Programming
programming
Dr. Katie Kindt
A staff scientist position is available within the Section on Sensory Cell Development and Function at the National Institute on Deafness and Other Communication Disorders (NIDCD), at the National Institutes of Health (NIH). We are located in the multidisciplinary Neuroscience Research Center (Building 35A) in Bethesda, Maryland just outside of Washington D.C. Our group utilizes the zebrafish system to study hair cells, the specialized mechanoreceptors that are required to reliably transmit auditory and vestibular information to the brain. Specifically, we use this in vivo model to investigate the function and assembly of the hair cell system. Our work uses this relevant model by combining powerful genetics, functional and time-lapse imaging, electrophysiology, and behavioral analyses to comprehensively dissect the molecular and functional requirements underlying the assembly and function of hair cell systems in vivo. The main questions we are currently asking include: 1) how do collections of sensory cells, synapses, and neurons coordinate to encode sensory information; 2) how does sensory activity impact circuit assembly, function and health; and 3) what molecules are required to set up sensory function and synapse specificity?
Silvio P. Sabatini
The position is a full-time PhD studentship for a period of 3 years, starting on Nov 1st, 2023. The research project is titled 'Early vision function in silico networks of LIF neurons'. The project aims to develop an 'artificial observer' composed of an active event-based camera feeding a neuromorphic multi-layer network of leaky integrate and fire (LIF) neurons. The system should provide the inference engines for relating visual representations to performance on perceptual judgement tasks. Multiple and varying parameters captured under complex, real-life conditions should be comparatively assessed in silicon and human observers. The research will be conducted at the Bioengineering/PSPC labs of DIBRIS.
Dr. Ziad Nahas
Dr. Ziad Nahas (Interventional Psychiatry Lab) in the University of Minnesota Department of Psychiatry and Behavioral Sciences is seeking an outstanding candidate for a postdoctoral position to conduct and analyze the effects of neuromodulation on brain activity in mood disorders. Candidates should be passionate about advancing knowledge in the area of translational research of depressive disorders and other mental health conditions with a focus on invasive and non-invasive brain stimulation treatments. The position is available June 1, 2023, and funding is available for at least two years.
Dr. Ziad Nahas
Dr. Ziad Nahas (Interventional Psychiatry Lab) in the University of Minnesota Department of Psychiatry and Behavioral Sciences is seeking an outstanding candidate for a postdoctoral position to conduct and analyze the effects of neuromodulation on brain activity in mood disorders. Candidates should be passionate about advancing knowledge in the area of translational research of depressive disorders and other mental health conditions with a focus on invasive and non-invasive brain stimulation treatments. The position is available June 1, 2023, and funding is available for at least two years.
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.
Prof. Jim Torresen
The goal of the position is to create prediction methods for proactive planning of future robot actions and to design robot acting mechanisms for adaptive response ranging from quick and intuitive to slower well-reasoned. We combine sensing across multiple modalities with learned knowledge to predict outcomes and choose the best actions. The goal is to transfer these skills to human-robot interaction in home scenarios, including the support of everyday tasks and physical rehabilitation. Thus, it is relevant to work with implementation and research within robot perception and control for the robot tasks. User studies through human-robot interaction experiments are to be performed. A PhD fellow and a researcher are already hired for the project and will complement in performing the above outlined research.
Angelo Cangelosi
Two Research Fellows in Robotics / Human Robot Interaction are required for a period of 12 months each to work on the UKRI/EPSRC project “Trustworthy Autonomous Systems Node on Trust”. This is a collaborative project of the University of Manchester’s Cognitive Robotics Lab with Heriot-Watt University Edinburgh and Imperial College London. The candidates will carry out research on robot cognitive architectures for theory of mind and trust, using a combination of machine learning and robotics methodologies, and/or human-robot interaction for trust. The research fellows will be working collaboratively as part of the Cognitive Robotics Lab at the Department of Computer Science at the University of Manchester under the supervision of Professor Angelo Cangelosi. Close collaboration with the other project partners will also be required.
Prof. Jim Torresen
The goal of the position is to create prediction methods for proactive planning of future robot actions and to design robot acting mechanisms for adaptive response ranging from quick and intuitive to slower well-reasoned. We combine sensing across multiple modalities with learned knowledge to predict outcomes and choose the best actions. The goal is to transfer these skills to human-robot interaction in home scenarios, including the support of everyday tasks and physical rehabilitation. Thus, it is relevant to work with implementation and research within robot perception and control for the robot tasks. User studies through human-robot interaction experiments are to be performed. A PhD fellow and a researcher are already hired for the project and will complement in performing the above outlined research.
Vinita Samarasinghe
The research group uses diverse computational modeling approaches, including biological neural networks, cognitive modeling, and machine learning/artificial intelligence, to study learning and memory. The group is actively seeking a talented graduate student to join the team, who will expand the computational modeling framework Cobel-RL (https://doi.org/10.3389/fninf.2023.1134405) and use it to study how episodic memory might be used to learn to navigate.
Jens Peter Lindemann
The PhD project is part of the DFG-funded project 'Cue integration by bumblebees during navigation in uncertain environments with multiple goal options: Behavioural analysis in virtual reality and computational modelling' in an international research team. Bumblebees can be trained to prefer certain places or objects in a virtual environment through appropriate rewarding. In a close integration of two PhD projects, one with a focus on VR behaviour experiments and the other focussing on computational modelling and simulation, we are investigating the mechanisms underlying these learning and orientation performances. The applicant is expected to design and implement models for behavioral control of bumblebees, test them in computer simulations, contribute to VR experiments with bumblebees, and collaborate intensively with other project participants.
Mathieu Desroches
The aim of the project is to develop a multiscale model of Dravet syndrome, from ionic channels of interacting neurons to large neural populations. We will use various modeling frameworks, adapted to the scale, from piecewise-deterministic Markov processes to mean-field formalism. The postdoc will perform a mathematical analysis of the models, extensive numerical simulations as well as data analysis using neural recordings from our experimental partners.
Mathieu Desroches
The aim of the project is to develop a multiscale model of Dravet syndrome, from ionic channels of interacting neurons to large neural populations. We will use various modeling frameworks, adapted to the scale, from piecewise-deterministic Markov processes to mean-field formalism. The postdoc will perform a mathematical analysis of the models, extensive numerical simulations as well as data analysis using neural recordings from our experimental partners.
Antonio C. Roque
The Research, Innovation and Dissemination Center for Neuromathematics (NeuroMat), hosted by the University of São Paulo (USP), Brazil, and funded by the São Paulo Research Foundation (FAPESP), is offering post-doctoral fellowships for recent PhDs with outstanding research potential. The fellowship will involve collaborations with research teams and laboratories associated with NeuroMat. The research to be developed by the post-doc fellow shall be strictly related to ongoing research lines developed by the NeuroMat team that can be consulted at our website. The project may be developed at the laboratories of USP, campuses of São Paulo or Ribeirão Preto, or at UNICAMP, Campinas, in person.
Arun Antony MD
The Neuroscience Institute at Jersey Shore University Medical Center, New Jersey, USA is seeking a postdoctoral fellow to work on basic, clinical, and translational projects in the fields of seizures, epilepsy, human intracranial EEG, signal processing, cognition and consciousness. The fellow will join a multidisciplinary team of five epileptologists, neurosurgeons, epilepsy nurses, nurse practitioners, neuropsychologists and researchers providing holistic care to patients with epilepsy. The postdoctoral fellows will have access to the large clinical, imaging, and EEG databases, and outcome measures of cutting edge treatment modalities within the system for research purposes. The successful candidate will be well versed in data collection, processing, programming and will lead an independent research project working closely with collaborators and publish high-quality research.
Silvia Lopez-Guzman
The Unit on Computational Decision Neuroscience (CDN) at the National Institute of Mental Health is seeking a full-time Data Scientist/Data Analyst. The lab is focused on understanding the neural and computational bases of adaptive and maladaptive decision-making and their relationship to mental health. Current studies investigate how internal states lead to biases in decision-making and how this is exacerbated in mental health disorders. Our approach involves a combination of computational model-based tasks, questionnaires, biosensor data, fMRI, and intracranial recordings. The main models of interest come from neuroeconomics, reinforcement learning, Bayesian inference, signal detection, and information theory. The main tasks for this position include computational modeling of behavioral data from decision-making and other cognitive tasks, statistical analysis of task-based, clinical, physiological and neuroimaging data, as well as data visualization for scientific presentations, public communication, and academic manuscripts. The candidate is expected to demonstrate experience with best practices for the development of well-documented, reproducible programming pipelines for data analysis, that facilitate sharing and collaboration, and live up to our open-science philosophy, as well as to our data management and sharing commitments at NIH.
Joseph Lizier
The successful candidates will join a dynamic interdisciplinary collaboration between A/Prof Mac Shine (Brain and Mind Centre), A/Prof Joseph Lizier (School of Computer Science) and Dr Ben Fulcher (School of Physics), within the University's Centre for Complex Systems, focused on advancing our understanding of brain function and cognition using cutting-edge computational and neuroimaging techniques at the intersection of network neuroscience, dynamical systems and information theory. The positions are funded by a grant from the Australian Research Council 'Evaluating the Network Neuroscience of Human Cognition to Improve AI'.
Dr. Joseph M Barnby
The post holder will be a member of the SoCCR Lab with responsibility for the provision of computational support for a Wellcome Trust-funded project, “Hypatia: Health Modelling Made Simple”. The role involves software engineering/programming the Hypatia site, retaining a core knowledge of computational modelling as it relates to health care, neuroscience, and psychology, and liaising with project consultants to develop the UX/UI, backend support, and aesthetics of the platform. The position offers an opportunity to develop and use state-of-the-art methods to build a free browser-based solution to allow simulation and fitting of common computational models, with the final milestone of 12 months to have a working beta version. There is significant opportunity for skill development, including gaining experience of industry liaison, translational neuroscience, and computational modelling expertise.
Ioan Marius BILASCO
The FOX team from the CRIStAL laboratory (UMR CNRS), Lille France and the PR team from the MIS Laboratory, Amiens France are looking to recruit a joint PhD student for a project titled 'EventSpike - Asynchronous computer vision from event cameras'. The project aims to develop new models of spiking neural networks (SNN) capable of directly processing visual information in the form of spike trains for applications in autonomous driving. The thesis will focus on weakly supervised learning methods based on spiking learning mechanisms to exploit the flow of impulses generated by an event camera.
Dr. Romy Lorenz
The Cognitive Neuroscience & Neurotechnology group at the Max Planck Institute for Biological Cybernetics, led by Dr. Romy Lorenz, is seeking two ambitious PhD students to work on the field of ultrahigh resolution fMRI for investigating the human cortex at the scale of layers and columns. The lab focuses on understanding the frontoparietal brain network mechanisms underpinning high-level cognition and adaptive behaviour through an interdisciplinary research programme. Methodologies include subject-specific brain-computer interface technology, fMRI at 3T and ultrahigh magnetic field strengths (7T and 9.4T), EEG, non-invasive brain stimulation, and machine learning.
N/A
The PostDoctoral researcher will conduct research activities in modelling and simulation of reward-modulated prosocial behavior and decision-making. The position is part of a larger effort to uncover the computational and mechanistic bases of prosociality and empathy at the behavioral and circuit levels. The role involves working at the interface between experimental data (animal behavior and electrophysiology) and theoretical modelling, with an emphasis on Multi-Agent Reinforcement Learning and neural population dynamics.
Rania Rayyes
The research in the AI & Robotics group focuses on developing novel AI systems for real robot applications for manipulation tasks, e.g., grasping, pin-picking. Areas of focus include Autonomous Robot Learning (active learning, lifelong learning, intrinsic motivation) and Human-Robot Learning (imitation learning, interactive learning).
Dr. Udo Ernst
In this project we want to study organization and optimization of flexible information processing in neural networks, with specific focus on the visual system. You will use network modelling, numerical simulation, and mathematical analysis to investigate fundamental aspects of flexible computation such as task-dependent coordination of multiple brain areas for efficient information processing, as well as the emergence of flexible circuits originating from learning schemes which simultaneously optimize for function and flexibility. These studies will be complemented by biophysically realistic modelling and data analysis in collaboration with experimental work done in the lab of Prof. Dr. Andreas Kreiter, also at the University of Bremen. Here we will investigate selective attention as a central aspect of flexibility in the visual system, involving task-dependent coordination of multiple visual areas.
N/A
The hired postdoctoral researcher will mainly work on WP2, i.e., on the development of new formalisms and methods to apply to higher order interaction patterns identified in the data analyzed in WP1. The project aims to build a theoretical and data analysis framework to demonstrate the role of higher-order interactions (HOIs) in human brain networks supporting causal learning. The Hinteract project includes three scientific work packages (WPs): WP1 focuses on developing an informational theoretical approach to infer task-related HOIs from neural time series and characterizing HOIs supporting causal learning using MEG and SEEG data. WP2 involves developing a network science formalism to analyze the structure and dynamics of functional HOIs patterns and characterizing the hierarchical organization of learning-related HOIs. WP3 is about compiling and sharing neuroinformatics tools developed in the project and making them interoperable with the EBRAINS infrastructure.
Pranav Nerurkar
Join our comprehensive online internship program focusing on Two Sample Hypothesis Testing, designed for students eager to delve into the world of statistical analysis and coding. This internship offers a unique blend of theoretical learning and practical application, providing participants with a robust understanding of hypothesis testing using real-world data. Key features include Interactive Video Lectures, Hands-On Coding Assignments, Practical Applications, Mentorship and Support, and Certification.
Ekta Vats
A fully funded PhD position in Machine Learning and Computer Vision is available at Uppsala University, Sweden. The position is a part of the Beijer Laboratory for Artificial Intelligence Research, funded by Kjell and Märta Beijer Foundation. In this project you will join us in conducting fundamental machine learning research and developing principled foundations of vision-language models, with opportunities to validate the methods on challenging real-world problems involving computer vision.
Genetic and epigenetic underpinnings of neurodegenerative disorders
Pluripotent cells, including embryonic stem (ES) and induced pluripotent stem (iPS) cells, are used to investigate the genetic and epigenetic underpinnings of human diseases such as Parkinson’s, Alzheimer’s, autism, and cancer. Mechanisms of somatic cell reprogramming to an embryonic pluripotent state are explored, utilizing patient-specific pluripotent cells to model and analyze neurodegenerative diseases.
Astrocyte reprogramming / activation and brain homeostasis
Astrocytes are multifunctional glial cells, implicated in neurogenesis and synaptogenesis, supporting and fine-tuning neuronal activity and maintaining brain homeostasis by controlling blood-brain barrier permeability. During the last years a number of studies have shown that astrocytes can also be converted into neurons if they force-express neurogenic transcription factors or miRNAs. Direct astrocytic reprogramming to induced-neurons (iNs) is a powerful approach for manipulating cell fate, as it takes advantage of the intrinsic neural stem cell (NSC) potential of brain resident reactive astrocytes. To this end, astrocytic cell fate conversion to iNs has been well-established in vitro and in vivo using combinations of transcription factors (TFs) or chemical cocktails. Challenging the expression of lineage-specific TFs is accompanied by changes in the expression of miRNAs, that post-transcriptionally modulate high numbers of neurogenesis-promoting factors and have therefore been introduced, supplementary or alternatively to TFs, to instruct direct neuronal reprogramming. The neurogenic miRNA miR-124 has been employed in direct reprogramming protocols supplementary to neurogenic TFs and other miRNAs to enhance direct neurogenic conversion by suppressing multiple non-neuronal targets. In our group we aimed to investigate whether miR-124 is sufficient to drive direct reprogramming of astrocytes to induced-neurons (iNs) on its own both in vitro and in vivo and elucidate its independent mechanism of reprogramming action. Our in vitro data indicate that miR-124 is a potent driver of the reprogramming switch of astrocytes towards an immature neuronal fate. Elucidation of the molecular pathways being triggered by miR-124 by RNA-seq analysis revealed that miR-124 is sufficient to instruct reprogramming of cortical astrocytes to immature induced-neurons (iNs) in vitro by down-regulating genes with important regulatory roles in astrocytic function. Among these, the RNA binding protein Zfp36l1, implicated in ARE-mediated mRNA decay, was found to be a direct target of miR-124, that be its turn targets neuronal-specific proteins participating in cortical development, which get de-repressed in miR-124-iNs. Furthermore, miR-124 is potent to guide direct neuronal reprogramming of reactive astrocytes to iNs of cortical identity following cortical trauma, a novel finding confirming its robust reprogramming action within the cortical microenvironment under neuroinflammatory conditions. In parallel to their reprogramming properties, astrocytes also participate in the maintenance of blood-brain barrier integrity, which ensures the physiological functioning of the central nervous system and gets affected contributing to the pathology of several neurodegenerative diseases. To study in real time the dynamic physical interactions of astrocytes with brain vasculature under homeostatic and pathological conditions, we performed 2-photon brain intravital imaging in a mouse model of systemic neuroinflammation, known to trigger astrogliosis and microgliosis and to evoke changes in astrocytic contact with brain vasculature. Our in vivo findings indicate that following neuroinflammation the endfeet of activated perivascular astrocytes lose their close proximity and physiological cross-talk with vasculature, however this event is at compensated by the cross-talk of astrocytes with activated microglia, safeguarding blood vessel coverage and maintenance of blood-brain integrity.
Epigenomic (re)programming of the brain and behavior by ovarian hormones
Rhythmic changes in sex hormone levels across the ovarian cycle exert powerful effects on the brain and behavior, and confer female-specific risks for neuropsychiatric conditions. In this talk, Dr. Kundakovic will discuss the role of fluctuating ovarian hormones as a critical biological factor contributing to the increased depression and anxiety risk in women. Cycling ovarian hormones drive brain and behavioral plasticity in both humans and rodents, and the talk will focus on animal studies in Dr. Kundakovic’s lab that are revealing the molecular and receptor mechanisms that underlie this female-specific brain dynamic. She will highlight the lab’s discovery of sex hormone-driven epigenetic mechanisms, namely chromatin accessibility and 3D genome changes, that dynamically regulate neuronal gene expression and brain plasticity but may also prime the (epi)genome for psychopathology. She will then describe functional studies, including hormone replacement experiments and the overexpression of an estrous cycle stage-dependent transcription factor, which provide the causal link(s) between hormone-driven chromatin dynamics and sex-specific anxiety behavior. Dr. Kundakovic will also highlight an unconventional role that chromatin dynamics may have in regulating neuronal function across the ovarian cycle, including in sex hormone-driven X chromosome plasticity and hormonally-induced epigenetic priming. In summary, these studies provide a molecular framework to understand ovarian hormone-driven brain plasticity and increased female risk for anxiety and depression, opening new avenues for sex- and gender-informed treatments for brain disorders.
Experimental Neuroscience Bootcamp
This course provides a fundamental foundation in the modern techniques of experimental neuroscience. It introduces the essentials of sensors, motor control, microcontrollers, programming, data analysis, and machine learning by guiding students through the “hands on” construction of an increasingly capable robot. In parallel, related concepts in neuroscience are introduced as nature’s solution to the challenges students encounter while designing and building their own intelligent system.
Circuit solutions for programming actions
The hippocampus is one of the few regions in the adult mammalian brain which is endowed with life-long neurogenesis. Despite intense investigation, it remains unclear how neurons newly-generated may retain unique functions that contribute to modulate hippocampal information processing and cognition. In this talk, I will present some recent findings revealing how enhanced forms of plasticity in adult-born neurons underlie the way they become incorporated into pre-existing networks in response to experience.
Pynapple: a light-weight python package for neural data analysis - webinar + tutorial
In systems neuroscience, datasets are multimodal and include data-streams of various origins: multichannel electrophysiology, 1- or 2-p calcium imaging, behavior, etc. Often, the exact nature of data streams are unique to each lab, if not each project. Analyzing these datasets in an efficient and open way is crucial for collaboration and reproducibility. In this combined webinar and tutorial, Adrien Peyrache and Guillaume Viejo will present Pynapple, a Python-based data analysis pipeline for systems neuroscience. Designed for flexibility and versatility, Pynapple allows users to perform cross-modal neural data analysis via a common programming approach which facilitates easy sharing of both analysis code and data.
Pynapple: a light-weight python package for neural data analysis - webinar + tutorial
In systems neuroscience, datasets are multimodal and include data-streams of various origins: multichannel electrophysiology, 1- or 2-p calcium imaging, behavior, etc. Often, the exact nature of data streams are unique to each lab, if not each project. Analyzing these datasets in an efficient and open way is crucial for collaboration and reproducibility. In this combined webinar and tutorial, Adrien Peyrache and Guillaume Viejo will present Pynapple, a Python-based data analysis pipeline for systems neuroscience. Designed for flexibility and versatility, Pynapple allows users to perform cross-modal neural data analysis via a common programming approach which facilitates easy sharing of both analysis code and data.
Cell-type specific genomics and transcriptomics of HIV in the brain
Exploration of genome organization and function in the HIV infected brain is critical to aid in the understanding and development of treatments for HIV-associated neurocognitive disorder (HAND). Here, we applied a multiomic approach, including single nuclei transcriptomics, cell-type specific Hi-C 3D genome mapping, and viral integration site sequencing (IS-seq) to frontal lobe tissue from HIV-infected individuals with encephalitis (HIVE) and without encephalitis (HIV+). We observed reorganization of open/repressive (A/B) compartment structures in HIVE microglia encompassing 6.4% of the genome with enrichment for regions containing interferon (IFN) pathway genes. 3D genome remodeling was associated with transcriptomic reprogramming, including down-regulation of cell adhesion and synapse-related functions and robust activation of IFN signaling and cell migratory pathways, and was recapitulated by IFN-g stimulation of cultured microglial cells. Microglia from HIV+ brains showed, to a lesser extent, similar transcriptional alterations. IS-seq recovered 1,221 integration sites in the brain that were enriched for chromosomal domains newly mobilized into a permissive chromatin environment in HIVE microglia. Viral transcription, which was detected in 0.003% of all nuclei in HIVE brain, occurred in a subset of highly activated microglia that drove differential expression in HIVE. Thus, we observed a dynamic interrelationship of interferon-associated 3D genome and transcriptome remodeling with HIV integration and transcription in the brain.
Reprogramming the nociceptive circuit topology reshapes sexual behavior in C. elegans
In sexually reproducing species, males and females respond to environmental sensory cues and transform the input into sexually dimorphic traits. Yet, how sexually dimorphic behavior is encoded in the nervous system is poorly understood. We characterize the sexually dimorphic nociceptive behavior in C. elegans – hermaphrodites present a lower pain threshold than males in response to aversive stimuli, and study the underlying neuronal circuits, which are composed of the same neurons that are wired differently. By imaging receptor expression, calcium responses and glutamate secretion, we show that sensory transduction is similar in the two sexes, and therefore explore how downstream network topology shapes dimorphic behavior. We generated a computational model that replicates the observed dimorphic behavior, and used this model to predict simple network rewirings that would switch the behavior between the sexes. We then showed experimentally, using genetic manipulations, artificial gap junctions, automated tracking and optogenetics, that these subtle changes to male connectivity result in hermaphrodite-like aversive behavior in-vivo, while hermaphrodite behavior was more robust to perturbations. Strikingly, when presented with aversive cues, rewired males were compromised in finding mating partners, suggesting that the network topology that enables efficient avoidance of noxious cues would have a reproductive "cost". To summarize, we present a deconstruction of a sex-shared neural circuit that affects sexual behavior, and how to reprogram it. More broadly, our results are an example of how common neuronal circuits changed their function during evolution by subtle topological rewirings to account for different environmental and sexual needs.
PiSpy: An Affordable, Accessible, and Flexible Imaging Platform for the Automated Observation of Organismal Biology and Behavior
A great deal of understanding can be gleaned from direct observation of organismal growth, development, and behavior. However, direct observation can be time consuming and influence the organism through unintentional stimuli. Additionally, video capturing equipment can often be prohibitively expensive, difficult to modify to one’s specific needs, and may come with unnecessary features. Here, we describe the PiSpy, a low-cost, automated video acquisition platform that uses a Raspberry Pi computer and camera to record video or images at specified time intervals or when externally triggered. All settings and controls, such as programmable light cycling, are accessible to users with no programming experience through an easy-to-use graphical user interface. Importantly, the entire PiSpy system can be assembled for less than $100 using laser-cut and 3D-printed components. We demonstrate the broad applications and flexibility of the PiSpy across a range of model and non-model organisms. Designs, instructions, and code can be accessed through an online repository, where a global community of PiSpy users can also contribute their own unique customizations and help grow the community of open-source research solutions.
Mesmerize: A blueprint for shareable and reproducible analysis of calcium imaging data
Mesmerize is a platform for the annotation and analysis of neuronal calcium imaging data. Mesmerize encompasses the entire process of calcium imaging analysis from raw data to interactive visualizations. Mesmerize allows you to create FAIR-functionally linked datasets that are easy to share. The analysis tools are applicable for a broad range of biological experiments and come with GUI interfaces that can be used without requiring a programming background.
Why would we need Cognitive Science to develop better Collaborative Robots and AI Systems?
While classical industrial robots are mostly designed for repetitive tasks, assistive robots will be challenged by a variety of different tasks in close contact with humans. Hereby, learning through the direct interaction with humans provides a potentially powerful tool for an assistive robot to acquire new skills and to incorporate prior human knowledge during the exploration of novel tasks. Moreover, an intuitive interactive teaching process may allow non-programming experts to contribute to robotic skill learning and may help to increase acceptance of robotic systems in shared workspaces and everyday life. In this talk, I will discuss recent research I did on interactive robot skill learning and the remaining challenges on the route to human-centered teaching of assistive robots. In particular, I will also discuss potential connections and overlap with cognitive science. The presented work covers learning a library of probabilistic movement primitives from human demonstrations, intention aware adaptation of learned skills in shared workspaces, and multi-channel interactive reinforcement learning for sequential tasks.
GuPPy, a Python toolbox for the analysis of fiber photometry data
Fiber photometry (FP) is an adaptable method for recording in vivo neural activity in freely behaving animals. It has become a popular tool in neuroscience due to its ease of use, low cost, the ability to combine FP with freely moving behavior, among other advantages. However, analysis of FP data can be a challenge for new users, especially those with a limited programming background. Here, we present Guided Photometry Analysis in Python (GuPPy), a free and open-source FP analysis tool. GuPPy is provided as a Jupyter notebook, a well-commented interactive development environment (IDE) designed to operate across platforms. GuPPy presents the user with a set of graphic user interfaces (GUIs) to load data and provide input parameters. Graphs produced by GuPPy can be exported into various image formats for integration into scientific figures. As an open-source tool, GuPPy can be modified by users with knowledge of Python to fit their specific needs.
Fundamentals of PyTorch: Building a Model Step-by-Step
In this workshop you'll learn the fundamentals of PyTorch using an incremental, from-first-principles approach. We'll start with tensors, autograd, and the dynamic computation graph, and then move on to developing and training a simple model using PyTorch's model classes, datasets, data loaders, optimizers, and more. You should be comfortable using Python, Jupyter notebooks, Google Colab, Numpy and, preferably, object oriented programming.
Creating and controlling visual environments using BonVision
Real-time rendering of closed-loop visual environments is important for next-generation understanding of brain function and behaviour, but is often prohibitively difficult for non-experts to implement and is limited to few laboratories worldwide. We developed BonVision as an easy-to-use open-source software for the display of virtual or augmented reality, as well as standard visual stimuli. BonVision has been tested on humans and mice, and is capable of supporting new experimental designs in other animal models of vision. As the architecture is based on the open-source Bonsai graphical programming language, BonVision benefits from native integration with experimental hardware. BonVision therefore enables easy implementation of closed-loop experiments, including real-time interaction with deep neural networks, and communication with behavioural and physiological measurement and manipulation devices.
Using Human Stem Cells to Uncover Genetic Epilepsy Mechanisms
Reprogramming somatic cells to a pluripotent state via the induced pluripotent stem cell (iPSC) method offers an increasingly utilized approach for neurological disease modeling with patient-derived cells. Several groups, including ours, have applied the iPSC approach to model severe genetic developmental and epileptic encephalopathies (DEEs) with patient-derived cells. Although most studies to date involve 2-D cultures of patient-derived neurons, brain organoids are increasingly being employed to explore genetic DEE mechanisms. We are applying this approach to understand PMSE (Polyhydramnios, Megalencephaly and Symptomatic Epilepsy) syndrome, Rett Syndrome (in collaboration with Ben Novitch at UCLA) and Protocadherin-19 Clustering Epilepsy (PCE). I will describe our findings of robust structural phenotypes in PMSE and PCE patient-derived brain organoid models, as well as functional abnormalities identified in fusion organoid models of Rett syndrome. In addition to showing epilepsy-relevant phenotypes, both 2D and brain organoid cultures offer platforms to identify novel therapies. We will also discuss challenges and recent advances in the brain organoid field, including a new single rosette brain organoid model that we have developed. The field is advancing rapidly and our findings suggest that brain organoid approaches offers great promise for modeling genetic neurodevelopmental epilepsies and identifying precision therapies.
Analysis and manipulation of facilitators and barriers of cell identity reprogramming
Faces influence saccade programming
Several studies have showed that face stimuli elicit extremely fast and involuntary saccadic responses toward them, relative to other categories of visual stimuli. In the talk, I will mainly focus on a quite recent research done in our team that investigated to what extent face stimuli influence the programming and execution of saccades. In this research, two experiments were performed using a saccadic choice task: two images (one with a face, one with a vehicle) were simultaneously displayed in the left and right visual fields of participants who had to execute a saccade toward the image (Experiment 1) or toward a cross added in the center of the image (Experiment 2) containing a target stimulus (a face or a vehicle). As expected participants were faster to execute a saccade toward a face than toward a vehicle and did less errors. We also observed shorter saccades toward vehicle than face targets, even if participants were explicitly asked to perform their saccades toward a specific location (Experiment 2). Further analyses, that I will detailed in the talk, showed that error saccades might be interrupted in mid-fight to initiate a concurrently programmed corrective saccade.
Mobilefuge: A low-cost, portable, open source, 3D-printed centrifuge that can be used for purification of saliva samples for SARS-CoV2 detection
We made a low-cost centrifuge that can be useful for carrying out low-cost LAMP based detection of SARS-Cov2 virus in saliva. The 3D printed centrifuge (Mobilefuge) is portable, robust, stable, safe, easy to build and operate. The Mobilefuge doesn’t require soldering or programming skills and can be built without any specialised equipment, yet practical enough for high throughput use. More importantly, Mobilefuge can be powered from widely available USB ports, including mobile phones and associated power supplies. This allows the Mobilefuge to be used even in off-grid and resource limited settings. Website: https://www.cappa.ie/chinna-devarapu/
Novel mechanisms of neurogenesis and neural repair
In order to re-install neurogenesis after loss of neurons upon injury or neurodegeneration, we need to understand the basic principles of neurogenesis. I will first discuss about our discovery of a novel centrosome protein (Camargo et al., 2019) and discuss unpublished work about the great diversity of interphase centrosome proteomes and their relevance for neurodevelopmental disorders. I would then present work on a master regulator of neural stem cell amplification and brain folding (Stahl et al., 2013; Esgleas et al., 2020) to proceed presenting data on utilizing some of these factors for turning astrocytes into neurons. I will present data on the critical role of mitochondria in this conversion process (Gascon et al., 2016, Russo et al., 2020) and how it regulates the speed of conversion also showing unpublished data. If time permits I may touch on recent progress in in vivo reprogramming (Mattugini et al., 2019). Taken together, these data highlight the surprising specificity and importance of organelle diversity from centrosome, nucleolus and mitochondria as key regulators in development and reprogramming.
Role of Oxytocin in regulating microglia functions to prevent brain damage of the developing brain
Every year, 30 million infants worldwide are delivered after intra-uterine growth restriction (IUGR) and 15 million are born preterm. These two conditions are the leading causes of ante/perinatal stress and brain injury responsible for neurocognitive and behavioral disorders in more than 9 million children each year. Both prematurity and IUGR are associated with perinatal systemic inflammation, a key factor associated with neuroinflammation and identified to be the best predictor of subsequent neurological impairments. Most of pharmacological candidates have failed to demonstrate any beneficial effect to prevent perinatal brain damage. In contrast, environmental enrichment based on developmental care, skin-to-skin contact and vocal/music intervention appears to confer positive effects on brain structure and function. However, mechanisms underlying these effects remain unknown. There is strong evidence that an adverse environment during pregnancy and the perinatal period can influence hormonal responses of the newborn with long-lasting neurobehavioral consequences in infancy and adulthood. Excessive cortisol release in response to perinatal stress induces pro-inflammatory and brain-programming effects. These deleterious effects are known to be balanced by Oxytocin (OT), a neuropeptide playing a key role during the perinatal period and parturition, in social behavior and regulating the central inflammatory response to injury in the adult brain. Using a rodent model of IUGR associated with perinatal brain damage, we recently reported that Carbetocin, a brain permeable long-lasting OT receptor (OTR) agonist, was associated with a significant reduction of activated microglia, the primary immune cells of the brain. Moreover this reduced microglia reactivity was associated to a long-term neuroprotection. These findings make OT a promising candidate for neonatal neuroprotection through neuroinflammation regulation. However, the causality between the endogenous OT and central inflammation response to injury has not been established and will be further studied by the lab.
Synthesizing Machine Intelligence in Neuromorphic Computers with Differentiable Programming
The potential of machine learning and deep learning to advance artificial intelligence is driving a quest to build dedicated computers, such as neuromorphic hardware that emulate the biological processes of the brain. While the hardware technologies already exist, their application to real-world tasks is hindered by the lack of suitable programming methods. Advances at the interface of neural computation and machine learning showed that key aspects of deep learning models and tools can be transferred to biologically plausible neural circuits. Building on these advances, I will show that differentiable programming can address many challenges of programming spiking neural networks for solving real-world tasks, and help devise novel continual and local learning algorithms. In turn, these new algorithms pave the road towards systematically synthesizing machine intelligence in neuromorphic hardware without detailed knowledge of the hardware circuits.
Epigenetic Reprogramming of Taste by Diet
Diets rich in sugar, salt, and fat alter taste perception and food intake, leading to obesity and metabolic disorders, but the molecular mechanisms through which this occurs are unknown. Here we show that in response to a high sugar diet, the epigenetic regulator Polycomb Repressive Complex 2.1 (PRC2.1) persistently reprograms the sensory neurons of D. melanogaster flies to reduce sweet sensation and promote obesity. In animals fed high sugar, the binding of PRC2.1 to the chromatin of the sweet gustatory neurons is redistributed to repress a developmental transcriptional network that modulates the responsiveness of these cells to sweet stimuli, reducing sweet sensation. Importantly, half of these transcriptional changes persist despite returning the animals to a control diet, causing a permanent decrease in sweet taste. Our results uncover a new epigenetic mechanism that, in response to the dietary environment, regulates neural plasticity and feeding behavior to promote obesity.
The thalamus that speaks to the cortex: spontaneous activity in the developing brain
Our research team runs several related projects studying the cellular and molecular mechanisms involved in the development of axonal connections in the brain. In particular, our aim is to uncover the principles underlying thalamocortical axonal wiring, maintenance and ultimately the rewiring of connections, through an integrated and innovative experimental programme. The development of the thalamocortical wiring requires a precise topographical sorting of its connections. Each thalamic nucleus receives specific sensory information from the environment and projects topographically to its corresponding cortical. A second level of organization is achieved within each area, where thalamocortical connections display an intra-areal topographical organization, allowing the generation of accurate spatial representations within each cortical area. Therefore, the level of organization and specificity of the thalamocortical projections is much more complex than other projection systems in the CNS. The central hypothesis of our laboratory is that thalamocortical input influences and maintains the functional architecture of the sensory cortices. We also believe that rewiring and plasticity events can be triggered by activity-dependent mechanisms in the thalamus. Three major questions are been focused in the laboratory: i) the role of spontaneous patterns of activity in thalamocortical wiring and cortical development, ii) the role of the thalamus and its connectivity in the neuroplastic cortical changes following sensory deprivation, and iii) reprogramming thalamic cells for sensory circuit restoration. Within these projects we are using several experimental programmes, these include: optical imaging, manipulation of gene expression in vivo, cell and molecular biology, biochemistry, cell culture, sensory deprivation paradigms and electrophysiology. The results derived from our investigations will contribute to our understating of how reprogramming of cortical wiring takes place following brain damage and how cortical structure is maintained.
Exploring the impact of partial reprogramming on astrocyte biology and its implications for brain homeostasis and aging
FENS Forum 2024
Innovative models for amyotrophic lateral sclerosis research: Dermal fibroblasts and direct cell reprogramming
FENS Forum 2024
Molecular reprogramming of engram cells rescues memory in AD
FENS Forum 2024
Tumor tissue metabolomics informs metabolic reprogramming in IDH wild-type gliomas
FENS Forum 2024