Resonancia Magnética y Detección Remota: No se Necesita Estar tan Cerca”

La resonancia magnética nuclear está basada en el fenómeno del magnetismo nuclear que más aplicaciones ha encontrado para el estudio de enfermedades humanas. Usualmente la señal de RM es recibida y transmitida a distancias cercanas al objeto del que se quiere obtener una imagen. Otra alternativa es emitir y recibir la misma señal de manera remota haciendo uso de guías de onda. Este enfoque tiene la ventaja que se puede aplicar a altos campos magnéticos, la absorción de energía es menor, además es posible cubrir mayores regiones de interés y comodidad para el paciente. Por otro lado, sufre de baja calidad de imagen en algunos casos. En esta ocasión hablaremos de nuestra experiencia haciendo uso de este enfoque empleando una guía de ondas abierta y metamateriales tanto para sistemas clínicos como preclínicos de IRM.

Open source FPGA tools for building research devices

Edmund will present why to use FPGAs when building scientific instruments, when and why to use open source FPGA tools, the history of their development, their development status, currently supported FPGA families and functions, current developments in design languages and tools, the community, freely available design blocks, and possible future developments.

Trends in NeuroAI - Brain-like topography in transformers (Topoformer)

Dr. Nicholas Blauch will present on his work "Topoformer: Brain-like topographic organization in transformer language models through spatial querying and reweighting". Dr. Blauch is a postdoctoral fellow in the Harvard Vision Lab advised by Talia Konkle and George Alvarez. Paper link: https://openreview.net/pdf?id=3pLMzgoZSA Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri | https://groups.google.com/g/medarc-fmri).

Conversations with Caves? Understanding the role of visual psychological phenomena in Upper Palaeolithic cave art making

How central were psychological features deriving from our visual systems to the early evolution of human visual culture? Art making emerged deep in our evolutionary history, with the earliest art appearing over 100,000 years ago as geometric patterns etched on fragments of ochre and shell, and figurative representations of prey animals flourishing in the Upper Palaeolithic (c. 40,000 – 15,000 years ago). The latter reflects a complex visual process; the ability to represent something that exists in the real world as a flat, two-dimensional image. In this presentation, I argue that pareidolia – the psychological phenomenon of seeing meaningful forms in random patterns, such as perceiving faces in clouds – was a fundamental process that facilitated the emergence of figurative representation. The influence of pareidolia has often been anecdotally observed in Upper Palaeolithic art examples, particularly cave art where the topographic features of cave wall were incorporated into animal depictions. Using novel virtual reality (VR) light simulations, I tested three hypotheses relating to pareidolia in the caves of Upper Palaeolithic cave art in the caves of Las Monedas and La Pasiega (Cantabria, Spain). To evaluate this further, I also developed an interdisciplinary VR eye-tracking experiment, where participants were immersed in virtual caves based on the cave of El Castillo (Cantabria, Spain). Together, these case studies suggest that pareidolia was an intrinsic part of artist-cave interactions (‘conversations’) that influenced the form and placement of figurative depictions in the cave. This has broader implications for conceiving of the role of visual psychological phenomena in the emergence and development of figurative art in the Palaeolithic.

Trends in NeuroAI - Unified Scalable Neural Decoding (POYO)

Lead author Mehdi Azabou will present on his work "POYO-1: A Unified, Scalable Framework for Neural Population Decoding" (https://poyo-brain.github.io/). Mehdi is an ML PhD student at Georgia Tech advised by Dr. Eva Dyer. Paper link: https://arxiv.org/abs/2310.16046 Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri | https://groups.google.com/g/medarc-fmri).

Trends in NeuroAI - Meta's MEG-to-image reconstruction

Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri). Title: Brain-optimized inference improves reconstructions of fMRI brain activity Abstract: The release of large datasets and developments in AI have led to dramatic improvements in decoding methods that reconstruct seen images from human brain activity. We evaluate the prospect of further improving recent decoding methods by optimizing for consistency between reconstructions and brain activity during inference. We sample seed reconstructions from a base decoding method, then iteratively refine these reconstructions using a brain-optimized encoding model that maps images to brain activity. At each iteration, we sample a small library of images from an image distribution (a diffusion model) conditioned on a seed reconstruction from the previous iteration. We select those that best approximate the measured brain activity when passed through our encoding model, and use these images for structural guidance during the generation of the small library in the next iteration. We reduce the stochasticity of the image distribution at each iteration, and stop when a criterion on the "width" of the image distribution is met. We show that when this process is applied to recent decoding methods, it outperforms the base decoding method as measured by human raters, a variety of image feature metrics, and alignment to brain activity. These results demonstrate that reconstruction quality can be significantly improved by explicitly aligning decoding distributions to brain activity distributions, even when the seed reconstruction is output from a state-of-the-art decoding algorithm. Interestingly, the rate of refinement varies systematically across visual cortex, with earlier visual areas generally converging more slowly and preferring narrower image distributions, relative to higher-level brain areas. Brain-optimized inference thus offers a succinct and novel method for improving reconstructions and exploring the diversity of representations across visual brain areas. Speaker: Reese Kneeland is a Ph.D. student at the University of Minnesota working in the Naselaris lab. Paper link: https://arxiv.org/abs/2312.07705

Trends in NeuroAI - Meta's MEG-to-image reconstruction

Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri). This will be an informal journal club presentation, we do not have an author of the paper joining us. Title: Brain decoding: toward real-time reconstruction of visual perception Abstract: In the past five years, the use of generative and foundational AI systems has greatly improved the decoding of brain activity. Visual perception, in particular, can now be decoded from functional Magnetic Resonance Imaging (fMRI) with remarkable fidelity. This neuroimaging technique, however, suffers from a limited temporal resolution (≈0.5 Hz) and thus fundamentally constrains its real-time usage. Here, we propose an alternative approach based on magnetoencephalography (MEG), a neuroimaging device capable of measuring brain activity with high temporal resolution (≈5,000 Hz). For this, we develop an MEG decoding model trained with both contrastive and regression objectives and consisting of three modules: i) pretrained embeddings obtained from the image, ii) an MEG module trained end-to-end and iii) a pretrained image generator. Our results are threefold: Firstly, our MEG decoder shows a 7X improvement of image-retrieval over classic linear decoders. Second, late brain responses to images are best decoded with DINOv2, a recent foundational image model. Third, image retrievals and generations both suggest that MEG signals primarily contain high-level visual features, whereas the same approach applied to 7T fMRI also recovers low-level features. Overall, these results provide an important step towards the decoding - in real time - of the visual processes continuously unfolding within the human brain. Speaker: Dr. Paul Scotti (Stability AI, MedARC) Paper link: https://arxiv.org/abs/2310.19812

Trends in NeuroAI - SwiFT: Swin 4D fMRI Transformer

Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri). Title: SwiFT: Swin 4D fMRI Transformer Abstract: Modeling spatiotemporal brain dynamics from high-dimensional data, such as functional Magnetic Resonance Imaging (fMRI), is a formidable task in neuroscience. Existing approaches for fMRI analysis utilize hand-crafted features, but the process of feature extraction risks losing essential information in fMRI scans. To address this challenge, we present SwiFT (Swin 4D fMRI Transformer), a Swin Transformer architecture that can learn brain dynamics directly from fMRI volumes in a memory and computation-efficient manner. SwiFT achieves this by implementing a 4D window multi-head self-attention mechanism and absolute positional embeddings. We evaluate SwiFT using multiple large-scale resting-state fMRI datasets, including the Human Connectome Project (HCP), Adolescent Brain Cognitive Development (ABCD), and UK Biobank (UKB) datasets, to predict sex, age, and cognitive intelligence. Our experimental outcomes reveal that SwiFT consistently outperforms recent state-of-the-art models. Furthermore, by leveraging its end-to-end learning capability, we show that contrastive loss-based self-supervised pre-training of SwiFT can enhance performance on downstream tasks. Additionally, we employ an explainable AI method to identify the brain regions associated with sex classification. To our knowledge, SwiFT is the first Swin Transformer architecture to process dimensional spatiotemporal brain functional data in an end-to-end fashion. Our work holds substantial potential in facilitating scalable learning of functional brain imaging in neuroscience research by reducing the hurdles associated with applying Transformer models to high-dimensional fMRI. Speaker: Junbeom Kwon is a research associate working in Prof. Jiook Cha’s lab at Seoul National University. Paper link: https://arxiv.org/abs/2307.05916

How fly neurons compute the direction of visual motion

Detecting the direction of image motion is important for visual navigation, predator avoidance and prey capture, and thus essential for the survival of all animals that have eyes. However, the direction of motion is not explicitly represented at the level of the photoreceptors: it rather needs to be computed by subsequent neural circuits, involving a comparison of the signals from neighboring photoreceptors over time. The exact nature of this process represents a classic example of neural computation and has been a longstanding question in the field. Much progress has been made in recent years in the fruit fly Drosophila melanogaster by genetically targeting individual neuron types to block, activate or record from them. Our results obtained this way demonstrate that the local direction of motion is computed in two parallel ON and OFF pathways. Within each pathway, a retinotopic array of four direction-selective T4 (ON) and T5 (OFF) cells represents the four Cartesian components of local motion vectors (leftward, rightward, upward, downward). Since none of the presynaptic neurons is directionally selective, direction selectivity first emerges within T4 and T5 cells. Our present research focuses on the cellular and biophysical mechanisms by which the direction of image motion is computed in these neurons.

Estimating repetitive spatiotemporal patterns from resting-state brain activity data

Repetitive spatiotemporal patterns in resting-state brain activities have been widely observed in various species and regions, such as rat and cat visual cortices. Since they resemble the preceding brain activities during tasks, they are assumed to reflect past experiences embedded in neuronal circuits. Moreover, spatiotemporal patterns involving whole-brain activities may also reflect a process that integrates information distributed over the entire brain, such as motor and visual information. Therefore, revealing such patterns may elucidate how the information is integrated to generate consciousness. In this talk, I will introduce our proposed method to estimate repetitive spatiotemporal patterns from resting-state brain activity data and show the spatiotemporal patterns estimated from human resting-state magnetoencephalography (MEG) and electroencephalography (EEG) data. Our analyses suggest that the patterns involved whole-brain propagating activities that reflected a process to integrate the information distributed over frequencies and networks. I will also introduce our current attempt to reveal signal flows and their roles in the spatiotemporal patterns using a big dataset. - Takeda et al., Estimating repetitive spatiotemporal patterns from resting-state brain activity data. NeuroImage (2016); 133:251-65. - Takeda et al., Whole-brain propagating patterns in human resting-state brain activities. NeuroImage (2021); 245:118711.

A carnivorous mushroom paralyzes and kills nematodes via a volatile ketone

How a fungus overcomes the defence of C. elegans

Autopoiesis and Enaction in the Game of Life

Enaction plays a central role in the broader fabric of so-called 4E (embodied, embedded, extended, enactive) cognition. Although the origin of the enactive approach is widely dated to the 1991 publication of the book "The Embodied Mind" by Varela, Thompson and Rosch, many of the central ideas trace to much earlier work. Over 40 years ago, the Chilean biologists Humberto Maturana and Francisco Varela put forward the notion of autopoiesis as a way to understand living systems and the phenomena that they generate, including cognition. Varela and others subsequently extended this framework to an enactive approach that places biological autonomy at the foundation of situated and embodied behavior and cognition. I will describe an attempt to place Maturana and Varela's original ideas on a firmer foundation by studying them within the context of a toy model universe, John Conway's Game of Life (GoL) cellular automata. This work has both pedagogical and theoretical goals. Simple concrete models provide an excellent vehicle for introducing some of the core concepts of autopoiesis and enaction and explaining how these concepts fit together into a broader whole. In addition, a careful analysis of such toy models can hone our intuitions about these concepts, probe their strengths and weaknesses, and move the entire enterprise in the direction of a more mathematically rigorous theory. In particular, I will identify the primitive processes that can occur in GoL, show how these can be linked together into mutually-supporting networks that underlie persistent bounded entities, map the responses of such entities to environmental perturbations, and investigate the paths of mutual perturbation that these entities and their environments can undergo.

Vision for Predation

Adaptation via innovation in the animal kingdom

Over the course of evolution, the human race has achieved a number of remarkable innovations, that have enabled us to adapt to and benefit from the environment ever more effectively. The ongoing environmental threats and health disasters of our world have now made it crucial to understand the cognitive mechanisms behind innovative behaviours. In my talk, I will present two research projects with examples of innovation-based behavioural adaptation from the taxonomic kingdom of animals, serving as a comparative psychological model for mapping the evolution of innovation. The first project focuses on the challenge of overcoming physical disability. In this study, we investigated an injured kea (Nestor notabilis) that exhibits an efficient, intentional, and innovative tool-use behaviour to compensate his disability, showing evidence for innovation-based adaptation to a physical disability in a non-human species. The second project focuses on the evolution of fire use from a cognitive perspective. Fire has been one of the most dominant ecological forces in human evolution; however, it is still unknown what capabilities and environmental factors could have led to the emergence of fire use. In the core study of this project, we investigated a captive population of Japanese macaques (Macaca fuscata) that has been regularly exposed to campfires during the cold winter months for over 60 years. Our results suggest that macaques are able to take advantage of the positive effects of fire while avoiding the dangers of flames and hot ashes, and exhibit calm behaviour around the bonfire. In addition, I will present a research proposal targeting the foraging behaviour of predatory birds in parts of Australia frequently affected by bushfires. Anecdotal reports suggest that some birds use burning sticks to spread the flames, a behaviour that has not been scientifically observed and evaluated. In summary, the two projects explore innovative behaviours along three different species groups, three different habitats, and three different ecological drivers, providing insights into the cognitive and behavioural mechanisms of adaptation through innovation.

How fly neurons compute the direction of visual motion

Detecting the direction of image motion is important for visual navigation, predator avoidance and prey capture, and thus essential for the survival of all animals that have eyes. However, the direction of motion is not explicitly represented at the level of the photoreceptors: it rather needs to be computed by subsequent neural circuits. The exact nature of this process represents a classic example of neural computation and has been a longstanding question in the field. Our results obtained in the fruit fly Drosophila demonstrate that the local direction of motion is computed in two parallel ON and OFF pathways. Within each pathway, a retinotopic array of four direction-selective T4 (ON) and T5 (OFF) cells represents the four Cartesian components of local motion vectors (leftward, rightward, upward, downward). Since none of the presynaptic neurons is directionally selective, direction selectivity first emerges within T4 and T5 cells. Our present research focuses on the cellular and biophysical mechanisms by which the direction of image motion is computed in these neurons.

It’s not over our heads: Why human language needs a body

n the ‘orthodox’ view, cognition has been seen as manipulation of symbolic, mental representations, separate from the body. This dualist Cartesian approach characterised much of twentieth-century thought and is still taken for granted by many people today. Language, too, has for a long time been treated across scientific domains as a system operating largely independently from perception, action, and the body (articulatory-perceptual organs notwithstanding). This could lead one into believing that to emulate linguistic behaviour, it would suffice to develop ‘software’ operating on abstract representations that would work on any computational machine. Yet the brain is not the sole problem-solving resource we have at our disposal. The disembodied picture is inaccurate for numerous reasons, which will be presented addressing the issue of the indissoluble link between cognition, language, body, and environment in understanding and learning. The talk will conclude with implications and suggestions for pedagogy, relevant for disciplines as diverse as instruction in language, mathematics, and sports.

A Flash of Darkness within Dusk: Crossover inhibition in the mouse retina

To survive in the wild small rodents evolved specialized retinas. To escape predators, looming shadows need to be detected with speed and precision. To evade starvation, small seeds, grass, nuts and insects need to also be detected quickly. Some of these succulent seeds and insects may be camouflaged offering only low contrast targets.Moreover, these challenging tasks need to be accomplished continuously at dusk, night, dawn and daytime. Crossover inhibition is thought to be involved in enhancing contrast detectionin the microcircuits of the inner plexiform layer of the mammalian retina. The AII amacrine cells are narrow field cells that play a key role in crossover inhibition. Our lab studies the synaptic physiology that regulates glycine release from AII amacrine cellsin mouse retina. These interneurons receive excitation from rod and conebipolar cells and transmit excitation to ON-type bipolar cell terminals via gap junctions. They also transmit inhibition via multiple glycinergic synapses onto OFF bipolar cell terminals.AII amacrine cells are thus a central hub of synaptic information processing that cross links the ON and the OFF pathways. What are the functions of crossover inhibition? How does it enhance contrast detection at different ambient light levels? How is the dynamicrange, frequency response and synaptic gain of glycine release modulated by luminance levels and circadian rhythms? How is synaptic gain changed by different extracellular neuromodulators, like dopamine, and by intracellular messengers like cAMP, phosphateand Ca2+ ions from Ca2+ channels and Ca2+ stores? My talk will try to answer some of these questions and will pose additional ones. It will end with further hypothesis and speculations on the multiple roles of crossover inhibition.

Improving Communication With the Brain Through Electrode Technologies

Over the past 30 years bionic devices such as cochlear implants and pacemakers, have used a small number of metal electrodes to restore function and monitor activity in patients following disease or injury of excitable tissues. Growing interest in neurotechnologies, facilitated by ventures such as BrainGate, Neuralink and the European Human Brain Project, has increased public awareness of electrotherapeutics and led to both new applications for bioelectronics and a growing demand for less invasive devices with improved performance. Coupled with the rapid miniaturisation of electronic chips, bionic devices are now being developed to diagnose and treat a wide variety of neural and muscular disorders. Of particular interest is the area of high resolution devices that require smaller, more densely packed electrodes. Due to poor integration and communication with body tissue, conventional metallic electrodes cannot meet these size and spatial requirements. We have developed a range of polymer based electronic materials including conductive hydrogels (CHs), conductive elastomers (CEs) and living electrodes (LEs). These technologies provide synergy between low impedance charge transfer, reduced stiffness and an ability to be provide a biologically active interface. A range of electrode approaches are presented spanning wearables, implantables and drug delivery devices. This talk outlines the materials development and characterisation of both in vitro properties and translational in vivo performance. The challenges for translation and commercial uptake of novel technologies will also be discussed.

Predator-prey interactions: the avian visual sensory perspective

My research interests are centered on animal ecology, and more specifically include the following areas: visual ecology, behavioral ecology, and conservation biology, as well as the interactions between them. My research is question-driven. I answer my questions in a comprehensive manner, using a combination of empirical, theoretical, and comparative approaches. My model species are usually birds, but I have also worked with fish, mammals, amphibians, and insects. ​I was fortunate to enrich my education by attending Universities in different parts of the world. I did my undergraduate, specialized in ecology and biodiversity, at the "Universidad Nacional de Cordoba", Argentina. My Ph.D. was in animal ecology and conservation biology at the "Universidad Complutense de Madrid", Spain. My two post-docs were focused on behavioral ecology; the first one at University of Oxford (United Kingdom), and the second one at University of Minnesota (USA). I was an Assistant Professor at California State University Long Beach for almost six years. I am now a Full Professor of Biological Sciences at Purdue University.

Analyzing Retinal Disease Using Electron Microscopic Connectomics

John DowlingJohn E. Dowling received his AB and PhD from Harvard University. He taught in the Biology Department at Harvard from 1961 to 1964, first as an Instructor, then as assistant professor. In 1964 he moved to Johns Hopkins University, where he held an appointment as associate professor of Ophthalmology and Biophysics. He returned to Harvard as professor of Biology in 1971, was the Maria Moors Cabot Professor of Natural Sciences from 1971-2001, Harvard College professor from 1999-2004 and is presently the Gordon and Llura Gund Professor of Neurosciences. Dowling was chairman of the Biology Department at Harvard from 1975 to 1978 and served as associate dean of the faculty of Arts and Sciences from 1980 to 1984. He was Master of Leverett House at Harvard from 1981-1998 and currently serves as president of the Corporation of The Marine Biological Laboratory in Woods Hole. He is a Fellow of the American Academy of Arts and Sciences, a member of the National Academy of Sciences and a member of the American Philosophical Society. Awards that Dowling received include the Friedenwald Medal from the Association of Research in Ophthalmology and Vision in 1970, the Annual Award of the New England Ophthalmological Society in 1979, the Retinal Research Foundation Award for Retinal Research in 1981, an Alcon Vision Research Recognition Award in 1986, a National Eye Institute's MERIT award in 1987, the Von Sallman Prize in 1992, The Helen Keller Prize for Vision Research in 2000 and the Llura Ligget Gund Award for Lifetime Achievement and Recognition of Contribution to the Foundation Fighting Blindness in 2001. He was granted an honorary MD degree by the University of Lund (Sweden) in 1982 and an honorary Doctor of Laws degree from Dalhousie University (Canada) in 2012. Dowling's research interests have focused on the vertebrate retina as a model piece of the brain. He and his collaborators have long been interested in the functional organization of the retina, studying its synaptic organization, the electrical responses of the retinal neurons, and the mechanisms underlying neurotransmission and neuromodulation in the retina. Dowling became interested in zebrafish as a system in which one could explore the development and genetics of the vertebrate retina about 20 years ago. Part of his research team has focused on retinal development in zebrafish and the role of retinoic acid in early eye and photoreceptor development. A second group has developed behavioral tests to isolate mutations, both recessive and dominant, specific to the visual system.

Capacitance clamp - artificial capacitance in biological neurons via dynamic clamp

A basic time scale in neural dynamics from single cells to the network level is the membrane time constant - set by a neuron’s input resistance and its capacitance. Interestingly, the membrane capacitance appears to be more dynamic than previously assumed with implications for neural function and pathology. Indeed, altered membrane capacitance has been observed in reaction to physiological changes like neural swelling, but also in ageing and Alzheimer's disease. Importantly, according to theory, even small changes of the capacitance can affect neuronal signal processing, e.g. increase network synchronization or facilitate transmission of high frequencies. In experiment, robust methods to modify the capacitance of a neuron have been missing. Here, we present the capacitance clamp - an electrophysiological method for capacitance control based on an unconventional application of the dynamic clamp. In its original form, dynamic clamp mimics additional synaptic or ionic conductances by injecting their respective currents. Whereas a conductance directly governs a current, the membrane capacitance determines how fast the voltage responds to a current. Accordingly, capacitance clamp mimics an altered capacitance by injecting a dynamic current that slows down or speeds up the voltage response (Fig 1 A). For the required dynamic current, the experimenter only has to specify the original cell and the desired target capacitance. In particular, capacitance clamp requires no detailed model of present conductances and thus can be applied in every excitable cell. To validate the capacitance clamp, we performed numerical simulations of the protocol and applied it to modify the capacitance of cultured neurons. First, we simulated capacitance clamp in conductance based neuron models and analysed impedance and firing frequency to verify the altered capacitance. Second, in dentate gyrus granule cells from rats, we could reliably control the capacitance in a range of 75 to 200% of the original capacitance and observed pronounced changes in the shape of the action potentials: increasing the capacitance reduced after-hyperpolarization amplitudes and slowed down repolarization. To conclude, we present a novel tool for electrophysiology: the capacitance clamp provides reliable control over the capacitance of a neuron and thereby opens a new way to study the temporal dynamics of excitable cells.

The pathophysiology of prodromal Parkinson’s disease

Studying the pathophysiology of late stage Parkinson’s disease (PD) – after the patients have experienced severe neuronal loss – has helped develop various symptomatic treatments for PD (e.g., deep brain stimulation). However, it has been of limited use in developing neuroprotective disease-modifying therapies (DMTs), because DMTs require interventions at much earlier stages of PD when vulnerable neurons are still intact. Because PD patients exhibit various non-motor prodromal symptoms (ie, symptoms that predate diagnosis), understanding the pathophysiology underlying these symptom could lead to earlier diagnosis and intervention. In my talk, I will present a recently elucidated example of how PD pathologies alter the channel biophysics of intact vagal motoneurons (known to be selectively vulnerable in PD) to drive dysautonomia that is reminiscent of prodromal PD. I will discuss how elucidating the pathophysiology of prodromal symptoms can lead to earlier diagnosis through the development of physiological biomarkers for PD.

Towards targeted therapies for the treatment of Dravet Syndrome

Dravet syndrome is a severe epileptic encephalopathy that begins during the first year of life and leads to severe cognitive and social interaction deficits. It is mostly caused by heterozygous loss-of-function mutations in the SCN1A gene, which encodes for the alpha-subunit of the voltage-gated sodium channel (Nav1.1) and is responsible mainly of GABAergic interneuron excitability. While different therapies based on the upregulation of the healthy allele of the gene are being developed, the dynamics of reversibility of the pathology are still unclear. In fact, whether and to which extent the pathology is reversible after symptom onset and if it is sufficient to ensure physiological levels of Scn1a during a specific critical period of time are open questions in the field and their answers are required for proper development of effective therapies. We generated a novel Scn1a conditional knock-in mouse model (Scn1aSTOP) in which the endogenous Scn1a gene is silenced by the insertion of a floxed STOP cassette in an intron of Scn1a gene; upon Cre recombinase expression, the STOP cassette is removed, and the mutant allele can be reconstituted as a functional Scn1a allele. In this model we can reactivate the expression of Scn1a exactly in the neuronal subtypes in which it is expressed and at its physiological level. Those aspects are crucial to obtain a final answer on the reversibility of DS after symptom onset. We exploited this model to demonstrate that global brain re-expression of the Scn1a gene when symptoms are already developed (P30) led to a complete rescue of both spontaneous and thermic inducible seizures and amelioration of behavioral abnormalities characteristic of this model. We also highlighted dramatic gene expression alterations associated with astrogliosis and inflammation that, accordingly, were rescued by Scn1a gene expression normalization at P30. Moreover, employing a conditional knock-out mouse model of DS we reported that ensuring physiological levels of Scn1a during the critical period of symptom appearance (until P30) is not sufficient to prevent the DS, conversely, mice start to die of SUDEP and develop spontaneous seizures. These results offer promising insights in the reversibility of DS and can help to accelerate therapeutic translation, providing important information on the timing for gene therapy delivery to Dravet patients.

Untangling brain wide current flow using neural network models

Rajanlab designs neural network models constrained by experimental data, and reverse engineers them to figure out how brain circuits function in health and disease. Recently, we have been developing a powerful new theory-based framework for “in-vivo tract tracing” from multi-regional neural activity collected experimentally. We call this framework CURrent-Based Decomposition (CURBD). CURBD employs recurrent neural networks (RNNs) directly constrained, from the outset, by time series measurements acquired experimentally, such as Ca2+ imaging or electrophysiological data. Once trained, these data-constrained RNNs let us infer matrices quantifying the interactions between all pairs of modeled units. Such model-derived “directed interaction matrices” can then be used to separately compute excitatory and inhibitory input currents that drive a given neuron from all other neurons. Therefore different current sources can be de-mixed – either within the same region or from other regions, potentially brain-wide – which collectively give rise to the population dynamics observed experimentally. Source de-mixed currents obtained through CURBD allow an unprecedented view into multi-region mechanisms inaccessible from measurements alone. We have applied this method successfully to several types of neural data from our experimental collaborators, e.g., zebrafish (Deisseroth lab, Stanford), mice (Harvey lab, Harvard), monkeys (Rudebeck lab, Sinai), and humans (Rutishauser lab, Cedars Sinai), where we have discovered both directed interactions brain wide and inter-area currents during different types of behaviors. With this powerful framework based on data-constrained multi-region RNNs and CURrent Based Decomposition (CURBD), we ask if there are conserved multi-region mechanisms across different species, as well as identify key divergences.

Vision for escape and pursuit

We want to understand how the visual system detects and tracks salient stimuli in the environment to initiate and guide specific behaviors (i.e., visual neuroethology). Predator avoidance and prey capture are central selection pressures of animal evolution. Mice use vision to detect aerial predators and hunt insects. I will discuss studies from my group that identify specific circuits and pathways in the early visual system (i.e., the retina and its subcortical targets) mediating predator avoidance and prey capture in mice. Our results highlight the importance of subcellular visual processing in the retina and the alignment of viewing strategies with region- and cell-type-specific retinal ganglion cell projection patterns to the brain.

The When, Where and What of visual memory formation

The eyes send a continuous stream of about two million nerve fibers to the brain, but only a fraction of this information is stored as visual memories. This talk will detail three neurocomputational models that attempt an understanding how the visual system makes on-the-fly decisions about how to encode that information. First, the STST family of models (Bowman & Wyble 2007; Wyble, Potter, Bowman & Nieuwenstein 2011) proposes mechanisms for temporal segmentation of continuous input. The conclusion of this work is that the visual system has mechanisms for rapidly creating brief episodes of attention that highlight important moments in time, and also separates each episode from temporally adjacent neighbors to benefit learning. Next, the RAGNAROC model (Wyble et al. 2019) describes a decision process for determining the spatial focus (or foci) of attention in a spatiotopic field and the neural mechanisms that provide enhancement of targets and suppression of highly distracting information. This work highlights the importance of integrating behavioral and electrophysiological data to provide empirical constraints on a neurally plausible model of spatial attention. The model also highlights how a neural circuit can make decisions in a continuous space, rather than among discrete alternatives. Finally, the binding pool (Swan & Wyble 2014; Hedayati, O’Donnell, Wyble in Prep) provides a mechanism for selectively encoding specific attributes (i.e. color, shape, category) of a visual object to be stored in a consolidated memory representation. The binding pool is akin to a holographic memory system that layers representations of select latent representations corresponding to different attributes of a given object. Moreover, it can bind features into distinct objects by linking them to token placeholders. Future work looks toward combining these models into a coherent framework for understanding the full measure of on-the-fly attentional mechanisms and how they improve learning.

Safety in numbers: how animals use motion of others as threat or safety cues

Our work concerns the general problem of adaptive behaviour in response to predatory threats, and of the neural mechanisms underlying a choice between strategies. When faced with a threat, an animal must decide whether to freeze, reducing its chances of being noticed, or to flee to the safety of a refuge. Animals from fish to primates choose between these two alternatives when confronted by an attacking predator, a choice that largely depends on the context in which the threat occurs. Recent work has made strides identifying the pre-motor circuits, and their inputs, which control freezing behaviour in rodents, but how contextual information is integrated to guide this choice is still far from understood. The social environment is a potent contextual modulator of defensive behaviours of animals in a group. Indeed, anti-predation strategies are believed to be a major driving force for the evolution of sociality. We recently found that fruit flies in response to visual looming stimuli, simulating a large object on collision course, make rapid freeze/flee choices accompanied by lasting changes in the fly’s internal state, reflected in altered cardiac activity. In this talk, I will discuss our work on how flies process contextual cues, focusing on the social environment, to guide their behavioural response to a threat. We have identified a social safety cue, resumption of activity, and visual projection neurons involved in processing this cue. Given the knowledge regarding sensory detection of looming threats and descending neuron involved in the expression of freezing, we are now in a unique position to understand how information about a threat is integrated with cues from the social environment to guide the choice of whether to freeze.

Neural circuit and genetic bases of behaviour in Platynereis larva

We study the larval stages of the marine annelid Platynereis dumerilii, a powerful experimental system for neural circuits. With serial electron microscopy, we have reconstructed the entire nervous and effector systems of a Platynereis larva. We use neurogenetics, activity imaging, and behavioural experiments to understand circuit activity and how the nervous system controls behaviour and physiology. Platynereis is one of very few systems where these different approaches can be combined to study an entire nervous system. I will talk about circuits for the whole-body coordination of locomotor cilia and a hydrodynamic startle response for predator avoidance.

Stochastic control of passive colloidal objects by micro-swimmers

The way single colloidal objects behave in presence of active forces arising from within the bulk of the system is crucial to many situations, notably biological and ecological (e.g. intra-cellular transport, predation), and potential medical or environmental applications (e.g. targeted delivery of cargoes, depollution of waters and soils). In this talk I will present experimental findings that my collaborators and I have obtained over the past years on the dynamics of single Brownian colloids in suspensions of biological micro-swimmers, especially the green alga Chlamydomonas reinhardtii. I'll show notably that spatial heterogeneities and anisotropies in the active particles statistics can control the preferential localisation of their passive counterparts. The results will be rationalized using theoretical approaches from hydrodynamics and stochastic processes.

Development of Sara-home: a novel assessment tool for patients with ataxia

Senescencia celular y su impacto en enfermedades neurodegenerativas

Las enfermedades neurodegenerativas como la Enfermedad de Alzheimer, Enfermedad de Parkinson y la Esclerosis Lateral Amiotrófica tienen una prevalencia creciente en nuestra sociedad, de acuerdo con el aumento de la expectativa de vida. Durante el envejecimiento, las células gliales sufren cambios funcionales favoreciendo la “neuroinflamación”, que tiene un reconocido papel patogénico en la progresión de la enfermedad neurodegenerativa. Estudios recientes demuestran que durante el envejecimiento del sistema nervioso se acumulan notablemente células senescentes, tanto de estirpe neuronal como glial. Las células senescentes no proliferan, muchas de ellas exhiben un fenotipo secretor (SASP) con capacidad de inducir inflamación. La eliminación de células senescentes por ablación genética inducida farmacológicamente o por bloqueos de fármacos senolíticos mejoran la neuroinflamación y disminuyen la neurotoxicidad. En la presentación, se realizará una revisión de la bibliografía sobre este tema y se realizará un análisis del potencial terapéutico de fármacos senolíticos como una aproximación terapéutica novedosa de las enfermedades neurodegenerativas.

Inferring brain-wide current flow using data-constrained neural network models

Rajanlab designs neural network models constrained by experimental data, and reverse engineers them to figure out how brain circuits function in health and disease. Recently, we have been developing a powerful new theory-based framework for “in-vivo tract tracing” from multi-regional neural activity collected experimentally. We call this framework CURrent-Based Decomposition (CURBD). CURBD employs recurrent neural networks (RNNs) directly constrained, from the outset, by time series measurements acquired experimentally, such as Ca2+ imaging or electrophysiological data. Once trained, these data-constrained RNNs let us infer matrices quantifying the interactions between all pairs of modeled units. Such model-derived “directed interaction matrices” can then be used to separately compute excitatory and inhibitory input currents that drive a given neuron from all other neurons. Therefore different current sources can be de-mixed – either within the same region or from other regions, potentially brain-wide – which collectively give rise to the population dynamics observed experimentally. Source de-mixed currents obtained through CURBD allow an unprecedented view into multi-region mechanisms inaccessible from measurements alone. We have applied this method successfully to several types of neural data from our experimental collaborators, e.g., zebrafish (Deisseroth lab, Stanford), mice (Harvey lab, Harvard), monkeys (Rudebeck lab, Sinai), and humans (Rutishauser lab, Cedars Sinai), where we have discovered both directed interactions brain wide and inter-area currents during different types of behaviors. With this framework based on data-constrained multi-region RNNs and CURrent Based Decomposition (CURBD), we can ask if there are conserved multi-region mechanisms across different species, as well as identify key divergences.

Learning Neurobiology with electric fish

Electric Gymnotiform fish live in muddy, shallow waters near the shore – hiding in the dense filamentous roots of floating plants such as Eichornia crassipes (“camalote”). They explore their surroundings by using a series of electric pulses that serve as self emitted carrier of electrosensory signals. This propagates at the speed of light through this spongiform habitat and is barely sensed by the lateral line of predators and prey. The emitted field polarizes the surroundings according to the difference in impedance with water which in turn modifies the profile of transcutaneous currents considered as an electrosensory image. Using this system, pulse Gymnotiformes create an electrosensory bubble where an object’s location, impedance, size and other characteristics are discriminated and probably recognized. Although consciousness is still not well-proven, cognitive functions as volition, attention, and path integration have been shown. Here I will summarize different aspects of the electromotor electrosensory loop of pulse Gymnotiforms. First, I will address how objects are polarized with a stereotyped but temporospatially complex electric field, consisting of brief pulses emitted at regular intervals. This relies on complex electric organs quasi periodically activated through an electromotor coordination system by a pacemaker in the medulla. Second, I will deal with the imaging mechanisms of pulse gymnotiform fish and the presence of two regions in the electrosensory field, a rostral region where the field time course is coherent and field vector direction is constant all along the electric organ discharge and a lateral region where the field time course is site specific and field vector direction describes a stereotyped 3D trajectory. Third, I will describe the electrosensory mosaic and their characteristics. Receptor and primary afferents correspond one to one showing subtypes optimally responding to the time course of the self generated pulse with a characteristic train of spikes. While polarized objects at the rostral region project their electric images on the perioral region where electrosensory receptor density, subtypes and central projection are maximal, the image of objects on the side recruit a single type of scattered receptors. Therefore, the rostral mosaic has been likened to an electrosensory fovea and its receptive field referred to as foveal field. The rest of the mosaic and field are referred to as peripheral. Finally, I will describe ongoing work on early processing structures. I will try to generate an integrated view, including anatomical and functional data obtained in vitro, acute experiments, and unitary recordings in freely moving fish. We have recently shown have shown that these fish tract allo-generated fields and the virtual fields generated by nearby objects in the presence of self-generated fields to explore the nearby environment. These data together with the presence of a multimodal receptor mosaic at the cutaneous surface particularly surrounding the mouth and an important role of proprioception in early sensory processing suggests the hypothesis that the active electrosensory system is part of a multimodal haptic sense.

Monkey Talk – what studies about nonhuman primate vocal communication reveal about the evolution of speech

The evolution of speech is considered to be one of the hardest problems in science. Studies of the communicative abilities of our closest living relatives, the nonhuman primates, aim to contribute to a better understanding of the emergence of this uniquely human capability. Following a brief introduction over the key building blocks that make up the human speech faculty, I will focus on the question of meaning in nonhuman primate vocalizations. While nonhuman primate calls may be highly context specific, thus giving rise to the notion of ‘referentiality’, comparisons across closely related species suggest that this specificity is evolved rather than learned. Yet, as in humans, the structure of calls varies with arousal and affective state, and there is some evidence for effects of sensory-motor integration in vocal production. Thus, the vocal production of nonhuman primates bears little resemblance to the symbolic and combinatorial features of human speech, while basic production mechanisms are shared. Listeners, in contrast, are able learning the meaning of new sounds. A recent study using artificial predator shows that this learning may be extremely rapid. Furthermore, listeners are able to integrate information from multiple sources to make adaptive decisions, which renders the vocal communication system as a whole relatively flexible and powerful. In conclusion, constraints at the side of vocal production, including limits in social cognition and motivation to share experiences, rather than constraints at the side of the recipient explain the differences in communicative abilities between humans and other animals.

Neuroscience in the mud: interplay between lab and field research for understanding animal behavior

Investigations of the neurophysiological processes underlying animal behaviors are almost exclusively done inside the laboratory, typically using few animal models born and reared under artificially stabilized conditions. Yet, animals living in the wild have to cope with much complex and variable environments. Thus, while the laboratory provides the technical possibilities for physiological research, the field offers a more realistic perspective about the animal´s behavioral abilities. We study neural circuits underlying the visually guided prey and predator behaviors in a semiterrestrial crab. By combining lab and field experiments we have, for example, found that the level of predation risk experienced by the animals in the wild affects the responsiveness of identified neurons involved in the animal escape response. Using this and other results from my lab I will illustrate and discuss the importance of complementing lab with field studies in wild animals for understanding the neural mechanisms subserving behavior.

Tools for Analyzing and Repairing the Brain. (Simultaneous translation to Spanish)

To enable the understanding and repair of complex biological systems, such as the brain, we are creating novel optical tools that enable molecular-resolution maps of such systems, as well as technologies for observing and controlling high-speed physiological dynamics in such systems. First, we have developed a method for imaging specimens with nanoscale precision, by embedding them in a swellable polymer, homogenizing their mechanical properties, and exposing them to water – which causes them to expand manyfold isotropically. This method, which we call expansion microscopy (ExM), enables ordinary microscopes to do nanoscale imaging, in a multiplexed fashion – important, for example, for brain mapping. Second, we have developed a set of genetically-encoded reagents, known as optogenetic tools, that when expressed in specific neurons, enable their electrical activities to be precisely driven or silenced in response to millisecond timescale pulses of light. Finally, we are designing, and evolving, novel reagents, such as fluorescent voltage indicators and somatically targeted calcium indicators, to enable the imaging of fast physiological processes in 3-D with millisecond precision. In this way we aim to enable the systematic mapping, control, and dynamical observation of complex biological systems like the brain. The talk will be simultaneously interpreted English-Spanish) by the Interpreter, Mg. Lourdes Martino. Para permitir la comprensión y reparación de sistemas biológicos complejos, como el cerebro, estamos creando herramientas ópticas novedosas que permiten crear mapas de resolución molecular de dichos sistemas, así como tecnologías para observar y controlar la dinámica fisiológica de alta velocidad en dichos sistemas. Primero, hemos desarrollado un método para obtener imágenes de muestras con precisión a nanoescala, incrustándolas en un polímero hinchable, homogeneizando sus propiedades mecánicas y exponiéndolas al agua, lo que hace que se expandan muchas veces isotrópicamente. Este método, que llamamos microscopía de expansión (ExM), permite que los microscopios ordinarios obtengan imágenes a nanoescala, de forma multiplexada, lo que es importante, por ejemplo, para el mapeo cerebral. En segundo lugar, hemos desarrollado un conjunto de reactivos codificados genéticamente, conocidos como herramientas optogenéticas, que cuando se expresan en neuronas específicas, permiten que sus actividades eléctricas sean activadas o silenciadas con precisión en respuesta a pulsos de luz en una escala de tiempo de milisegundos. Finalmente, estamos diseñando y desarrollando reactivos novedosos, como indicadores de voltaje fluorescentes e indicadores de calcio dirigidos somáticamente, para permitir la obtención de imágenes de procesos fisiológicos rápidos en 3-D con precisión de milisegundos. De esta manera, nuestro objetivo es permitir el mapeo sistemático, el control y la observación dinámica de sistemas biológicos complejos como el cerebro. La conferencia será traducida simultáneamente al español por la intérprete Mg. Lourdes Martino.

Differential Resilience of Neurons and Networks with Similar Behavior to Perturbation. (Simultaneous translation to Spanish)

Both computational and experimental results in single neurons and small networks demonstrate that very similar network function can result from quite disparate sets of neuronal and network parameters. Using the crustacean stomatogastric nervous system, we study the influence of these differences in underlying structure on differential resilience of individuals to a variety of environmental perturbations, including changes in temperature, pH, potassium concentration and neuromodulation. We show that neurons with many different kinds of ion channels can smoothly move through different mechanisms in generating their activity patterns, thus extending their dynamic range. The talk will be simultaneously translated to spanish by the interpreter Liliana Viera, MSc. Los resultados tanto computacionales como experimentales en neuronas individuales y redes pequeñas demuestran que funcionamientos de redes muy similares pueden pueden resultar de conjuntos bastante dispares de parámetros neuronales y de las redes. Utilizando el sistema nervioso estomatogástrico de los crustáceos, estudiamos la influencia de estas diferencias en la estructura subyacente en la resistencia diferencial de los individuos a una variedad de perturbaciones ambientales, incluidos los cambios de temperatura, pH, concentración de potasio y neuromodulación. Mostramos que neuronas con muchos tipos diferentes de canales iónicos pueden moverse suavemente a través de diferentes mecanismos para generar sus patrones de actividad, extendiendo así su rango dinámico. La conferencia será traducida simultáneamente al español por la intérprete Liliana Viera MSc.

Computational models of neural development

Unlike even the most sophisticated current forms of artificial intelligence, developing biological organisms must build their neural hardware from scratch. Furthermore they must start to evade predators and find food before this construction process is complete. I will discuss an interdisciplinary program of mathematical and experimental work which addresses some of the computational principles underlying neural development. This includes (i) how growing axons navigate to their targets by detecting and responding to molecular cues in their environment, (ii) the formation of maps in the visual cortex and how these are influenced by visual experience, and (iii) how patterns of neural activity in the zebrafish brain develop to facilitate precisely targeted hunting behaviour. Together this work contributes to our understanding of both normal neural development and the etiology of neurodevelopmental disorders.

Rapid predator learning in a developing vertebrate brain

COSYNE 2025

Assessing the EEG reactivity to photic stimulation in the sedated epileptic brain

FENS Forum 2024

Closed-loop control of quadrupedal mouse locomotion over uneven terrain: A neuromechanical modeling study

FENS Forum 2024

The differential role of the subiculum > hypothalamic paths in diverse contextual fear responses to a live predator and physically harmful events

FENS Forum 2024

Distributed encoding of motor commands mediates response to environmental confinement and escape from predators

FENS Forum 2024

Integrated Ayurveda and yoga intervention for enhancing functionality and ambulation in patients with muscular dystrophies: A single-arm pilot feasibility trial

FENS Forum 2024

Magnetic detection electrical impedance tomography (MDEIT) for non-invasive neural imaging

FENS Forum 2024

PIANP/LEDA-1 influences adult hippocampal neurogenesis and cerebellar morphology

FENS Forum 2024

A quadrupedal neurorobotic model to study the role of long propriospinal neurons before and after spinal cord injury

FENS Forum 2024

Several phytocannabinoids induce sedation in rats

FENS Forum 2024

Shining light on compound action potential recording and electrical impedance tomography for the closed-loop control of bioelectronic devices

FENS Forum 2024