essential to understanding the interface’s mechanical response. I then extend this method to various actomyosin systems to measure local strain fields around myosin molecular motors. I show how active stresses propagate in 2d liquid crystalline structures and in disordered networks that are formed by the actin filaments. In particular, the response functions of contractile and stable gels are characterized. Through similar analysis, I also measure the retrograde flow fields of stress fibers in single cells to understand subcellular mechanochemical systems.
Electrophysiologic Monitoring and Modulation of Enteric Nervous System
We will highlight recent technological and methodological advances in deploying miniaturized technologies that can monitor the spatial electrophysiologic patterns of the visceral nervous system. As an example, we will discuss recent developments of thin, stretchable, wireless biosensor patches that can be embedded within routinely used medical adhesives for recording electrophysiologic patterns of the GI tract. We will also showcase recent developments in array signal processing that enable non-invasive tracking, and source localization, of the slow wave patterns associated with the GI tract. We will illustrate how such systems can also be used in tandem with novel miniaturized pacing devices to can enable closed-loop neuromodulation of the enteric nervous system. We will conclude with a summary of the knowns and unknowns in how multi-organ physiology research, technology miniaturization, and data science may create unique opportunities for the intersection of electrical engineering and neuroscience.
3D Printing Cellular Communities: Mammalian Cells, Bacteria, And Beyond
While the motion and collective behavior of cells are well-studied on flat surfaces or in unconfined liquid media, in most natural settings, cells thrive in complex 3D environments. Bioprinting processes are capable of structuring cells in 3D and conventional bioprinting approaches address this challenge by embedding cells in bio-degradable polymer networks. However, heterogeneity in network structure and biodegradation often preclude quantitative studies of cell behavior in specified 3D architectures. Here, I will present a new approach to 3D bioprinting of cellular communities that utilizes jammed, granular polyelectrolyte microgels as a support medium. The self-healing nature of this medium allows the creation of highly precise cellular communities and tissue-like structures by direct injection of cells inside the 3D medium. Further, the transparent nature of this medium enables precise characterization of cellular behavior. I will describe two examples of my work using this platform to study the behavior of two different classes of cells in 3D. First, I will describe how we interrogate the growth, viability, and migration of mammalian cells—ranging from epithelial cells, cancer cells, and T cells—in the 3D pore space. Second, I will describe how we interrogate the migration of E. coli bacteria through the 3D pore space. Direct visualization enables us to reveal a new mode of motility exhibited by individual cells, in stark contrast to the paradigm of run-and-tumble motility, in which cells are intermittently and transiently trapped as they navigate the pore space; further, analysis of these dynamics enables prediction of single-cell transport over large length and time scales. Moreover, we show that concentrated populations of E. coli can collectively migrate through a porous medium—despite being strongly confined—by chemotactically “surfing” a self-generated nutrient gradient. Together, these studies highlight how the jammed microgel medium provides a powerful platform to design and interrogate complex cellular communities in 3D—with implications for tissue engineering, microtissue mechanics, studies of cellular interactions, and biophysical studies of active matter.
The 2021 Annual Bioengineering Lecture + Bioinspired Guidance, Navigation and Control Symposium
Join the Department of Bioengineering on the 26th May at 9:00am for The 2021 Annual Bioengineering Lecture + Bioinspired Guidance, Navigation and Control Symposium. This year’s lecture speaker will be distinguished bioengineer and neuroscientist Professor Mandyam V. Srinivasan AM FRS, from the University of Queensland. Professor Srinivasan studies visual systems, particularly those of bees and birds. His research has revealed how flying insects negotiate narrow gaps, regulate the height and speed of flight, estimate distance flown, and orchestrate smooth landings. Apart from enhancing fundamental knowledge, these findings are leading to novel, biologically inspired approaches to the design of guidance systems for unmanned aerial vehicles with applications in the areas of surveillance, security and planetary exploration. Following Professor Srinivasan’s lecture will be the Bioinspired GNC Mini Symposium with guest speakers from Google Deepmind, Imperial College London, the University of Würzburg and the University of Konstanz giving talks on their research into autonomous robot navigation, neural mechanisms of compass orientation in insects and computational approaches to motor control.
Dr Lindsay reads from "Models of the Mind : How Physics, Engineering and Mathematics Shaped Our Understanding of the Brain" 📖
Though the term has many definitions, computational neuroscience is mainly about applying mathematics to the study of the brain. The brain—a jumble of all different kinds of neurons interconnected in countless ways that somehow produce consciousness—has been described as “the most complex object in the known universe”. Physicists for centuries have turned to mathematics to properly explain some of the most seemingly simple processes in the universe—how objects fall, how water flows, how the planets move. Equations have proved crucial in these endeavors because they capture relationships and make precise predictions possible. How could we expect to understand the most complex object in the universe without turning to mathematics? — The answer is we can’t, and that is why I wrote this book. While I’ve been studying and working in the field for over a decade, most people I encounter have no idea what “computational neuroscience” is or that it even exists. Yet a desire to understand how the brain works is a common and very human interest. I wrote this book to let people in on the ways in which the brain will ultimately be understood: through mathematical and computational theories. — At the same time, I know that both mathematics and brain science are on their own intimidating topics to the average reader and may seem downright prohibitory when put together. That is why I’ve avoided (many) equations in the book and focused instead on the driving reasons why scientists have turned to mathematical modeling, what these models have taught us about the brain, and how some surprising interactions between biologists, physicists, mathematicians, and engineers over centuries have laid the groundwork for the future of neuroscience. — Each chapter of Models of the Mind covers a separate topic in neuroscience, starting from individual neurons themselves and building up to the different populations of neurons and brain regions that support memory, vision, movement and more. These chapters document the history of how mathematics has woven its way into biology and the exciting advances this collaboration has in store.
Light-degradable hydrogels as dynamic triggers for implantable devices
Triggerable materials capable of being degraded by selective stimuli stand to transform our capacity to precisely control biomedical device activity and performance while reducing the need for invasive interventions. This talk will cover the development of a modular and tunable light-triggerable hydrogel capable of interfacing with implantable devices. We have applied these materials to two applications in the gastrointestinal (GI) tract and demonstrated biocompatibility and on-demand triggering of the material in vitro, ex vivo, and in vivo. Light-triggerable hydrogels have the potential to be applied broadly throughout the GI tract and other anatomic areas. By demonstrating the first use of light-degradable hydrogels in vivo, we provide biomedical engineers and clinicians with a previously unavailable, safe, dynamically deliverable, and precise tool to design dynamically actuated implantable devices.
Learning in pain: probabilistic inference and (mal)adaptive control
Pain is a major clinical problem affecting 1 in 5 people in the world. There are unresolved questions that urgently require answers to treat pain effectively, a crucial one being how the feeling of pain arises from brain activity. Computational models of pain consider how the brain processes noxious information and allow mapping neural circuits and networks to cognition and behaviour. To date, they have generally have assumed two largely independent processes: perceptual and/or predictive inference, typically modelled as an approximate Bayesian process, and action control, typically modelled as a reinforcement learning process. However, inference and control are intertwined in complex ways, challenging the clarity of this distinction. I will discuss how they may comprise a parallel hierarchical architecture that combines pain inference, information-seeking, and adaptive value-based control. Finally, I will discuss whether and how these learning processes might contribute to chronic pain.
Choice engineering and the modeling of operant learning
Organisms modify their behavior in response to its consequences, a phenomenon referred to as operant learning. Contemporary modeling of this learning behavior is based on reinforcement learning algorithms. I will discuss some of the challenges that these models face, and proposed a new approach to model-selection that is based on testing their ability to engineer behavior. Finally, I will present the results of The Choice Engineering Competition – an academic competition that compared the efficacies of qualitative and quantitative models of operant learning in shaping behavior.
Mapping the brain’s remaining terra incognita
In this webinar, Dr Ye Tian and A/Prof Andrew Zalesky will present new research on mapping the functional architecture of the human subcortex. They used 3T and 7T functional MRI from more than 1000 people to map one of the most detailed functional atlases of the human subcortex to date. Comprising four hierarchical scales, the new atlas reveals the complex topographic organisation of the subcortex, which dynamically adapts to changing cognitive demands. The atlas enables whole-brain mapping of connectomes and has been used to optimise targeting of deep brain stimulation. This joint work with Professors Michael Breakspear and Daniel Margulies was recently published in Nature Neuroscience. In the second part of the webinar, Dr Ye Tian will present her current research on the biological ageing of different body systems, including the human brain, in health and degenerative conditions. Conducted in more than 30,000 individuals, this research reveals associations between the biological ageing of different body systems. She will show the impact of lifestyle factors on ageing and how advanced ageing can predict the risk of mortality. Associate Professor Andrew Zalesky is a Principal Researcher with a joint appointment between the Faculties of Engineering and Medicine at The University of Melbourne. He currently holds a NHMRC Senior Research Fellowship and serves as Associate Editor for Brain Topography, Neuroimage Clinical and Network Neuroscience. Dr Zalesky is recognised for the novel tools that he has developed to analyse brain networks and their application to the study of neuropsychiatric disorders. Dr Ye Tian is a postdoctoral researcher at the Department of Psychiatry, University of Melbourne. She received her PhD from the University of Melbourne in 2020, during which she established the Melbourne Subcortex Atlas. Dr Tian is interested in understanding brain organisation and using brain imaging techniques to unveil neuropathology underpinning neuropsychiatric disorders.
Reverse-engineering Drosophila motor control
Silicon retinas that make spike events
The story of event cameras starts from the very beginnings of neuromorphic engineering with Misha Mahowald and Carver Mead. The chip design of these “silicon retina” cameras is the most crucial aspect that might enable them to come to mass production and widespread use. Once we have a usable camera is just the beginning, because now we need to think of our use of the data as though we were some type of artificial “silicon cortex”. That step has just started but the last few years have brought some remarkable results from the computer vision community. This talk will have a lot of live demonstrations.
Electronics on the brain
One of the most important scientific and technological frontiers of our time is the interfacing of electronics with the human brain. This endeavour promises to help understand how the brain works and deliver new tools for diagnosis and treatment of pathologies including epilepsy and Parkinson’s disease. Current solutions, however, are limited by the materials that are brought in contact with the tissue and transduce signals across the biotic/abiotic interface. Recent advances in electronics have made available materials with a unique combination of attractive properties, including mechanical flexibility, mixed ionic/electronic conduction, enhanced biocompatibility, and capability for drug delivery. Professor Malliaras will present examples of novel devices for recording and stimulation of neurons and show that organic electronic materials offer tremendous opportunities to study the brain and treat its pathologies.
CURE-ND Neurotechnology Workshop - Innovative models of neurodegenerative diseases
One of the major roadblocks to medical progress in the field of neurodegeneration is the absence of animal models that fully recapitulate features of the human diseases. Unprecedented opportunities to tackle this challenge are emerging e.g. from genome engineering and stem cell technologies, and there are intense efforts to develop models with a high translational value. Simultaneously, single-cell, multi-omics and optogenetics technologies now allow longitudinal, molecular and functional analysis of human disease processes in these models at high resolution. During this workshop, 12 experts will present recent progress in the field and discuss: - What are the most advanced disease models available to date? - Which aspects of the human disease do these accurately models, which ones do they fail to replicate? - How should models be validated? Against which reference, which standards? - What are currently the best methods to analyse these models? - What is the field still missing in terms of modelling, and of technologies to analyse disease models? CURE-ND stands for 'Catalysing a United Response in Europe to Neurodegenerative Diseases'. It is a new alliance between the German Center for Neurodegenerative Diseases (DZNE), the Paris Brain Institute (ICM), Mission Lucidity (ML, a partnership between imec, KU Leuven, UZ Leuven and VIB in Belgium) and the UK Dementia Research Institute (UK DRI). Together, these partners embrace a joint effort to accelerate the pace of scientific discovery and nurture breakthroughs in the field of neurodegenerative diseases. This Neurotechnology Workshop is the first in a series of joint events aiming at exchanging expertise, promoting scientific collaboration and building a strong community of neurodegeneration researchers in Europe and beyond.
European University for Brain and Technology Virtual Opening
The European University for Brain and Technology, NeurotechEU, is opening its doors on the 16th of December. From health & healthcare to learning & education, Neuroscience has a key role in addressing some of the most pressing challenges that we face in Europe today. Whether the challenge is the translation of fundamental research to advance the state of the art in prevention, diagnosis or treatment of brain disorders or explaining the complex interactions between the brain, individuals and their environments to design novel practices in cities, schools, hospitals, or companies, brain research is already providing solutions for society at large. There has never been a branch of study that is as inter- and multi-disciplinary as Neuroscience. From the humanities, social sciences and law to natural sciences, engineering and mathematics all traditional disciplines in modern universities have an interest in brain and behaviour as a subject matter. Neuroscience has a great promise to become an applied science, to provide brain-centred or brain-inspired solutions that could benefit the society and kindle a new economy in Europe. The European University of Brain and Technology (NeurotechEU) aims to be the backbone of this new vision by bringing together eight leading universities, 250+ partner research institutions, companies, societal stakeholders, cities, and non-governmental organizations to shape education and training for all segments of society and in all regions of Europe. We will educate students across all levels (bachelor’s, master’s, doctoral as well as life-long learners) and train the next generation multidisciplinary scientists, scholars and graduates, provide them direct access to cutting-edge infrastructure for fundamental, translational and applied research to help Europe address this unmet challenge.
Sustainability in Space and on Earth: Research Initiatives of the Space Enabled Research Group
The presentation will present the work of the Space Enabled Research Group at the MIT Media Lab. The mission of the Space Enabled Research Group is to advance justice in Earth’s complex systems using designs enabled by space. Our message is that six types of space technology are supporting societal needs, as defined by the United Nations Sustainable Development Goals. These six technologies include satellite earth observation, satellite communication, satellite positioning, microgravity research, technology transfer, and the infrastructure related to space research and education. While much good work has been done, barriers remain that limit the application of space technology as a tool for sustainable development. The Space Enabled Research Group works to increase the opportunities to apply space technology in support of the Sustainable Development Goals and to support space sustainability. Our research applies six methods, including design thinking, art, social science, complex systems, satellite engineering and data science. We pursue our work by collaborating with development leaders who represent multilateral organizations, national and local governments, non-profits and entrepreneurial firms to identify opportunities to apply space technology in their work. We strive to enable a more just future in which every community can easily and affordably apply space technology. The work toward our mission covers three themes: 1) Research to apply existing space technology to support the United Nations Sustainable Development Goals; 2) Research to design space systems that are accessible and sustainable; and 3) Research to study the relationship between technology design and justice. The presentation will give examples of research projects within each of these themes.
Holographic control of neuronal circuits
Genetic targeting of neuronal cells with activity reporters (calcium or voltage indicators) has initiated the paradigmatic transition whereby photons have replaced electrons for reading large-scale brain activities at cellular resolution. This has alleviated the limitations of single cell or extracellular electrophysiological probing, which only give access to the activity of at best a few neurons simultaneously and to population activity of unresolved cellular origin, respectively. In parallel, optogenetics has demonstrated that targeting neuronal cells with photosensitive microbial opsins, enables the transduction of photons into electrical currents of opposite polarities thus writing, through activation or inhibition, neuronal signals in a non-invasive way. These progresses have in turn stimulated the development of sophisticated optical methods to increase spatial and temporal resolution, light penetration depth and imaging volume. Today, nonlinear microscopy, combined with spatio-temporal wave front shaping, endoscopic probes engineering or multi scan heads design, enable in vivo in depth, simultaneous recording of thousands of cells in mm 3 volumes at single-spike precision and single-cell resolution. Joint progress in opsin engineering, wave front shaping and laser development have provided the methodology, that we named circuits optogenetics, to control single or multiple target activity independently in space and time with single- neuron and single-spike precision, at large depths. Here, we will review the most significant breakthroughs of the past years, which enable reading and writing neuronal activity at the relevant spatiotemporal scale for brain circuits manipulation, with particular emphasis on the most recent advances in circuit optogenetics.
Human voluntary action: from thought to movement
The ability to decide and act autonomously is a distinctive feature of human cognition. From a motor neurophysiology viewpoint, these 'voluntary' actions can be distinguished by the lack of an obvious triggering sensory stimulus: the action is considered to be a product of thought, rather than a reflex result of a specific input. A reverse engineering approach shows that such actions are caused by neurons of the primary cortex, which in turn depend on medial frontal areas, and finally a combination of prefrontal cortical connections and subcortical drive from basal ganglia loops. One traditional marker of voluntary action is the EEG readiness potential (RP), recorded over the frontal cortex prior to voluntary actions. However, the interpretation of this signal remains controversial, and very few experimental studies have attempted to link the RP to the thought process that lead to voluntary action. In this talk, I will report new studies that show learning an internal model about the optimum delay at which to act influences the amplitude of the RP. More generally, a scientific understanding of voluntariness and autonomy will require new neurocognitive paradigms connecting thought and action.
Harnessing the CRISPR toolbox to engineer biology
“Biophysics of Structural Plasticity in Postsynaptic Spines”
The ability of the brain to encode and store information depends on the plastic nature of the individual synapses. The increase and decrease in synaptic strength, mediated through the structural plasticity of the spine, are important for learning, memory, and cognitive function. Dendritic spines are small structures that contain the synapse. They come in a variety of shapes (stubby, thin, or mushroom-shaped) and a wide range of sizes that protrude from the dendrite. These spines are the regions where the postsynaptic biochemical machinery responds to the neurotransmitters. Spines are dynamic structures, changing in size, shape, and number during development and aging. While spines and synapses have inspired neuromorphic engineering, the biophysical events underlying synaptic and structural plasticity of single spines remain poorly understood. Our current focus is on understanding the biophysical events underlying structural plasticity. I will discuss recent efforts from my group — first, a systems biology approach to construct a mathematical model of biochemical signaling and actin-mediated transient spine expansion in response to calcium influx caused by NMDA receptor activation and a series of spatial models to study the role of spine geometry and organelle location within the spine for calcium and cyclic AMP signaling. Second, I will discuss how mechanics of membrane-cytoskeleton interactions can give insight into spine shape region. And I will conclude with some new efforts in using reconstructions from electron microscopy to inform computational domains. I will conclude with how geometry and mechanics plays an important role in our understanding of fundamental biological phenomena and some general ideas on bio-inspired engineering.
Reverse engineering neural control of movement in Hydra
Hydra is a fascinating model organism for neuroscience. It is transparent; new genetic lines allow one to image activity in both neurons (Dupre and Yuste, 2017) and muscle cells (Szymanski and Yuste, 2019) ; it exhibits rich behavior, and it continually rebuilds itself. Hydra’s fairly simply physical structure as a two-layered fluid-filled hydrostat and the accessibility of information about neural and muscle activity opens the possibility of a complete model of neural control of behavior. This requires understanding the transformations that occur in the muscle cell layers and a biomechanical model of the body column. We show that we can use this modeling to reverse engineer how neural activity drives behavior.
Affordable Robots/Computer Systems to Identify, Assess, and Treat Impairment After Brain Injury
Non-traumatic brain injury due to stroke, cerebral palsy and HIV often result in serious long-term disability worldwide, affecting more than 150 million persons globally; with the majority of persons living in low and middle income countries. These diseases often result in varying levels of motor and cognitive impairment due to brain injury which then affects the person’s ability to complete activities of daily living and fully participate in society. Increasingly advanced technologies are being used to support identification, diagnosis, assessment, and therapy for patients with brain injury. Specifically, robot and mechatronic systems can provide patients, physicians and rehabilitation clinical providers with additional support to care for and improve the quality of life of children and adults with motor and cognitive impairment. This talk will provide a brief introduction to the area of rehabilitation robotics and, via case studies, illustrate how computer/technology-assisted rehabilitation systems can be developed and used to assess motor and cognitive impairment, detect early evidence of functional impairment, and augment therapy in high and low-resource settings.
Reverse engineering recurrent network models reveals mechanisms for location memory
Bernstein Conference 2024
Set-based Fitness Comparisons - Could Neuroscientists Benefit from Engineering Studies on Conceptual Design?
Bernstein Conference 2024
Engineering capsid-variant AAVs for selective gene delivery to dentate gyrus granule cells in the hippocampus
FENS Forum 2024
Engineering human induced pluripotent stem cells for spinal cord repair
FENS Forum 2024
Reverse engineering recurrent network models reveals mechanisms for location memory
FENS Forum 2024
Unravelling microglia-specific contributions to neuronal network activity: Engineering a human stem cell-derived tri-culture on microelectrode arrays
FENS Forum 2024