In the Hartveit-Veruki group (Retinal Microcircuits) at the Department of Biomedicine, Faculty of Medicine, a full-time (100 %) position as Postdoctoral Research Fellow is available for a period of three (3) years. The position is linked to the consortium project ”Understanding plasticity and neural circuit dynamics in the brain” (TMS Brain Research Initiative), financed by the "Trond Mohn Stiftelse" (TMS), the University of Bergen, and the Norwegian University of Science and Technology (NTNU). Expected starting date is negotiable, but preferably by end of 2023 / beginning of 2024. The main objective of the TMS Brain Research Initiative is to identify core principles of plasticity and neural circuit dynamics in the brain. The project is hosted by the University of Bergen (UiB) at the Mohn Research Centre for the Brain and organized as a consortium collaboration between the Department of Biomedicine at UiB and the Kavli Institute for Systems Neuroscience at the Norwegian University of Science and Technology (NTNU). Through the centre's activities, the Postdoc will interact with a multidisciplinary team covering broad areas of neuroscience and gain experience in scientific presentation and discussion. The project focuses on investigating plasticity of neuronal microcircuits involving amacrine cells in the mammalian retina, including functions of ion channels and chemical and electrical synapses. Primary methods include patch-clamp electrophysiology, two-photon microscopy, and two-photon FLIM-FRET of intracellular signaling. The experimental work will be performed with the mammalian retina as the model system. Your application must be submitted via the JobbNorge website (deadline Oct 20): (https://www.jobbnorge.no/en/available-jobs/job/249982/postdoctoral-research-fellow-in-neuroscience).

A postdoctoral position is available immediately in the lab of Dr. Paul Shaw in the Neuroscience Department at Washington University School of Medicine in St. Louis to study the molecular and cellular bases for sleep regulation, plasticity and memory consolidation in the fruit fly Drosophila melanogaster. Successful candidates will have the opportunity to learn and apply molecular, genetic, physiological, and behavioral tools to study mechanisms by which sleep might influence plasticity. Qualified applicants are expected to hold a recent doctoral degree in the biological sciences, or in related disciplines. Prior experience in working with flies and broad understanding of genetic principles are highly preferred. Highly competitive salary and benefits are available and will commensurate with experience. Washington University School of Medicine offers a highly collaborative, top-notch training and research environment in neuroscience and the biomedical sciences. Wash U’s community is a very active and highly regarded neuroscience community, and is an excellent training environment for postdoctoral fellows. Interested candidates should email their curriculum vitae, a letter of interest outlining experience and research goals, and the names and contact information of at least three references to shawp@wustl.edu EOE Washington University is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity or expression, national origin, genetic information, disability, or protected veteran status.

TL;DR: If you liked our last NeurIPS paper https://www.biorxiv.org/content/10.1101/2020.10.24.353409v1 and you think you can contribute and imagine, oh, the places we’ll go from here,...from one of the most loivable cities in the world, … DO apply. --- We are looking for at least two scientists to join the vogelslab.org as postdocs at the @ISTAustria near Vienna. The successful candidate will join an ongoing ERC funded project to discover families of spike-induced synaptic plasticity rules by way of numerical derivation. Together, we will define and explore search spaces of biologically plausible plasticity rules expressed e.g., as polynomial expansions or multi layer perceptrons. We aim to compare the results to various experimental and theoretical data sets, including functional spiking network models, human stem-cell derived neural network cultures, in vitro and in vivo experimental data. We are looking for someone who can expand and develop our multipurpose modular library dedicated to optimization of non-differentiable systems. Due to the modularity of the library, the candidate will have extensive freedom regarding which optimization techniques to use and what to learn in which systems, but being a team player will be a crucial skill. Depending on your (flexible, possibly immediate) starting date we can offer up to 4-year contracts with competitive salaries, benefits, vacation time and ample budget for materials and travel in a tranquil and inspiring environment. The Vogelslab, and the IST Austria is located in Klosterneuburg, a historic city northwest of Vienna. The campus is located in the middle of the beautiful landscape of the Vienna Woods, 30 minutes from downtown Vienna, the capital of Austria that consistently scores in the top cities of the world for its high standard of living. If you are interested, send an email with your application to Jessica . deAntonio [at] ist.ac.at. Your application should include your CV, your most relevant publication, contact information for 2 or more references, and a cover letter with - a short description of you & your career - a brief discussion of what you think is the greatest weakness of the above mentioned NeurIPS paper (and maybe how you would go about fixing it). We are looking to build a diverse and interesting environment, so if you bring any qualities that make our lab (and computational neuroscience at large) more diverse than it is right now, please consider applying. We will begin to evaluate applications on the 31st of April and aim to get back to you with a decision within 5 weeks of your application.

The Neuroplasticity & Development Lab investigates the contributions of nature and nurture to human cognition. Areas of interest include the origins of conceptual representations, the contribution of linguistic and sensory experience to knowledge and the neurocognitive basis of cultural skills (e.g., reading, programming). We use functional magnetic resonance imaging (fMRI), transcranial magnetic stimulation (TMS) and behavioral measures to investigate these questions. One line of research in the lab compares the minds and brains of populations with different visual experiences e.g., congenitally blind, late blind and sighted individuals. Working with people who are blind enables disentangling the contributions of sensory and linguistic experience to conceptual representations. We investigate visual cortex plasticity in blindness as a window into the mechanisms and timing of neural specialization in humans.

The Department of Neurophysiology, Donders Centre for Neuroscience is looking for a PhD candidate to discover targets and pathways in molecular networks. You will investigate transcriptomic and proteomic networks in neurons, and how these networks relate to experience-dependent plasticity, i.e. the changes in neuronal and network structure upon sensory input. This includes developing statistical methods for molecular target identification, and comparison of connectivity in molecular networks to connectivity in cellular networks. For more information see: https://www.ru.nl/werken-bij/vacature/details-vacature/?recid=1129025 For more information about SmartNets: https://www.smartnets-etn.eu/

The successful applicant will build a simulation model of the DG-CA3-CA1 hippocampal network and use it to assess the role of dendritic nonlinearities on both the memory capacity and the structure of the resulting networks, following contextual and spatial learning. Specifically, the successful applicant will build simplified integrate-and-fire circuits of the DG, CA3 and CA1 hippocampal sub-regions, interconnected in one network and equipped with known plasticity rules such as LTP/LTD, homeostatic plasticity and plasticity of neuronal excitability. The basic biophysical properties of all models will be validated against existing experimental data provided by the literature, ongoing collaborations (e.g. Attila Losonczy, Columbia University) and partners of this ETN (secondment Fleur Zeldenrust and Tansu Celikel). The hippocampal circuit model will be trained with two integrative tasks: contextual learning and spatial learning. The properties of the resulting engram (e.g. neuronal population size during recall, somatic and dendritic excitability, dendritic spiking probability, synaptic weight changes, spatial distribution of modified synapses, sparsity, place cell number, stability, sensitivity etc) will be assessed. By systematically manipulating dendritic biophysics (i.e. ionic and synaptic conductances) so as to generate linear, sublinear and supralinear dendrites, as well as the various synaptic/intrinsic plasticity rules, we will assess the effect of these manipulations on a) learning capacity, b) the emergent connectivity of the network post-learning, c) engram size/connectivity/recall stability etc. For more information see: https://www.smartnets-etn.eu/role-of-dendritic-nonlinearities-in-hippocampal-network-properties-after-contextual-and-spatial-learning/

Searching for Two postdoctoral fellows / PhD students with expertise/interest in Bioinformatics (in particular single cell transcriptomics) & Experimental genetics (rodents or fly) Projects involve investigation of the origins of cellular diversity in the developing mammalian cortex and input/environment-dependent plasticity Starting dates are flexible, and funding secured by the ERC and Swiss NSF. Stipends are according to University of Geneva scales (e.g. postdoctoral starting salary 81'000 CHF pa).

The Perception and Plasticity Group of Caspar Schwiedrzik at the DPZ is looking for an outstanding postdoc interested in studying the neural basis of high-dimensional category learning in vision. The project investigates neural mechanisms of category learning at the level of circuits and single cells, utilizing electrophysiology, functional magnetic resonance imaging, behavioral testing in humans and non-human primates, and computational modeling. It is funded by an ERC Consolidator Grant (Acronym DimLearn; “Flexible Dimensionality of Representational Spaces in Category Learning”). The postdoc’s project will focus on investigating the neural basis of visual category learning in macaque monkeys combining chronic multi-electrode electrophysiological recordings and electrical microstimulation. In addition, the postdoc will have the opportunity to cooperate with other lab members on parallel computational investigations using artificial neural networks as well as comparative research exploring the same questions in humans. The postdoc will play a key role in our research efforts in this area. The lab is located at Ruhr-University Bochum and the German Primate Center in Göttingen. At both locations, the lab is embedded into interdisciplinary research centers with international faculty and students pursuing cutting-edge research in cognitive and computational neuroscience. The main site for this part of the project will be Göttingen. The postdoc will have access to state-of-the-art electrophysiology, an imaging center with a dedicated 3T research scanner, and behavioral setups. The project will be conducted in close collaboration with the labs of Fabian Sinz, Alexander Gail, and Igor Kagan.

Connectomic traces of Hebbian plasticity in the entorhinalhippocampal system

Distinct synaptic plasticity rules operate across dendritic compartments in vivo during learning

Behavioral time scale synaptic plasticity underlies CA1 place fields

Sleep is an active state critical for processing emotional memories encoded during waking in both humans and animals. There is a remarkable overlap between the brain structures and circuits active during sleep, particularly rapid eye-movement (REM) sleep, and the those encoding emotions. Accordingly, disruptions in sleep quality or quantity, including REM sleep, are often associated with, and precede the onset of, nearly all affective psychiatric and mood disorders. In this context, a major biomedical challenge is to better understand the underlying mechanisms of the relationship between (REM) sleep and emotion encoding to improve treatments for mental health. This lecture will summarize our investigation of the cellular and circuit mechanisms underlying sleep architecture, sleep oscillations, and local brain dynamics across sleep-wake states using electrophysiological recordings combined with single-cell calcium imaging or optogenetics. The presentation will detail the discovery of a 'somato-dendritic decoupling'in prefrontal cortex pyramidal neurons underlying REM sleep-dependent stabilization of optimal emotional memory traces. This decoupling reflects a tonic inhibition at the somas of pyramidal cells, occurring simultaneously with a selective disinhibition of their dendritic arbors selectively during REM sleep. Recent findings on REM sleep-dependent subcortical inputs and neuromodulation of this decoupling will be discussed in the context of synaptic plasticity and the optimization of emotional responses in the maintenance of mental health.

Efficient cognition requires flexible interactions between distributed neural networks in the human brain. These networks adapt to challenges by flexibly recruiting different regions and connections. In this talk, I will discuss how we study functional network plasticity and reorganization with combined neurostimulation and neuroimaging across the adult life span. I will argue that short-term plasticity enables flexible adaptation to challenges, via functional reorganization. My key hypothesis is that disruption of higher-level cognitive functions such as language can be compensated for by the recruitment of domain-general networks in our brain. Examples from healthy young brains illustrate how neurostimulation can be used to temporarily interfere with efficient processing, probing short-term network plasticity at the systems level. Examples from people with dyslexia help to better understand network disorders in the language domain and outline the potential of facilitatory neurostimulation for treatment. I will also discuss examples from aging brains where plasticity helps to compensate for loss of function. Finally, examples from lesioned brains after stroke provide insight into the brain’s potential for long-term reorganization and recovery of function. Collectively, these results challenge the view of a modular organization of the human brain and argue for a flexible redistribution of function via systems plasticity.

Fear learning induces synaptic potentiation between engram neurons in the rat lateral amygdala. This study by Marios Abatis et al. demonstrates how fear conditioning strengthens synaptic connections between engram cells in the lateral amygdala, revealed through optogenetic identification of neuronal ensembles and electrophysiological measurements. The work provides crucial insights into memory formation mechanisms at the synaptic level, with implications for understanding anxiety disorders and developing targeted interventions. Presented by Dr. Kenneth Hayworth, this journal club will explore the paper's methodology linking engram cell reactivation with synaptic plasticity measurements, and discuss implications for memory decoding research.

Behavior and cognition depend on the integrated action of neural structures and populations distributed throughout the brain. We recently developed a set of molecular imaging tools that enable multiregional processing and plasticity in neural networks to be studied at a brain-wide scale in rodents and nonhuman primates. Here we will describe how a novel genetically encoded activity reporter enables information flow in virally labeled neural circuitry to be monitored by fMRI. Using the reporter to perform functional imaging of synaptically defined neural populations in the rat somatosensory system, we show how activity is transformed within brain regions to yield characteristics specific to distinct output projections. We also show how this approach enables regional activity to be modeled in terms of inputs, in a paradigm that we are extending to address circuit-level origins of functional specialization in marmoset brains. In the second part of the talk, we will discuss how another genetic tool for MRI enables systematic studies of the relationship between anatomical and functional connectivity in the mouse brain. We show that variations in physical and functional connectivity can be dissociated both across individual subjects and over experience. We also use the tool to examine brain-wide relationships between plasticity and activity during an opioid treatment. This work demonstrates the possibility of studying diverse brain-wide processing phenomena using molecular neuroimaging.

This webinar brings together three leaders in theoretical and computational neuroscience—Larry Abbott, Haim Sompolinsky, and Terry Sejnowski—to discuss how neural circuits generate fundamental aspects of the mind. Abbott illustrates mechanisms in electric fish that differentiate self-generated electric signals from external sensory cues, showing how predictive plasticity and two-stage signal cancellation mediate a sense of self. Sompolinsky explores attractor networks, revealing how discrete and continuous attractors can stabilize activity patterns, enable working memory, and incorporate chaotic dynamics underlying spontaneous behaviors. He further highlights the concept of object manifolds in high-level sensory representations and raises open questions on integrating connectomics with theoretical frameworks. Sejnowski bridges these motifs with modern artificial intelligence, demonstrating how large-scale neural networks capture language structures through distributed representations that parallel biological coding. Together, their presentations emphasize the synergy between empirical data, computational modeling, and connectomics in explaining the neural basis of cognition—offering insights into perception, memory, language, and the emergence of mind-like processes.

In this webinar on spatial navigation circuits, three researchers—Ann Hermundstad, Ila Fiete, and Barbara Webb—discussed how diverse species solve navigation problems using specialized yet evolutionarily conserved brain structures. Hermundstad illustrated the fruit fly’s central complex, focusing on how hardwired circuit motifs (e.g., sinusoidal steering curves) enable rapid, flexible learning of goal-directed navigation. This framework combines internal heading representations with modifiable goal signals, leveraging activity-dependent plasticity to adapt to new environments. Fiete explored the mammalian head-direction system, demonstrating how population recordings reveal a one-dimensional ring attractor underlying continuous integration of angular velocity. She showed that key theoretical predictions—low-dimensional manifold structure, isometry, uniform stability—are experimentally validated, underscoring parallels to insect circuits. Finally, Webb described honeybee navigation, featuring path integration, vector memories, route optimization, and the famous waggle dance. She proposed that allocentric velocity signals and vector manipulation within the central complex can encode and transmit distances and directions, enabling both sophisticated foraging and inter-bee communication via dance-based cues.

This webinar on learning and memory features three experts—Nicolas Brunel, Ashok Litwin-Kumar, and Julijana Gjorgieva—who present theoretical and computational approaches to understanding how neural circuits acquire and store information across different scales. Brunel discusses calcium-based plasticity and how standard “Hebbian-like” plasticity rules inferred from in vitro or in vivo datasets constrain synaptic dynamics, aligning with classical observations (e.g., STDP) and explaining how synaptic connectivity shapes memory. Litwin-Kumar explores insights from the fruit fly connectome, emphasizing how the mushroom body—a key site for associative learning—implements a high-dimensional, random representation of sensory features. Convergent dopaminergic inputs gate plasticity, reflecting a high-dimensional “critic” that refines behavior. Feedback loops within the mushroom body further reveal sophisticated interactions between learning signals and action selection. Gjorgieva examines how activity-dependent plasticity rules shape circuitry from the subcellular (e.g., synaptic clustering on dendrites) to the cortical network level. She demonstrates how spontaneous activity during development, Hebbian competition, and inhibitory-excitatory balance collectively establish connectivity motifs responsible for key computations such as response normalization.

Recent research on the neural crest has revealed the multipotency and plasticity of nerve-associated Schwann cell precursors, which can differentiate into diverse cell types, including parasympathetic neurons, neuroendocrine cells, and mesenchymal stem cells. These findings challenge the traditional view of peripheral nerves, highlighting their role as niches for migratory progenitor cells that contribute to tissue formation and regeneration.

How do cortical circuits acquire new dynamics that drive learned movements? This webinar will focus on mouse premotor cortex in relation to learned lick-timing and explore high-density electrophysiology using our silicon neural probes alongside region and cell-type-specific acute genetic manipulations of proteins required for synaptic plasticity.

The visual system faces a dual challenge. On the one hand, features of the natural visual environment should be stably processed - irrespective of ongoing wiring changes, representational drift, and behavior. On the other hand, eye, head, and body motion require a robust integration of pose and gaze shifts in visual computations for a stable perception of the world. We address these dimensions of stable visual processing by studying the circuit mechanism of long-term representational stability, focusing on the role of plasticity, network structure, experience, and behavioral state while recording large-scale neuronal activity with miniature two-photon microscopy.

Executive functions are cognitive processes that allow us to plan, monitor and execute our goals. Using fMRI, we investigated how early deafness influences crossmodal plasticity and the organisation of executive functions in the adult human brain. Results from a range of visual executive function tasks (working memory, task switching, planning, inhibition) show that deaf individuals specifically recruit superior temporal “auditory” regions during task switching. Neural activity in auditory regions predicts behavioural performance during task switching in deaf individuals, highlighting the functional relevance of the observed cortical reorganisation. Furthermore, language grammatical skills were correlated with the level of activation and functional connectivity of fronto-parietal networks. Together, these findings show the interplay between sensory and language experience in the organisation of executive processing in the brain.

Nonstationarity presents a variety of challenges for machine learning systems. One surprising pathology which can arise in nonstationary learning problems is plasticity loss, whereby making progress on new learning objectives becomes more difficult as training progresses. Networks which are unable to adapt in response to changes in their environment experience plateaus or even declines in performance in highly non-stationary domains such as reinforcement learning, where the learner must quickly adapt to new information even after hundreds of millions of optimization steps. The loss of plasticity manifests in a cluster of related empirical phenomena which have been identified by a number of recent works, including the primacy bias, implicit under-parameterization, rank collapse, and capacity loss. While this phenomenon is widely observed, it is still not fully understood. This talk will present exciting recent results which shed light on the mechanisms driving the loss of plasticity in a variety of learning problems and survey methods to maintain network plasticity in non-stationary tasks, with a particular focus on deep reinforcement learning.

The neurovascular unit (NVU) consists of cerebral blood vessels, neurons, astrocytes, microglia, and pericytes. It plays a vital role in regulating blood flow and ensuring the proper functioning of neural circuits. Among other, this is made possible by the blood-brain barrier (BBB), which acts as both a physical and functional barrier. Previous studies have shown that dysfunction of the BBB is common in most neurological disorders and is associated with neural dysfunction. Our studies have demonstrated that BBB dysfunction results in the transformation of astrocytes through transforming growth factor beta (TGFβ) signaling. This leads to activation of the innate neuroinflammatory system, changes in the extracellular matrix, and pathological plasticity. These changes ultimately result in dysfunction of the cortical circuit, lower seizure threshold, and spontaneous seizures. Blocking TGFβ signaling and its associated pro-inflammatory pathway can prevent this cascade of events, reduces neuroinflammation, repairs BBB dysfunction, and prevents post-injury epilepsy, as shown in experimental rodents. To further understand and assess BBB integrity in human epilepsy, we developed a novel imaging technique that quantitatively measures BBB permeability. Our findings have confirmed that BBB dysfunction is common in patients with drug-resistant epilepsy and can assist in identifying the ictal-onset zone prior to surgery. Current clinical studies are ongoing to explore the potential of targeting BBB dysfunction as a novel treatment approach and investigate its role in drug resistance, the spread of seizures, and comorbidities associated with epilepsy.

Recent studies identified memory engram neurons, a neuronal population that is recruited by initial learning and is reactivated during memory recall.  Memory engram neurons are connected to one another through memory engram synapses in a distributed network of brain areas.  Our central hypothesis is that an associative memory is encoded and consolidated by selective strengthening of engram synapses.  We are testing this hypothesis, using a combination of engram cell labeling, optogenetic/chemogenetic, electrophysiological, and virus tracing approaches in rodent models of contextual fear conditioning.  In this talk, I will discuss our findings on how synaptic plasticity in memory engram synapses contributes to the acquisition and consolidation of contextual fear memory in a distributed network of the amygdala, hippocampus, and neocortex.

The use of molecular imaging, particularly PET and SPECT, has significantly transformed the treatment of schizophrenia with antipsychotic drugs since the late 1980s. It has offered insights into the links between drug target engagement, clinical effects, and side effects. A therapeutic window for receptor occupancy is established for antipsychotics, yet there is a divergence of opinions regarding the importance of blood levels, with many downplaying their significance. As a result, the role of therapeutic drug monitoring (TDM) as a personalized therapy tool is often underrated. Since molecular imaging of antipsychotics has focused almost entirely on D2-like dopamine receptors and their potential to control positive symptoms, negative symptoms and cognitive deficits are hardly or not at all investigated. Alternative methods have been introduced, i.e. to investigate the correlation between approximated receptor occupancies from blood levels and cognitive measures. Within the domain of antidepressants, and specifically regarding ketamine's efficacy in depression treatment, there is limited comprehension of the association between plasma concentrations and target engagement. The measurement of AMPA receptors in the human brain has added a new level of comprehension regarding ketamine's antidepressant effects. To ensure precise prescription of psychotropic drugs, it is vital to have a nuanced understanding of how molecular and clinical effects interact. Clinician scientists are assigned with the task of integrating these indispensable pharmacological insights into practice, thereby ensuring a rational and effective approach to the treatment of mental health disorders, signaling a new era of personalized drug therapy mechanisms that promote neuronal plasticity not only under pathological conditions, but also in the healthy aging brain.

Humans and other animals evolved in habitats fraught with a range of environmental challenges to their bodies and brains. Accordingly, cells and organ systems possess adaptive stress-responsive signaling pathways that enable them to not only withstand environmental challenges, but also to prepare for future challenges and function more efficiently. These phylogenetically conserved processes are the foundation of the hormesis principle in which repeated exposures to low to moderate amounts of an environmental challenge improve cellular and organismal fitness. Here I describe cellular and molecular mechanisms by which cells in the brain and body respond to intermittent fasting and exercise in ways that enhance performance and counteract aging and disease processes. Switching back and forth between adaptive stress response (during fasting and exercise) and growth and plasticity (eating, resting, sleeping) modes enhances the performance and resilience of various organ systems. While pharmacological interventions that engage a particular hormetic mechanism are being developed, it seems unlikely that any will prove superior to fasting and exercise.

Sleep strongly affects synaptic strength, making it critical for cognition, especially learning and memory formation. Whether and how sleep deprivation modulates human brain physiology and cognition is poorly understood. Here we examined how overnight sleep deprivation vs overnight sufficient sleep affects (a) cortical excitability, measured by transcranial magnetic stimulation, (b) inducibility of long-term potentiation (LTP)- and long-term depression (LTD)-like plasticity via transcranial direct current stimulation (tDCS), and (c) learning, memory, and attention. We found that sleep deprivation increases cortical excitability due to enhanced glutamate-related cortical facilitation and decreases and/or reverses GABAergic cortical inhibition. Furthermore, tDCS-induced LTP-like plasticity (anodal) abolishes while the inhibitory LTD-like plasticity (cathodal) converts to excitatory LTP-like plasticity under sleep deprivation. This is associated with increased EEG theta oscillations due to sleep pressure. Motor learning, behavioral counterparts of plasticity, and working memory and attention, which rely on cortical excitability, are also impaired during sleep deprivation. Our study indicates that upscaled brain excitability and altered plasticity, due to sleep deprivation, are associated with impaired cognitive performance. Besides showing how brain physiology and cognition undergo changes (from neurophysiology to higher-order cognition) under sleep pressure, the findings have implications for variability and optimal application of noninvasive brain stimulation.

Despite her still poor visual acuity and minimal visual experience, a 2-3 month old baby will reliably respond to facial expressions, smiling back at her caretaker or older sibling. But what if that same baby had been deprived of her early visual experience? Will she be able to appropriately respond to seemingly mundane interactions, such as a peer’s facial expression, if she begins seeing at the age of 10? My work is part of Project Prakash, a dual humanitarian/scientific mission to identify and treat curably blind children in India and then study how their brain learns to make sense of the visual world when their visual journey begins late in life. In my talk, I will give a brief overview of Project Prakash, and present findings from one of my primary lines of research: plasticity of face perception with late sight onset. Specifically, I will discuss a mixed methods effort to probe and explain the differential windows of plasticity that we find across different aspects of distributed face recognition, from distinguishing a face from a nonface early in the developmental trajectory, to recognizing facial expressions, identifying individuals, and even identifying one’s own caretaker. I will draw connections between our empirical findings and our recent theoretical work hypothesizing that children with late sight onset may suffer persistent face identification difficulties because of the unusual acuity progression they experience relative to typically developing infants. Finally, time permitting, I will point to potential implications of our findings in supporting newly-sighted children as they transition back into society and school, given that their needs and possibilities significantly change upon the introduction of vision into their lives.

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.

During development, the complex neuronal circuitry of the brain arises from limited information contained in the genome. After the genetic code instructs the birth of neurons, the emergence of brain regions, and the formation of axon tracts, it is believed that neuronal activity plays a critical role in shaping circuits for behavior. Current AI technologies are modeled after the same principle: connections in an initial weight matrix are pruned and strengthened by activity-dependent signals until the network can sufficiently generalize a set of inputs into outputs. Here, we challenge these learning-dominated assumptions by quantifying the contribution of neuronal activity to the development of visually guided swimming behavior in larval zebrafish. Intriguingly, dark-rearing zebrafish revealed that visual experience has no effect on the emergence of the optomotor response (OMR). We then raised animals under conditions where neuronal activity was pharmacologically silenced from organogenesis onward using the sodium-channel blocker tricaine. Strikingly, after washout of the anesthetic, animals performed swim bouts and responded to visual stimuli with 75% accuracy in the OMR paradigm. After shorter periods of silenced activity OMR performance stayed above 90% accuracy, calling into question the importance and impact of classical critical periods for visual development. Detailed quantification of the emergence of functional circuit properties by brain-wide imaging experiments confirmed that neuronal circuits came ‘online’ fully tuned and without the requirement for activity-dependent plasticity. Thus, contrary to what you learned on your mother's knee, complex sensory guided behaviors can be wired up innately by activity-independent developmental mechanisms.

The hippocampus is a key player in learning and memory. Research into this brain structure has long emphasized its plasticity and flexibility, though recent reports have come to appreciate its remarkably stable firing patterns. How novel information incorporates itself into networks that maintain their ongoing dynamics remains an open question, largely due to a lack of experimental access points into network stability. Development may provide one such access point. To explore this hypothesis, we birthdated CA1 pyramidal neurons using in-utero electroporation and examined their functional features in freely moving, adult mice. We show that CA1 pyramidal neurons of the same embryonic birthdate exhibit prominent cofiring across different brain states, including behavior in the form of overlapping place fields. Spatial representations remapped across different environments in a manner that preserves the biased correlation patterns between same birthdate neurons. These features of CA1 activity could partially be explained by structured connectivity between pyramidal cells and local interneurons. These observations suggest the existence of developmentally installed circuit motifs that impose powerful constraints on the statistics of hippocampal output.

Neurons in the rodent hippocampus appear to encode the position of the animal in physical space during movement. Individual ``place cells'' fire in restricted sub-regions of an environment, a feature often taken as evidence that the hippocampus encodes a map of space that subserves navigation. But these same neurons exhibit complex responses to many other variables that defy explanation by position alone, and the hippocampus is known to be more broadly critical for memory formation. Here we elaborate and test a theory of hippocampal coding which produces place cells as a general consequence of efficient memory coding. We constructed neural networks that actively exploit the correlations between memories in order to learn compressed representations of experience. Place cells readily emerged in the trained model, due to the correlations in sensory input between experiences at nearby locations. Notably, these properties were highly sensitive to the compressibility of the sensory environment, with place field size and population coding level in dynamic opposition to optimally encode the correlations between experiences. The effects of learning were also strongly biphasic: nearby locations are represented more similarly following training, while locations with intermediate similarity become increasingly decorrelated, both distance-dependent effects that scaled with the compressibility of the input features. Using virtual reality and 2-photon functional calcium imaging in head-fixed mice, we recorded the simultaneous activity of thousands of hippocampal neurons during virtual exploration to test these predictions. Varying the compressibility of sensory information in the environment produced systematic changes in place cell properties that reflected the changing input statistics, consistent with the theory. We similarly identified representational plasticity during learning, which produced a distance-dependent exchange between compression and pattern separation. These results motivate a more domain-general interpretation of hippocampal computation, one that is naturally compatible with earlier theories on the circuit's importance for episodic memory formation. Work done in collaboration with James Priestley, Lorenzo Posani, Marcus Benna, Attila Losonczy.

In the central nervous system, neuronal activity is a critical regulator of development and plasticity. Activity-dependent proliferation of healthy glial progenitors, oligodendrocyte precursor cells (OPCs), and the consequent generation of new oligodendrocytes contributes to adaptive myelination. This plasticity of myelin tunes neural circuit function and contributes to healthy cognition. The robust mitogenic effect of neuronal activity on normal oligodendroglial precursor cells, a putative cellular origin for many forms of glioma, suggests that dysregulated or “hijacked” mechanisms of myelin plasticity might similarly promote malignant cell proliferation in this devastating group of brain cancers. Indeed, neuronal activity promotes progression of both high-grade and low-grade glioma subtypes in preclinical models. Crucial mechanisms mediating activity-regulated glioma growth include paracrine secretion of BDNF and the synaptic protein neuroligin-3 (NLGN3). NLGN3 induces multiple oncogenic signaling pathways in the cancer cell, and also promotes glutamatergic synapse formation between neurons and glioma cells. Glioma cells integrate into neural circuits synaptically through neuron-to-glioma synapses, and electrically through potassium-evoked currents that are amplified through gap-junctional coupling between tumor cells This synaptic and electrical integration of glioma into neural circuits is central to tumor progression in preclinical models. Thus, neuron-glial interactions not only modulate neural circuit structure and function in the healthy brain, but paracrine and synaptic neuron-glioma interactions also play important roles in the pathogenesis of glial cancers. The mechanistic parallels between normal and malignant neuron-glial interactions underscores the extent to which mechanisms of neurodevelopment and plasticity are subverted by malignant gliomas, and the importance of understanding the neuroscience of cancer.

Many animals live in complex social groups. To survive, it is essential to know who to avoid and who to interact. Although naïve mice are naturally attracted to any adult conspecifics, a single defeat experience could elicit social avoidance towards the aggressor for days. The neural mechanisms underlying the behavior switch from social approach to social avoidance remains incompletely understood. Here, we identify oxytocin neurons in the retrochiasmatic supraoptic nucleus (SOROXT) and oxytocin receptor (OXTR) expressing cells in the anterior subdivision of ventromedial hypothalamus, ventrolateral part (aVMHvlOXTR) as a key circuit motif for defeat-induced social avoidance learning. After defeat, aVMHvlOXTR cells drastically increase their responses to aggressor cues. This response change is functionally important as optogenetic activation of aVMHvlOXTR cells elicits time-locked social avoidance towards a benign social target whereas inactivating the cells suppresses defeat-induced social avoidance. Furthermore, OXTR in the aVMHvl is itself essential for the behavior change. Knocking out OXTR in the aVMHvl or antagonizing the receptor during defeat, but not during post-defeat social interaction, impairs defeat-induced social avoidance. aVMHvlOXTR receives its private supply of oxytocin from SOROXT cells. SOROXT is highly activated by the noxious somatosensory inputs associated with defeat. Oxytocin released from SOROXT depolarizes aVMHvlOXTR cells and facilitates their synaptic potentiation, and hence, increases aVMHvlOXTR cell responses to aggressor cues. Ablating SOROXT cells impairs defeat-induced social avoidance learning whereas activating the cells promotes social avoidance after a subthreshold defeat experience. Altogether, our study reveals an essential role of SOROXT-aVMHvlOXTR circuit in defeat-induced social learning and highlights the importance of hypothalamic oxytocin system in social ranking and its plasticity.

Synaptic plasticity is known to be a key player in the brain’s life-long learning abilities. However, due to experimental limitations, the nature of the local changes at individual synapses and their link with emerging network-level computations remain unclear. I will present a numerical, meta-learning approach to deduce plasticity rules from either neuronal activity data and/or prior knowledge about the network's computation. I will first show how to recover known rules, given a human-designed loss function in rate networks, or directly from data, using an adversarial approach. Then I will present how to scale-up this approach to recurrent spiking networks using simulation-based inference.

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.

Artificial neural networks simplify complex biological circuits into tractable models for computational exploration and experimentation. However, the simplification of artificial models also undermines their applicability to real brain dynamics. Typical efforts to address this mismatch add complexity to increasingly unwieldy models. Here, we take a different approach; by reducing the complexity of a biological cortical culture, we aim to distil the essential factors of neuronal dynamics and plasticity. We leverage recent advances in growing neurons from human induced pluripotent stem cells (hiPSCs) to analyse ex vivo cortical cultures with only two distinct excitatory and inhibitory neuron populations. Over 6 weeks of development, we record from thousands of neurons using high-density microelectrode arrays (HD-MEAs) that allow access to individual neurons and the broader population dynamics. We compare these dynamics to two-population artificial networks of single-compartment neurons with random sparse connections and show that they produce similar dynamics. Specifically, our model captures the firing and bursting statistics of the cultures. Moreover, tightly integrating models and cultures allows us to evaluate the impact of changing architectures over weeks of development, with and without external stimuli. Broadly, the use of simplified cortical cultures enables us to use the repertoire of theoretical neuroscience techniques established over the past decades on artificial network models. Our approach of deriving neural networks from human cells also allows us, for the first time, to directly compare neural dynamics of disease and control. We found that cultures e.g. from epilepsy patients tended to have increasingly more avalanches of synchronous activity over weeks of development, in contrast to the control cultures. Next, we will test possible interventions, in silico and in vitro, in a drive for personalised approaches to medical care. This work starts bridging an important theoretical-experimental neuroscience gap for advancing our understanding of mammalian neuron dynamics.

Memory consolidation is the process by which recent experiences are assimilated into long-term memory. In animals, this process requires the offline replay of sequences observed during online exploration in the hippocampus. Recent experimental work has found that salient but task-irrelevant stimuli are systematically excluded from these replay epochs, suggesting that replay samples from an abstracted model of the world, rather than verbatim previous experiences. We find that this phenomenon can be explained parsimoniously and biologically plausibly by a Hebbian spike time-dependent plasticity rule at inhibitory synapses. Using spiking networks at three levels of abstraction–leaky integrate-and-fire, biophysically detailed, and abstract binary–we show that this rule enables efficient inference of a model of the structure of the world. While plasticity has previously mainly been studied at excitatory synapses, we find that plasticity at excitatory synapses alone is insufficient to accomplish this type of structural learning. We present theoretical results in a simplified model showing that in the presence of Hebbian excitatory and inhibitory plasticity, the replayed sequences form a statistical estimator of a latent sequence, which converges asymptotically to the ground truth. Our work outlines a direct link between the synaptic and cognitive levels of memory consolidation, and highlights a potential conceptually distinct role for inhibition in computing with SNNs.

A central problem in biological learning is how information about the outcome of a decision or behavior can be used to reliably guide learning across distributed neural circuits while obeying biological constraints. This “credit assignment” problem is commonly solved in artificial neural networks through supervised gradient descent and the backpropagation algorithm. In contrast, biological learning is typically modelled using unsupervised Hebbian learning rules. While these rules only use local information to update synaptic weights, and are sometimes combined with weight constraints to reflect a diversity of excitatory (only positive weights) and inhibitory (only negative weights) cell types, they do not prescribe a clear mechanism for how to coordinate learning across multiple layers and propagate error information accurately across the network. In recent years, several groups have drawn inspiration from the known dendritic non-linearities of pyramidal neurons to propose new learning rules and network architectures that enable biologically plausible multi-layer learning by processing error information in segregated dendrites. Meanwhile, recent experimental results from the hippocampus have revealed a new form of plasticity—Behavioral Timescale Synaptic Plasticity (BTSP)—in which large dendritic depolarizations rapidly reshape synaptic weights and stimulus selectivity with as little as a single stimulus presentation (“one-shot learning”). Here we explore the implications of this new learning rule through a biologically plausible implementation in a rate neuron network. We demonstrate that regulation of dendritic spiking and BTSP by top-down feedback signals can effectively coordinate plasticity across multiple network layers in a simple pattern recognition task. By analyzing hidden feature representations and weight trajectories during learning, we show the differences between networks trained with standard backpropagation, Hebbian learning rules, and BTSP.

Memory is a key component of biological neural systems that enables the retention of information over a huge range of temporal scales, ranging from hundreds of milliseconds up to years. While Hebbian plasticity is believed to play a pivotal role in biological memory, it has so far been analyzed mostly in the context of pattern completion and unsupervised learning. Here, we propose that Hebbian plasticity is fundamental for computations in biological neural systems. We introduce a novel spiking neural network (SNN) architecture that is enriched by Hebbian synaptic plasticity. We experimentally show that our memory-equipped SNN model outperforms state-of-the-art deep learning mechanisms in a sequential pattern-memorization task, as well as demonstrate superior out-of-distribution generalization capabilities compared to these models. We further show that our model can be successfully applied to one-shot learning and classification of handwritten characters, improving over the state-of-the-art SNN model. We also demonstrate the capability of our model to learn associations for audio to image synthesis from spoken and handwritten digits. Our SNN model further presents a novel solution to a variety of cognitive question answering tasks from a standard benchmark, achieving comparable performance to both memory-augmented ANN and SNN-based state-of-the-art solutions to this problem. Finally we demonstrate that our model is able to learn from rewards on an episodic reinforcement learning task and attain near-optimal strategy on a memory-based card game. Hence, our results show that Hebbian enrichment renders spiking neural networks surprisingly versatile in terms of their computational as well as learning capabilities. Since local Hebbian plasticity can easily be implemented in neuromorphic hardware, this also suggests that powerful cognitive neuromorphic systems can be build based on this principle.

Graphics processing units (GPUs) are widely available and have been used with great success to accelerate scientific computing in the last decade. These advances, however, are often not available to researchers interested in simulating spiking neural networks, but lacking the technical knowledge to write the necessary low-level code. Writing low-level code is not necessary when using the popular Brian simulator, which provides a framework to generate efficient CPU code from high-level model definitions in Python. Here, we present Brian2CUDA, an open-source software that extends the Brian simulator with a GPU backend. Our implementation generates efficient code for the numerical integration of neuronal states and for the propagation of synaptic events on GPUs, making use of their massively parallel arithmetic capabilities. We benchmark the performance improvements of our software for several model types and find that it can accelerate simulations by up to three orders of magnitude compared to Brian’s CPU backend. Currently, Brian2CUDA is the only package that supports Brian’s full feature set on GPUs, including arbitrary neuron and synapse models, plasticity rules, and heterogeneous delays. When comparing its performance with Brian2GeNN, another GPU-based backend for the Brian simulator with fewer features, we find that Brian2CUDA gives comparable speedups, while being typically slower for small and faster for large networks. By combining the flexibility of the Brian simulator with the simulation speed of GPUs, Brian2CUDA enables researchers to efficiently simulate spiking neural networks with minimal effort and thereby makes the advancements of GPU computing available to a larger audience of neuroscientists.

A long standing challenge in biological and artificial intelligence is to understand how new knowledge can be constructed from known building blocks in a way that is amenable for computation by neuronal circuits. Here we focus on the task of storage and recall of structured knowledge in long-term memory. Specifically, we ask how recurrent neuronal networks can store and retrieve multiple knowledge structures. We model each structure as a set of binary relations between events and attributes (attributes may represent e.g., temporal order, spatial location, role in semantic structure), and map each structure to a distributed neuronal activity pattern using a vector symbolic architecture (VSA) scheme. We then use associative memory plasticity rules to store the binarized patterns as fixed points in a recurrent network. By a combination of signal-to-noise analysis and numerical simulations, we demonstrate that our model allows for efficient storage of these knowledge structures, such that the memorized structures as well as their individual building blocks (e.g., events and attributes) can be subsequently retrieved from partial retrieving cues. We show that long-term memory of structured knowledge relies on a new principle of computation beyond the memory basins. Finally, we show that our model can be extended to store sequences of memories as single attractors.

The predictive map hypothesis provides a promising framework to model representations in the hippocampal formation. I will introduce a tractable implementation of a predictive map called the successor representation (SR), before presenting data showing that rats and humans display SR-like navigational choices on a novel open-field maze. Next, I will show how such a predictive map could be implemented using spatial representations found in the hippocampal formation, before finally presenting how such learning might be well approximated by phenomena that exist in the spatial memory system - namely spike-timing dependent plasticity and theta phase precession.