Synaptic Plasticity
synaptic plasticity
Prof Carmen Varela
We are opening two positions (one postdoc and one research assistant/post-bac) in the laboratory of Dr. Carmen Varela (Psychology Department, Florida Atlantic University) to investigate the spike dynamics and synaptic changes in thalamic cells contributing to sleep-dependent memory consolidation.
Prof Julijana Gjorgjieva
Many neural circuits shows correlated firing among neurons; these correlations have been shown to be important for the encoding and decoding of sensory information. While most work has addressed correlations between pairs of neurons, recently, an increasing number of experimental studies have characterized Higher-Order spiking Correlations (HOCs) in several brain areas. Theoretical work has also addressed the importance of higher-order correlations (HOCs) on the firing of a postsynaptic neuron on circuit function and coding, and on the synchronous firing and the distribution of activity in a neuronal pool. However, the functional significance of such HOC for synaptic plasticity remains poorly understood. The student will study the role of these HOCs in how they shape plasticity in different network architectures. Previously, we have proposed and analyzed a model in feedforward networks, where plasticity depends on spike triplets: sets of three spikes (triplets) are used instead of pairs to induce synaptic potentiation and depression. While important for the propagation of neural activity, feedforward circuits are unlike the recurrent structure of the neocortex. Therefore, we would like to understand how these HOC and learning rules shape network connectivity in recurrent networks. We propose a theoretical analysis involving the extension of Hawkes processes in mathematics to neural networks where different combinations of spikes (pairs and triplets) interact to drive plasticity. In addition to developing rigorous mathematical frameworks, this theory will enable us to relate measured correlations in the activity that drive plasticity to the selective potentiation and depression of specific connectivity motifs in real biological networks recorded experimentally (for e.g. synfire chains, propagating ensembles). Thus, we would be able to predict the possible network structures emerging based on different plasticity rules, which can be related to functional connectivity characterized through correlations measured in experimental data. The mathematical analysis will be accompanied with numerical simulations and data analysis from collaborating partners to test the theoretical predictions. For more information see: https://www.smartnets-etn.eu/how-higher-order-correlations-shape-network-structure/
Dr Panayiota Poirazi
The successful applicant will build a simulation model of the rodent visual cortex and use it to assess the role of dendritic nonlinearities on the connectivity and activity properties of the resulting memory engrams. Selected model predictions will be tested in headfixed behaving animals performing a visual discrimination task. For more information see: https://www.smartnets-etn.eu/role-of-dendritic-nonlinearities-in-v1-network-properties-after-visual-learning/
Prof. Eilif B. Muller
I'm happy to announce the Architectures of Biological Learning Lab is Hiring! I'm looking for exceptional candidates at the MSc, PhD or post-doc level to work on "Dendritic Algorithms for Perceptual Learning". The project will employ simulations of pyramidal neurons and plasticity, and deep convolutional networks to study representation learning in the neocortex. Prior experience with Python, NEURON, and/or PyTorch would be an asset. This project will be undertaken in collaboration with Profs. Yoshua Bengio (UdeM, Mila), Roberto Araya (UdeM, CRCHUSJ), and Blake Richards (McGill, Mila). Montreal, Canada is a thriving international hub for Artificial Intelligence and Neuroscience research, with a booming AI industry. It's where Donald O. Hebb originally formulated Hebbian Learning. It's also a vibrant, funky, cosmopolitan yet affordable city of over 4 million, referred to as "Canada's Cultural Capital" by Monocle magazine. In 2017, Montreal was ranked the 12th-most liveable city in the world by the Economist Intelligence Unit in its annual Global Liveability Ranking, and the best city in the world to be a university student in the QS World University Rankings. For more details and instructions how to apply, please visit: https://bit.ly/3l1PGFH
Dr. Gabriele Scheler
Participation/Support in a Project on Theoretical Foundations of Plasticity, Review and planning
Prof. Tatjana Tchumatchenko
Postdoc position: The postdoc candidate will be involved in a computational project addressing how neurons efficiently synthesize and distribute proteins in order to ensure that these are readily available across all synapses, will analyze data and model synaptic plasticity changes in order to understand health and disease states computationally. This work is centered on computational tools and includes pen-and-paper calculations, data analysis, and numerical simulations and requires an interdisciplinary mindset. PhD position: The PhD candidate will be conducting circuit level data analysis and modeling of neural activity states. He/she will contribute to the development of machine learning algorithms to analyse imaging data or to distinguish different behavioral activity states. This work is centered on dynamical systems methods, data analysis and numerical simulations and requires an interdisciplinary mindset. Master students interested in conducting Master thesis research (6-12 months) related to the two projects above a welcome to apply.
Cian O’Donnell
We are looking for a computational neuroscience PhD student for a project on “NeuroAI approaches to understanding inter-individual differences in cognition and psychiatric disorders.” The goal is to use populations of deep neural networks as a simple model for populations of human brains, combined with models from evolutionary genetics, to understand the principles underlying the mapping from genotypes to cognitive phenotypes.
Prof. Joris de Wit
This is a collaborative international project with the laboratory of Anthony Holtmaat (University of Geneva, Switzerland), funded by the Weave cross-European initiative. The project is at the interface of the expertise of the De Wit lab (molecular mechanisms of synaptic connectivity) and of the Holtmaat lab (synaptic integration of sensory input and context in cortical circuits). The project will unravel molecular mechanisms of synaptic specificity in cortical and thalamocortical circuits. Our recent work has shown that higher-order thalamocortical inputs and cortical inputs to pyramidal neurons in the somatosensory cortex display striking differences in their synaptic properties, even when intermingled on the same cortical dendrite. This project will explore the molecular mechanisms that mediate this specificity and test how these regulate structure and function of higher-order thalamocortical inputs in cortical circuits. The applicant will use a broad array of technologies including super-resolution imaging, CRISPR/Cas9 gene editing, viral vectors, conditional knockout mice, optogenetics, and in vivo imaging. The successful candidate will be based in Leuven, Belgium. The two labs will interact regularly via zoom and in-person meetings, and there will be several visits to the Holtmaat lab to transfer skills and exchange results during this project.
Prof Joris de Wit
This is a collaborative international project with the laboratory of Anthony Holtmaat (University of Geneva, Switzerland), funded by the Weave cross-European initiative. The project is at the interface of the expertise of the De Wit lab (molecular mechanisms of synaptic connectivity) and of the Holtmaat lab (synaptic integration of sensory input and context in cortical circuits). The project will unravel molecular mechanisms of synaptic specificity in cortical and thalamocortical circuits. Our recent work has shown that higher-order thalamocortical inputs and cortical inputs to pyramidal neurons in the somatosensory cortex display striking differences in their synaptic properties, even when intermingled on the same cortical dendrite. This project will explore the molecular mechanisms that mediate this specificity and test how these regulate structure and function of higher-order thalamocortical inputs in cortical circuits. The applicant will use a broad array of technologies including super-resolution imaging, CRISPR/Cas9 gene editing, viral vectors, conditional knockout mice, optogenetics, and in vivo imaging.
Memory Decoding Journal Club: Distinct synaptic plasticity rules operate across dendritic compartments in vivo during learning
Distinct synaptic plasticity rules operate across dendritic compartments in vivo during learning
Memory Decoding Journal Club: Behavioral time scale synaptic plasticity underlies CA1 place fields
Behavioral time scale synaptic plasticity underlies CA1 place fields
Neural circuits underlying sleep structure and functions
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.
Fear learning induces synaptic potentiation between engram neurons in the rat lateral amygdala
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.
Cell-type-specific plasticity shapes neocortical dynamics for motor learning
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.
Consolidation of remote contextual memory in the neocortical memory engram
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.
Three-factor rules of synaptic plasticity: from reward to surprise
Meta-learning functional plasticity rules in neural networks
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.
Behavioral Timescale Synaptic Plasticity (BTSP) for biologically plausible credit assignment across multiple layers via top-down gating of dendritic plasticity
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-enriched computation and learning in spiking neural networks through Hebbian plasticity
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.
How neural circuits organize and learn during development
To generate brain circuits that are both flexible and stable requires the coordination of powerful developmental mechanisms acting at different scales, including activity-dependent synaptic plasticity and changes in single neuron properties. The brain prepares to efficiently compute information and reliably generate behavior during early development without any prior sensory experience but through patterned spontaneous activity. After the onset of sensory experience, ongoing activity continues to modify sensory circuits, and plays an important functional role in the mature brain. Using quantitative data analysis, experiment-driven theory and computational modeling, I will present a framework for how neural circuits are built and organized during early postnatal development into functional units, and how they are modified by intact and perturbed sensory-evoked activity. Inspired by experimental data from sensory cortex, I will then show how neural circuits use the resulting non-random connectivity to flexibly gate a network’s response, providing a mechanism for routing information.
The 15th David Smith Lecture in Anatomical Neuropharmacology: Professor Tim Bliss, "Memories of long term potentiation
The David Smith Lectures in Anatomical Neuropharmacology, Part of the 'Pharmacology, Anatomical Neuropharmacology and Drug Discovery Seminars Series', Department of Pharmacology, University of Oxford. The 15th David Smith Award Lecture in Anatomical Neuropharmacology will be delivered by Professor Tim Bliss, Visiting Professor at UCL and the Frontier Institutes of Science and Technology, Xi’an Jiaotong University, China, and is hosted by Professor Nigel Emptage. This award lecture was set up to celebrate the vision of Professor A David Smith, namely, that explanations of the action of drugs on the brain requires the definition of neuronal circuits, the location and interactions of molecules. Tim Bliss gained his PhD at McGill University in Canada. He joined the MRC National Institute for Medical Research in Mill Hill, London in 1967, where he remained throughout his career. His work with Terje Lømo in the late 1960’s established the phenomenon of long-term potentiation (LTP) as the dominant synaptic model of how the mammalian brain stores memories. He was elected as a Fellow of the Royal Society in 1994 and is a founding fellow of the Academy of Medical Sciences. He shared the Bristol Myers Squibb award for Neuroscience with Eric Kandel in 1991, the Ipsen Prize for Neural Plasticity with Richard Morris and Yadin Dudai in 2013. In May 2012 he gave the annual Croonian Lecture at the Royal Society on ‘The Mechanics of Memory’. In 2016 Tim, with Graham Collingridge and Richard Morris shared the Brain Prize, one of the world's most coveted science prizes. Abstract: In 1966 there appeared in Acta Physiologica Scandinavica an abstract of a talk given by Terje Lømo, a PhD student in Per Andersen’s laboratory at the University of Oslo. In it Lømo described the long-lasting potentiation of synaptic responses in the dentate gyrus of the anaesthetised rabbit that followed repeated episodes of 10-20Hz stimulation of the perforant path. Thus, heralded and almost entirely unnoticed, one of the most consequential discoveries of 20th century neuroscience was ushered into the world. Two years later I arrived in Oslo as a visiting post-doc from the National Institute for Medical Research in Mill Hill, London. In this talk I recall the events that led us to embark on a systematic reinvestigation of the phenomenon now known as long-term potentiation (LTP) and will then go on to describe the discoveries and controversies that enlivened the early decades of research into synaptic plasticity in the mammalian brain. I will end with an observer’s view of the current state of research in the field, and what we might expect from it in the future.
Molecular Logic of Synapse Organization and Plasticity
Connections between nerve cells called synapses are the fundamental units of communication and information processing in the brain. The accurate wiring of neurons through synapses into neural networks or circuits is essential for brain organization. Neuronal networks are sculpted and refined throughout life by constant adjustment of the strength of synaptic communication by neuronal activity, a process known as synaptic plasticity. Deficits in the development or plasticity of synapses underlie various neuropsychiatric disorders, including autism, schizophrenia and intellectual disability. The Siddiqui lab research program comprises three major themes. One, to assess how biochemical switches control the activity of synapse organizing proteins, how these switches act through their binding partners and how these processes are regulated to correct impaired synaptic function in disease. Two, to investigate how synapse organizers regulate the specificity of neuronal circuit development and how defined circuits contribute to cognition and behaviour. Three, to address how synapses are formed in the developing brain and maintained in the mature brain and how microcircuits formed by synapses are refined to fine-tune information processing in the brain. Together, these studies have generated fundamental new knowledge about neuronal circuit development and plasticity and enabled us to identify targets for therapeutic intervention.
Malignant synaptic plasticity in pediatric high-grade gliomas
Pediatric high-grade gliomas (pHGG) are a devastating group of diseases that urgently require novel therapeutic options. We have previously demonstrated that pHGGs directly synapse onto neurons and the subsequent tumor cell depolarization, mediated by calcium-permeable AMPA channels, promotes their proliferation. The regulatory mechanisms governing these postsynaptic connections are unknown. Here, we investigated the role of BDNF-TrkB signaling in modulating the plasticity of the malignant synapse. BDNF ligand activation of its canonical receptor, TrkB (which is encoded for by the gene NTRK2), has been shown to be one important modulator of synaptic regulation in the normal setting. Electrophysiological recordings of glioma cell membrane properties, in response to acute neurotransmitter stimulation, demonstrate in an inward current resembling AMPA receptor (AMPAR) mediated excitatory neurotransmission. Extracellular BDNF increases the amplitude of this glutamate-induced tumor cell depolarization and this effect is abrogated in NTRK2 knockout glioma cells. Upon examining tumor cell excitability using in situ calcium imaging, we found that BDNF increases the intensity of glutamate-evoked calcium transients in GCaMP6s expressing glioma cells. Western blot analysis indicates the tumors AMPAR properties are altered downstream of BDNF induced TrkB activation in glioma. Cell membrane protein capture (via biotinylation) and live imaging of pH sensitive GFP-tagged AMPAR subunits demonstrate an increase of calcium permeable channels at the tumors postsynaptic membrane in response to BDNF. We find that BDNF-TrkB signaling promotes neuron-to-glioma synaptogenesis as measured by high-resolution confocal and electron microscopy in culture and tumor xenografts. Our analysis of published pHGG transcriptomic datasets, together with brain slice conditioned medium experiments in culture, indicates the tumor microenvironment as the chief source of BDNF ligand. Disruption of the BDNF-TrkB pathway in patient-derived orthotopic glioma xenograft models, both genetically and pharmacologically, results in an increased overall survival and reduced tumor proliferation rate. These findings suggest that gliomas leverage normal mechanisms of plasticity to modulate the excitatory channels involved in synaptic neurotransmission and they reveal the potential to target the regulatory components of glioma circuit dynamics as a therapeutic strategy for these lethal cancers.
Meta-learning synaptic plasticity and memory addressing for continual familiarity detection
Over the course of a lifetime, we process a continual stream of information. Extracted from this stream, memories must be efficiently encoded and stored in an addressable manner for retrieval. To explore potential mechanisms, we consider a familiarity detection task where a subject reports whether an image has been previously encountered. We design a feedforward network endowed with synaptic plasticity and an addressing matrix, meta-learned to optimize familiarity detection over long intervals. We find that anti-Hebbian plasticity leads to better performance than Hebbian and replicates experimental results such as repetition suppression. A combinatorial addressing function emerges, selecting a unique neuron as an index into the synaptic memory matrix for storage or retrieval. Unlike previous models, this network operates continuously, and generalizes to intervals it has not been trained on. Our work suggests a biologically plausible mechanism for continual learning, and demonstrates an effective application of machine learning for neuroscience discovery.
MicroRNAs as targets in the epilepsies: hits, misses and complexes
MicroRNAs are small noncoding RNAs that provide a critical layer of gene expression control. Individual microRNAs variably exert effects across networks of genes via sequence-specific binding to mRNAs, fine-tuning protein levels. This helps coordinate the timing and specification of cell fate transitions during brain development and maintains neural circuit function and plasticity by activity-dependent (re)shaping of synapses and the levels of neurotransmitter components. MicroRNA levels have been found to be altered in tissue from the epileptogenic zone resected from adults with drug-resistant focal epilepsy and this has driven efforts to explore their therapeutic potential, in particular using antisense oligonucleotide (ASOs) inhibitors termed antimirs. Here, we review the molecular mechanisms by which microRNAs control brain excitability and the latest progress towards a microRNA-based treatment for temporal lobe epilepsy. We also look at whether microRNA-based approaches could be used to treat genetic epilepsies, correcting individual genes or dysregulated pathways. Finally, we look at how cells have evolved to maximise the efficiency of the microRNA system via RNA editing, where single base changes is capable of altering the repertoire of genes under the control of a single microRNA. The findings improve our understanding of the molecular landscape of the epileptic brain and may lead to new therapies.
Hebbian Plasticity Supports Predictive Self-Supervised Learning of Disentangled Representations
Discriminating distinct objects and concepts from sensory stimuli is essential for survival. Our brains accomplish this feat by forming meaningful internal representations in deep sensory networks with plastic synaptic connections. Experience-dependent plasticity presumably exploits temporal contingencies between sensory inputs to build these internal representations. However, the precise mechanisms underlying plasticity remain elusive. We derive a local synaptic plasticity model inspired by self-supervised machine learning techniques that shares a deep conceptual connection to Bienenstock-Cooper-Munro (BCM) theory and is consistent with experimentally observed plasticity rules. We show that our plasticity model yields disentangled object representations in deep neural networks without the need for supervision and implausible negative examples. In response to altered visual experience, our model qualitatively captures neuronal selectivity changes observed in the monkey inferotemporal cortex in-vivo. Our work suggests a plausible learning rule to drive learning in sensory networks while making concrete testable predictions.
Optimization at the Single Neuron Level: Prediction of Spike Sequences and Emergence of Synaptic Plasticity Mechanisms
Intelligent behavior depends on the brain’s ability to anticipate future events. However, the learning rules that enable neurons to predict and fire ahead of sensory inputs remain largely unknown. We propose a plasticity rule based on pre-dictive processing, where the neuron learns a low-rank model of the synaptic input dynamics in its membrane potential. Neurons thereby amplify those synapses that maximally predict other synaptic inputs based on their temporal relations, which provide a solution to an optimization problem that can be implemented at the single-neuron level using only local information. Consequently, neurons learn sequences over long timescales and shift their spikes towards the first inputs in a sequence. We show that this mechanism can explain the development of anticipatory motion signaling and recall in the visual system. Furthermore, we demonstrate that the learning rule gives rise to several experimentally observed STDP (spike-timing-dependent plasticity) mechanisms. These findings suggest prediction as a guiding principle to orchestrate learning and synaptic plasticity in single neurons.
From the cell biology of synaptic plasticity to SFARI
Experience-Dependent Transcription: From Genomic Mechanisms to Neural Circuit Function
Experience-dependent transcription is a key molecular mechanisms for regulating the development and plasticity of synapses and neural circuits and is thought to underlie cognitive functions such as perception, learning and memory. After two years of COVID-pandemic, the goal of this online conference is to allow investigators in the field to reconnect and to discuss their recent scientific findings.
New Mechanisms of Extracellular Matrix Remodeling
In the adult brain, synapses are tightly enwrapped by lattices of extracellular matrix that consist of extremely long-lived molecules. These lattices are deemed to stabilize synapses, restrict the reorganization of their transmission machinery, and prevent them from undergoing structural or morphological changes. At the same time, they are expected to retain some degree of flexibility to permit occasional events of synaptic plasticity. The recent understanding that structural changes to synapses are significantly more frequent than previously assumed (occurring even on a timescale of minutes) has called for a mechanism that allows continual and energy-efficient remodeling of the ECM at synapses. I review in the talk our recent work showcasing such a process, based on the constitutive recycling of synaptic ECM molecules. I discuss the key characteristics of this mechanism, focusing on its roles in mediating synaptic transmission and plasticity, and speculate on additional potential functions in neuronal signaling.
Structure, Function, and Learning in Distributed Neuronal Networks
A central goal in neuroscience is to understand how orchestrated computations in the brain arise from the properties of single neurons and networks of such neurons. Answering this question requires theoretical advances that shine light into the ‘black box’ of neuronal networks. In this talk, I will demonstrate theoretical approaches that help describe how cognitive and behavioral task implementations emerge from structure in neural populations and from biologically plausible learning rules. First, I will introduce an analytic theory that connects geometric structures that arise from neural responses (i.e., neural manifolds) to the neural population’s efficiency in implementing a task. In particular, this theory describes how easy or hard it is to discriminate between object categories based on the underlying neural manifolds’ structural properties. Next, I will describe how such methods can, in fact, open the ‘black box’ of neuronal networks, by showing how we can understand a) the role of network motifs in task implementation in neural networks and b) the role of neural noise in adversarial robustness in vision and audition. Finally, I will discuss my recent efforts to develop biologically plausible learning rules for neuronal networks, inspired by recent experimental findings in synaptic plasticity. By extending our mathematical toolkit for analyzing representations and learning rules underlying complex neuronal networks, I hope to contribute toward the long-term challenge of understanding the neuronal basis of behaviors.
JAK/STAT regulation of the transcriptomic response during epileptogenesis
Temporal lobe epilepsy (TLE) is a progressive disorder mediated by pathological changes in molecular cascades and neural circuit remodeling in the hippocampus resulting in increased susceptibility to spontaneous seizures and cognitive dysfunction. Targeting these cascades could prevent or reverse symptom progression and has the potential to provide viable disease-modifying treatments that could reduce the portion of TLE patients (>30%) not responsive to current medical therapies. Changes in GABA(A) receptor subunit expression have been implicated in the pathogenesis of TLE, and the Janus Kinase/Signal Transducer and Activator of Transcription (JAK/STAT) pathway has been shown to be a key regulator of these changes. The JAK/STAT pathway is known to be involved in inflammation and immunity, and to be critical for neuronal functions such as synaptic plasticity and synaptogenesis. Our laboratories have shown that a STAT3 inhibitor, WP1066, could greatly reduce the number of spontaneous recurrent seizures (SRS) in an animal model of pilocarpine-induced status epilepticus (SE). This suggests promise for JAK/STAT inhibitors as disease-modifying therapies, however, the potential adverse effects of systemic or global CNS pathway inhibition limits their use. Development of more targeted therapeutics will require a detailed understanding of JAK/STAT-induced epileptogenic responses in different cell types. To this end, we have developed a new transgenic line where dimer-dependent STAT3 signaling is functionally knocked out (fKO) by tamoxifen-induced Cre expression specifically in forebrain excitatory neurons (eNs) via the Calcium/Calmodulin Dependent Protein Kinase II alpha (CamK2a) promoter. Most recently, we have demonstrated that STAT3 KO in excitatory neurons (eNSTAT3fKO) markedly reduces the progression of epilepsy (SRS frequency) in the intrahippocampal kainate (IHKA) TLE model and protects mice from kainic acid (KA)-induced memory deficits as assessed by Contextual Fear Conditioning. Using data from bulk hippocampal tissue RNA-sequencing, we further discovered a transcriptomic signature for the IHKA model that contains a substantial number of genes, particularly in synaptic plasticity and inflammatory gene networks, that are down-regulated after KA-induced SE in wild-type but not eNSTAT3fKO mice. Finally, we will review data from other models of brain injury that lead to epilepsy, such as TBI, that implicate activation of the JAK/STAT pathway that may contribute to epilepsy development.
A nonlinear shot noise model for calcium-based synaptic plasticity
Activity dependent synaptic plasticity is considered to be a primary mechanism underlying learning and memory. Yet it is unclear whether plasticity rules such as STDP measured in vitro apply in vivo. Network models with STDP predict that activity patterns (e.g., place-cell spatial selectivity) should change much faster than observed experimentally. We address this gap by investigating a nonlinear calcium-based plasticity rule fit to experiments done in physiological conditions. In this model, LTP and LTD result from intracellular calcium transients arising almost exclusively from synchronous coactivation of pre- and postsynaptic neurons. We analytically approximate the full distribution of nonlinear calcium transients as a function of pre- and postsynaptic firing rates, and temporal correlations. This analysis directly relates activity statistics that can be measured in vivo to the changes in synaptic efficacy they cause. Our results highlight that both high-firing rates and temporal correlations can lead to significant changes to synaptic efficacy. Using a mean-field theory, we show that the nonlinear plasticity rule, without any fine-tuning, gives a stable, unimodal synaptic weight distribution characterized by many strong synapses which remain stable over long periods of time, consistent with electrophysiological and behavioral studies. Moreover, our theory explains how memories encoded by strong synapses can be preferentially stabilized by the plasticity rule. We confirmed our analytical results in a spiking recurrent network. Interestingly, although most synapses are weak and undergo rapid turnover, the fraction of strong synapses are sufficient for supporting realistic spiking dynamics and serve to maintain the network’s cluster structure. Our results provide a mechanistic understanding of how stable memories may emerge on the behavioral level from an STDP rule measured in physiological conditions. Furthermore, the plasticity rule we investigate is mathematically equivalent to other learning rules which rely on the statistics of coincidences, so we expect that our formalism will be useful to study other learning processes beyond the calcium-based plasticity rule.
NMC4 Short Talk: Systematic exploration of neuron type differences in standard plasticity protocols employing a novel pathway based plasticity rule
Spike Timing Dependent Plasticity (STDP) is argued to modulate synaptic strength depending on the timing of pre- and postsynaptic spikes. Physiological experiments identified a variety of temporal kernels: Hebbian, anti-Hebbian and symmetrical LTP/LTD. In this work we present a novel plasticity model, the Voltage-Dependent Pathway Model (VDP), which is able to replicate those distinct kernel types and intermediate versions with varying LTP/LTD ratios and symmetry features. In addition, unlike previous models it retains these characteristics for different neuron models, which allows for comparison of plasticity in different neuron types. The plastic updates depend on the relative strength and activation of separately modeled LTP and LTD pathways, which are modulated by glutamate release and postsynaptic voltage. We used the 15 neuron type parametrizations in the GLIF5 model presented by Teeter et al. (2018) in combination with the VDP to simulate a range of standard plasticity protocols including standard STDP experiments, frequency dependency experiments and low frequency stimulation protocols. Slight variation in kernel stability and frequency effects can be identified between the neuron types, suggesting that the neuron type may have an effect on the effective learning rule. This plasticity model builds a middle ground between biophysical and phenomenological models allowing not just for the combination with more complex and biophysical neuron models, but is also computationally efficient so can be used in network simulations. Therefore it offers the possibility to explore the functional role of the different kernel types and electrophysiological differences in heterogeneous networks in future work.
Synaptic plasticity controls the emergence of population-wide invariant representations in balanced network models
The intensity and features of sensory stimuli are encoded in the activity of neurons in the cortex. In the visual and piriform cortices, the stimulus intensity re-scales the activity of the population without changing its selectivity for the stimulus features. The cortical representation of the stimulus is therefore intensity-invariant. This emergence of network invariant representations appears robust to local changes in synaptic strength induced by synaptic plasticity, even though: i) synaptic plasticity can potentiate or depress connections between neurons in a feature-dependent manner, and ii) in networks with balanced excitation and inhibition, synaptic plasticity determines the non-linear network behavior. In this study, we investigate the consistency of invariant representations with a variety of synaptic states in balanced networks. By using mean-field models and spiking network simulations, we show how the synaptic state controls the emergence of intensity-invariant or intensity-dependent selectivity by inducing changes in the network response to intensity. In particular, we demonstrate how facilitating synaptic states can sharpen the network selectivity while depressing states broaden it. We also show how power-law-type synapses permit the emergence of invariant network selectivity and how this plasticity can be generated by a mix of different plasticity rules. Our results explain how the physiology of individual synapses is linked to the emergence of invariant representations of sensory stimuli at the network level.
Deriving local synaptic learning rules for efficient representations in networks of spiking neurons
How can neural networks learn to efficiently represent complex and high-dimensional inputs via local plasticity mechanisms? Classical models of representation learning assume that input weights are learned via pairwise Hebbian-like plasticity. Here, we show that pairwise Hebbian-like plasticity only works under specific requirements on neural dynamics and input statistics. To overcome these limitations, we derive from first principles a learning scheme based on voltage-dependent synaptic plasticity rules. Here, inhibition learns to locally balance excitatory input in individual dendritic compartments, and thereby can modulate excitatory synaptic plasticity to learn efficient representations. We demonstrate in simulations that this learning scheme works robustly even for complex, high-dimensional and correlated inputs. It also works in the presence of inhibitory transmission delays, where Hebbian-like plasticity typically fails. Our results draw a direct connection between dendritic excitatory-inhibitory balance and voltage-dependent synaptic plasticity as observed in vivo, and suggest that both are crucial for representation learning.
Credit Assignment in Neural Networks through Deep Feedback Control
The success of deep learning sparked interest in whether the brain learns by using similar techniques for assigning credit to each synaptic weight for its contribution to the network output. However, the majority of current attempts at biologically-plausible learning methods are either non-local in time, require highly specific connectivity motives, or have no clear link to any known mathematical optimization method. Here, we introduce Deep Feedback Control (DFC), a new learning method that uses a feedback controller to drive a deep neural network to match a desired output target and whose control signal can be used for credit assignment. The resulting learning rule is fully local in space and time and approximates Gauss-Newton optimization for a wide range of feedback connectivity patterns. To further underline its biological plausibility, we relate DFC to a multi-compartment model of cortical pyramidal neurons with a local voltage-dependent synaptic plasticity rule, consistent with recent theories of dendritic processing. By combining dynamical system theory with mathematical optimization theory, we provide a strong theoretical foundation for DFC that we corroborate with detailed results on toy experiments and standard computer-vision benchmarks.
Disinhibitory and neuromodulatory regulation of hippocampal synaptic plasticity
The CA1 pyramidal neurons are embedded in an intricate local circuitry that contains a variety of interneurons. The roles these interneurons play in the regulation of the excitatory synaptic plasticity remains largely understudied. Recent experiments showed that repeated cholinergic activation of 𝛼7 nACh receptors expressed in oriens-lacunosum-moleculare (OLM𝛼2) interneurons could induce LTP in SC-CA1 synapses. We used a biophysically realistic computational model to examine mechanistically how cholinergic activation of OLMa2 interneurons increases SC to CA1 transmission. Our results suggest that, when properly timed, activation of OLMa2 interneurons cancels the feedforward inhibition onto CA1 pyramidal cells by inhibiting fast-spiking interneurons that synapse on the same dendritic compartment as the SC, i.e., by disinhibiting the pyramidal cell dendritic compartment. Our work further describes the pairing of disinhibition with SC stimulation as a general mechanism for the induction of synaptic plasticity. We found that locally-reduced GABA release (disinhibition) paired with SC stimulation could lead to increased NMDAR activation and intracellular calcium concentration sufficient to upregulate AMPAR permeability and potentiate the excitatory synapse. Our work suggests that inhibitory synapses critically modulate excitatory neurotransmission and induction of plasticity at excitatory synapses. Our work also shows how cholinergic action on OLM interneurons, a mechanism whose disruption is associated with memory impairment, can down-regulate the GABAergic signaling into CA1 pyramidal cells and facilitate potentiation of the SC-CA1 synapse.
Dopaminergic modulation of synaptic plasticity in learning and psychiatric disorders
Transient changes in dopamine activity in response to reward and punishment have been known to regulate reward-related learning. However, the cellular basis that detects the transient dopamine signaling has long been unclear. Using two-photon microscopy and optogenetics, I have shown that transient increases and decreases of dopamine modulate plasticity of dopamine D1 and D2 receptor-expressing cells in the nucleus accumbens, respectively. At the behavioral level, I characterized that these D1 and D2 cells cooperatively tune learning by generalization and discrimination learning. Interestingly, disturbance of the dopamine signaling impaired D2 cell plasticity and discrimination learning, which was analogous to salience misattribution seen in subjects with schizophrenia.
D1 and D2 Accumbens Neurons May not be Who You Think They Are: Distinct tetrapartite synaptic plasticity regulating drug relapse
Neural mechanisms of navigation behavior
The regions of the insect brain devoted to spatial navigation are beautifully orderly, with a remarkably precise pattern of synaptic connections. Thus, we can learn much about the neural mechanisms of spatial navigation by targeting identifiable neurons in these networks for in vivo patch clamp recording and calcium imaging. Our lab has recently discovered that the "compass system" in the Drosophila brain is anchored to not only visual landmarks, but also the prevailing wind direction. Moreover, we found that the compass system can re-learn the relationship between these external sensory cues and internal self-motion cues, via rapid associative synaptic plasticity. Postsynaptic to compass neurons, we found neurons that conjunctively encode heading direction and body-centric translational velocity. We then showed how this representation of travel velocity is transformed from body- to world-centric coordinates at the subsequent layer of the network, two synapses downstream from compass neurons. By integrating this world-centric vector-velocity representation over time, it should be possible for the brain to form a stored representation of the body's path through the environment.
A theory for Hebbian learning in recurrent E-I networks
The Stabilized Supralinear Network is a model of recurrently connected excitatory (E) and inhibitory (I) neurons with a supralinear input-output relation. It can explain cortical computations such as response normalization and inhibitory stabilization. However, the network's connectivity is designed by hand, based on experimental measurements. How the recurrent synaptic weights can be learned from the sensory input statistics in a biologically plausible way is unknown. Earlier theoretical work on plasticity focused on single neurons and the balance of excitation and inhibition but did not consider the simultaneous plasticity of recurrent synapses and the formation of receptive fields. Here we present a recurrent E-I network model where all synaptic connections are simultaneously plastic, and E neurons self-stabilize by recruiting co-tuned inhibition. Motivated by experimental results, we employ a local Hebbian plasticity rule with multiplicative normalization for E and I synapses. We develop a theoretical framework that explains how plasticity enables inhibition balanced excitatory receptive fields that match experimental results. We show analytically that sufficiently strong inhibition allows neurons' receptive fields to decorrelate and distribute themselves across the stimulus space. For strong recurrent excitation, the network becomes stabilized by inhibition, which prevents unconstrained self-excitation. In this regime, external inputs integrate sublinearly. As in the Stabilized Supralinear Network, this results in response normalization and winner-takes-all dynamics: when two competing stimuli are presented, the network response is dominated by the stronger stimulus while the weaker stimulus is suppressed. In summary, we present a biologically plausible theoretical framework to model plasticity in fully plastic recurrent E-I networks. While the connectivity is derived from the sensory input statistics, the circuit performs meaningful computations. Our work provides a mathematical framework of plasticity in recurrent networks, which has previously only been studied numerically and can serve as the basis for a new generation of brain-inspired unsupervised machine learning algorithms.
Memory, learning to learn, and control of cognitive representations
Biological neural networks can represent information in the collective action potential discharge of neurons, and store that information amongst the synaptic connections between the neurons that both comprise the network and govern its function. The strength and organization of synaptic connections adjust during learning, but many cognitive neural systems are multifunctional, making it unclear how continuous activity alternates between the transient and discrete cognitive functions like encoding current information and recollecting past information, without changing the connections amongst the neurons. This lecture will first summarize our investigations of the molecular and biochemical mechanisms that change synaptic function to persistently store spatial memory in the rodent hippocampus. I will then report on how entorhinal cortex-hippocampus circuit function changes during cognitive training that creates memory, as well as learning to learn in mice. I will then describe how the hippocampus system operates like a competitive winner-take-all network, that, based on the dominance of its current inputs, self organizes into either the encoding or recollection information processing modes. We find no evidence that distinct cells are dedicated to those two distinct functions, rather activation of the hippocampus information processing mode is controlled by a subset of dentate spike events within the network of learning-modified, entorhinal-hippocampus excitatory and inhibitory synapses.
Circuit mechanisms for synaptic plasticity in the rodent somatosensory cortex
Sensory experience and perceptual learning changes receptive field properties of cortical pyramidal neurons possibly mediated by long-term potentiation (LTP) of synapses. We have previously shown in the mouse somatosensory cortex (S1) that sensory-driven LTP in layer (L) 2/3 pyramidal neurons is dependent on higher order thalamic feedback from the posteromedial nucleus (POm), which is thought to convey contextual information from various cortical regions integrated with sensory input. We have followed up on this work by dissecting the cortical microcircuitry that underlies this form of LTP. We found that repeated pairing of Pom thalamocortical and intracortical pathway activity in brain slices induces NMDAr-dependent LTP of the L2/3 synapses that are driven by the intracortical pathway. Repeated pairing also recruits activity of vasoactive intestinal peptide (VIP) interneurons, whereas it reduces the activity of somatostatin (SST) interneurons. VIP interneuron-mediated inhibition of SST interneurons has been established as a motif for the disinhibition of pyramidal neurons. By chemogenetic interrogation we found that activation of this disinhibitory microcircuit motif by higher-order thalamic feedback is indispensable for eliciting LTP. Preliminary results in vivo suggest that VIP neuron activity also increases during sensory-evoked LTP. Together, this suggests that the higherorder thalamocortical feedback may help modifying the strength of synaptic circuits that process first-order sensory information in S1. To start characterizing the relationship between higher-order feedback and cortical plasticity during learning in vivo, we adapted a perceptual learning paradigm in which head-fixed mice have to discriminate two types of textures in order to obtain a reward. POm axons or L2/3 pyramidal neurons labeled with the genetically encoded calcium indicator GCaMP6s were imaged during the acquisition of this task as well as the subsequent learning of a new discrimination rule. We found that a subpopulation of the POm axons and L2/3 neurons dynamically represent textures. Moreover, upon a change in reward contingencies, a fraction of the L2/3 neurons re-tune their selectivity to the texture that is newly associated with the reward. Altogether, our data indicates that higher-order thalamic feedback can facilitate synaptic plasticity and may be implicated in dynamic sensory stimulus representations in S1, which depends on higher-order features that are associated with the stimuli.
Hebbian learning, its inference, and brain oscillation
Despite the recent success of deep learning in artificial intelligence, the lack of biological plausibility and labeled data in natural learning still poses a challenge in understanding biological learning. At the other extreme lies Hebbian learning, the simplest local and unsupervised one, yet considered to be computationally less efficient. In this talk, I would introduce a novel method to infer the form of Hebbian learning from in vivo data. Applying the method to the data obtained from the monkey inferior temporal cortex for the recognition task indicates how Hebbian learning changes the dynamic properties of the circuits and may promote brain oscillation. Notably, recent electrophysiological data observed in rodent V1 showed that the effect of visual experience on direction selectivity was similar to that observed in monkey data and provided strong validation of asymmetric changes of feedforward and recurrent synaptic strengths inferred from monkey data. This may suggest a general learning principle underlying the same computation, such as familiarity detection across different features represented in different brain regions.
Interacting synapses stabilise both learning and neuronal dynamics in biological networks
Distinct synapses influence one another when they undergo changes, with unclear consequences for neuronal dynamics and function. Here we show that synapses can interact such that excitatory currents are naturally normalised and balanced by inhibitory inputs. This happens when classical spike-timing dependent synaptic plasticity rules are extended by additional mechanisms that incorporate the influence of neighbouring synaptic currents and regulate the amplitude of efficacy changes accordingly. The resulting control of excitatory plasticity by inhibitory activation, and vice versa, gives rise to quick and long-lasting memories as seen experimentally in receptive field plasticity paradigms. In models with additional dendritic structure, we observe experimentally reported clustering of co-active synapses that depends on initial connectivity and morphology. Finally, in recurrent neural networks, rich and stable dynamics with high input sensitivity emerge, providing transient activity that resembles recordings from the motor cortex. Our model provides a general framework for codependent plasticity that frames individual synaptic modifications in the context of population-wide changes, allowing us to connect micro-level physiology with behavioural phenomena.
Microglia, memories, and the extracellular space
Microglia are the immune cells of the brain, and play increasingly appreciated roles in synapse formation, brain plasticity, and cognition. A growing appreciation that the immune system involved in diseases like schizophrenia, epilepsy, and neurodegenerative diseases has led to renewed interest in how microglia regulate synaptic connectivity. Our group previously identified the IL-1 family cytokine Interleukin-33 (IL-33) as a novel regulator of microglial activation and function. I will discuss a mechanism by which microglia regulate synaptic plasticity and long-term memories by engulfing brain extracellular matrix (ECM) proteins. These studies raise the question of how these pathways may be altered or could be modified in the context of disease.
Emergence of long time scales in data-driven network models of zebrafish activity
How can neural networks exhibit persistent activity on time scales much larger than allowed by cellular properties? We address this question in the context of larval zebrafish, a model vertebrate that is accessible to brain-scale neuronal recording and high-throughput behavioral studies. We study in particular the dynamics of a bilaterally distributed circuit, the so-called ARTR, including hundreds neurons. ARTR exhibits slow antiphasic alternations between its left and right subpopulations, which can be modulated by the water temperature, and drive the coordinated orientation of swim bouts, thus organizing the fish spatial exploration. To elucidate the mechanism leading to the slow self-oscillation, we train a network graphical model (Ising) on neural recordings. Sampling the inferred model allows us to generate synthetic oscillatory activity, whose features correctly capture the observed dynamics. A mean-field analysis of the inferred model reveals the existence several phases; activated crossing of the barriers in between those phases controls the long time scales present in the network oscillations. We show in particular how the barrier heights and the nature of the phases vary with the water temperature.
Receptor Costs Determine Retinal Design
Our group is interested in discovering design principles that govern the structure and function of neurons and neural circuits. We record from well-defined neurons, mainly in flies’ visual systems, to measure the molecular and cellular factors that determine relevant measures of performance, such as representational capacity, dynamic range and accuracy. We combine this empirical approach with modelling to see how the basic elements of neural systems (ion channels, second messengers systems, membranes, synapses, neurons, circuits and codes) combine to determine performance. We are investigating four general problems. How are circuits designed to integrate information efficiently? How do sensory adaptation and synaptic plasticity contribute to efficiency? How do the sizes of neurons and networks relate to energy consumption and representational capacity? To what extent have energy costs shaped neurons, sense organs and brain regions during evolution?
Distinct synaptic plasticity mechanisms determine the diversity of cortical responses during behavior
Spike trains recorded from the cortex of behaving animals can be complex, highly variable from trial to trial, and therefore challenging to interpret. A fraction of cells exhibit trial-averaged responses with obvious task-related features such as pure tone frequency tuning in auditory cortex. However, a substantial number of cells (including cells in primary sensory cortex) do not appear to fire in a task-related manner and are often neglected from analysis. We recently used a novel single-trial, spike-timing-based analysis to show that both classically responsive and non-classically responsive cortical neurons contain significant information about sensory stimuli and behavioral decisions suggesting that non-classically responsive cells may play an underappreciated role in perception and behavior. We now expand this investigation to explore the synaptic origins and potential contribution of these cells to network function. To do so, we trained a novel spiking recurrent neural network model that incorporates spike-timing-dependent plasticity (STDP) mechanisms to perform the same task as behaving animals. By leveraging excitatory and inhibitory plasticity rules this model reproduces neurons with response profiles that are consistent with previously published experimental data, including classically responsive and non-classically responsive neurons. We found that both classically responsive and non-classically responsive neurons encode behavioral variables in their spike times as seen in vivo. Interestingly, plasticity in excitatory-to-excitatory synapses increased the proportion of non-classically responsive neurons and may play a significant role in determining response profiles. Finally, our model also makes predictions about the synaptic origins of classically and non-classically responsive neurons which we can compare to in vivo whole-cell recordings taken from the auditory cortex of behaving animals. This approach successfully recapitulates heterogeneous response profiles measured from behaving animals and provides a powerful lens for exploring large-scale neuronal dynamics and the plasticity rules that shape them.
Virus-like intercellular communication in the nervous system
The neuronal gene Arc is essential for long-lasting information storage in the mammalian brain and mediates various forms of synaptic plasticity. We recently discovered that Arc self-assembles into virus-like capsids that encapsulate RNA. Endogenous Arc protein is released from neurons in extracellular vesicles that mediate the transfer of Arc mRNA into new target cells. Evolutionary analysis indicates that Arc is derived from a vertebrate lineage of Ty3/gypsy retrotransposons, which are also ancestral to retroviruses such as HIV. These findings suggest that Gag retroelements have been repurposed during evolution to mediate intercellular communication in the nervous system that may underlie cognition and memory.
The emergence of contrast invariance in cortical circuits
Neurons in the primary visual cortex (V1) encode the orientation and contrast of visual stimuli through changes in firing rate (Hubel and Wiesel, 1962). Their activity typically peaks at a preferred orientation and decays to zero at the orientations that are orthogonal to the preferred. This activity pattern is re-scaled by contrast but its shape is preserved, a phenomenon known as contrast invariance. Contrast-invariant selectivity is also observed at the population level in V1 (Carandini and Sengpiel, 2004). The mechanisms supporting the emergence of contrast-invariance at the population level remain unclear. How does the activity of different neurons with diverse orientation selectivity and non-linear contrast sensitivity combine to give rise to contrast-invariant population selectivity? Theoretical studies have shown that in the balance limit, the properties of single-neurons do not determine the population activity (van Vreeswijk and Sompolinsky, 1996). Instead, the synaptic dynamics (Mongillo et al., 2012) as well as the intracortical connectivity (Rosenbaum and Doiron, 2014) shape the population activity in balanced networks. We report that short-term plasticity can change the synaptic strength between neurons as a function of the presynaptic activity, which in turns modifies the population response to a stimulus. Thus, the same circuit can process a stimulus in different ways –linearly, sublinearly, supralinearly – depending on the properties of the synapses. We found that balanced networks with excitatory to excitatory short-term synaptic plasticity cannot be contrast-invariant. Instead, short-term plasticity modifies the network selectivity such that the tuning curves are narrower (broader) for increasing contrast if synapses are facilitating (depressing). Based on these results, we wondered whether balanced networks with plastic synapses (other than short-term) can support the emergence of contrast-invariant selectivity. Mathematically, we found that the only synaptic transformation that supports perfect contrast invariance in balanced networks is a power-law release of neurotransmitter as a function of the presynaptic firing rate (in the excitatory to excitatory and in the excitatory to inhibitory neurons). We validate this finding using spiking network simulations, where we report contrast-invariant tuning curves when synapses release the neurotransmitter following a power- law function of the presynaptic firing rate. In summary, we show that synaptic plasticity controls the type of non-linear network response to stimulus contrast and that it can be a potential mechanism mediating the emergence of contrast invariance in balanced networks with orientation-dependent connectivity. Our results therefore connect the physiology of individual synapses to the network level and may help understand the establishment of contrast-invariant selectivity.
Protein Synthesis at Neuronal Synapses
The complex morphology of neurons, with synapses located 100’s of microns from the cell body, necessitates the localization of important cell biological machines and processes within dendrites and axons. Using expansion microscopy together with metabolic labeling we have discovered that both postsynaptic spines and presynaptic terminals exhibit rapid translation, which exhibits differential sensitivity to different neurotransmitters and neuromodulators. In addition, we have explored the unique mechanisms neurons use to meet protein demands at synapses, identifying the transcriptome and translatome in the neuropil.
Plasticity in hypothalamic circuits for oxytocin release
Mammalian babies are “sensory traps” for parents. Various sensory cues from the newborn are tremendously efficient in triggering parental responses in caregivers. We recently showed that core aspects of maternal behavior such as pup retrieval in response to infant vocalizations rely on active learning of auditory cues from pups facilitated by the neurohormone oxytocin (OT). Release of OT from the hypothalamus might thus help induce recognition of different infant cues but it is unknown what sensory stimuli can activate OT neurons. I performed unprecedented in vivo whole-cell and cell-attached recordings from optically-identified OT neurons in awake dams. I found that OT neurons, but not other hypothalamic cells, increased their firing rate after playback of pup distress vocalizations. Using anatomical tracing approaches and channelrhodopsin-assisted circuit mapping, I identified the projections and brain areas (including inferior colliculus, auditory cortex, and posterior intralaminar thalamus) relaying auditory information about social sounds to OT neurons. In hypothalamic brain slices, when optogenetically stimulating thalamic afferences to mimic high-frequency thalamic discharge, observed in vivo during pup calls playback, I found that thalamic activity led to long-term depression of synaptic inhibition in OT neurons. This was mediated by postsynaptic NMDARs-induced internalization of GABAARs. Therefore, persistent activation of OT neurons following pup calls in vivo is likely mediated by disinhibition. This gain modulation of OT neurons by infant cries, may be important for sustaining motivation. Using a genetically-encoded OT sensor, I demonstrated that pup calls were efficient in triggering OT release in downstream motivational areas. When thalamus projections to hypothalamus were inhibited with chemogenetics, dams exhibited longer latencies to retrieve crying pups, suggesting that the thalamus-hypothalamus noncanonical auditory pathway may be a specific circuit for the detection of social sounds, important for disinhibiting OT neurons, gating OT release in downstream brain areas, and speeding up maternal behavior.
Presynaptic plasticity in hippocampal circuits
Christophe Mulle is a cellular neurobiologist with expertise in electrophysiology of synaptic transmission and an international leader in studies on glutamate receptors and hippocampal synaptic plasticity. He was among the first to identify and characterize functional nicotinic receptors in the mammalian brain while working in the laboratory of Jean-Pierre Changeux at the Pasteur Institute. He then generated knock-out mice for KAR subunits at the Salk Institute in the laboratory of Steve Heinemann, which have proven to be instrumental for understanding the function of these elusive glutamate receptors in synaptic function and plasticity.
Dynamic computation in the retina by retuning of neurons and synapses
How does a circuit of neurons process sensory information? And how are transformations of neural signals altered by changes in synaptic strength? We investigate these questions in the context of the visual system and the lateral line of fish. A distinguishing feature of our approach is the imaging of activity across populations of synapses – the fundamental elements of signal transfer within all brain circuits. A guiding hypothesis is that the plasticity of neurotransmission plays a major part in controlling the input-output relation of sensory circuits, regulating the tuning and sensitivity of neurons to allow adaptation or sensitization to particular features of the input. Sensory systems continuously adjust their input-output relation according to the recent history of the stimulus. A common alteration is a decrease in the gain of the response to a constant feature of the input, termed adaptation. For instance, in the retina, many of the ganglion cells (RGCs) providing the output produce their strongest responses just after the temporal contrast of the stimulus increases, but the response declines if this input is maintained. The advantage of adaptation is that it prevents saturation of the response to strong stimuli and allows for continued signaling of future increases in stimulus strength. But adaptation comes at a cost: a reduced sensitivity to a future decrease in stimulus strength. The retina compensates for this loss of information through an intriguing strategy: while some RGCs adapt following a strong stimulus, a second population gradually becomes sensitized. We found that the underlying circuit mechanisms involve two opposing forms of synaptic plasticity in bipolar cells: synaptic depression causes adaptation and facilitation causes sensitization. Facilitation is in turn caused by depression in inhibitory synapses providing negative feedback. These opposing forms of plasticity can cause simultaneous increases and decreases in contrast-sensitivity of different RGCs, which suggests a general framework for understanding the function of sensory circuits: plasticity of both excitatory and inhibitory synapses control dynamic changes in tuning and gain.
AMPA receptor dysfunction in cognitive disorders
Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits
Synaptic plasticity is believed to be a key physiological mechanism for learning. It is well-established that it depends on pre and postsynaptic activity. However, models that rely solely on pre and postsynaptic activity for synaptic changes have, to date, not been able to account for learning complex tasks that demand hierarchical networks. Here, we show that if synaptic plasticity is regulated by high-frequency bursts of spikes, then neurons higher in the hierarchy can coordinate the plasticity of lower-level connections. Using simulations and mathematical analyses, we demonstrate that, when paired with short-term synaptic dynamics, regenerative activity in the apical dendrites, and synaptic plasticity in feedback pathways, a burst-dependent learning rule can solve challenging tasks that require deep network architectures. Our results demonstrate that well-known properties of dendrites, synapses, and synaptic plasticity are sufficient to enable sophisticated learning in hierarchical circuits.
Synaptic, cellular, and circuit mechanisms for learning: insights from electric fish
Understanding learning in neural circuits requires answering a number of difficult questions: (1) What is the computation being performed and what is its behavioral significance? (2) What are the inputs required for the computation and how are they represented at the level of spikes? (3) What are the sites and rules governing plasticity, i.e. how do pre and post-synaptic activity patterns produce persistent changes in synaptic strength? (4) How does network connectivity and dynamics shape the computation being performed? I will discuss joint experimental and theoretical work addressing these questions in the context of the electrosensory lobe (ELL) of weakly electric mormyrid fish.
Circuit and synaptic mechanisms of plasticity in neural ensembles
Inhibitory microcircuits play an important role regulating cortical responses to sensory stimuli. Interneurons that inhibit dendritic or somatic integration are gatekeepers for neural activity, synaptic plasticity and the formation of sensory representations. We have been investigating the synaptic plasticity mechanisms underlying the formation of ensembles in olfactory and orbitofrontal cortex. We have been focusing on the roles of three inhibitory neuron classes in gating excitatory synaptic plasticity in olfactory cortex- somatostatin (SST-INs), parvalbumin (PV-INs), and vasoactive intestinal polypeptide (VIP-INs) interneurons. Further, we are investigating the rules for inhibitory plasticity and a potential role in stabilizing ensembles in associative cortices. I will present new findings to support distinct roles for different interneuron classes in the gating and stabilization of ensemble representations of olfactory responses.
Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits
Synaptic plasticity is believed to be a key physiological mechanism for learning. It is well-established that it depends on pre and postsynaptic activity. However, models that rely solely on pre and postsynaptic activity for synaptic changes have, to date, not been able to account for learning complex tasks that demand hierarchical networks. Here, we show that if synaptic plasticity is regulated by high-frequency bursts of spikes, then neurons higher in the hierarchy can coordinate the plasticity of lower-level connections. Using simulations and mathematical analyses, we demonstrate that, when paired with short-term synaptic dynamics, regenerative activity in the apical dendrites, and synaptic plasticity in feedback pathways, a burst-dependent learning rule can solve challenging tasks that require deep network architectures. Our results demonstrate that well-known properties of dendrites, synapses, and synaptic plasticity are sufficient to enable sophisticated learning in hierarchical circuits.
Investigating hippocampal synaptic plasticity in Schizophrenia: a computational and experimental approach using MEA recordings
Bernstein Conference 2024
Exploring behavioral correlations with neuron activity through synaptic plasticity.
Bernstein Conference 2024
A family of synaptic plasticity rules based on spike times produces a diversity of triplet motifs in recurrent networks
Bernstein Conference 2024
Physiological Implementation of Synaptic Plasticity at Behavioral Timescales Supports Computational Properties of Place Cell Formation
Bernstein Conference 2024
Plastic Arbor: a modern simulation framework for synaptic plasticity – from single synapses to networks of morphological neurons
Bernstein Conference 2024
Synaptic Plasticity Mechanisms Enable Incremental Learning of Spatio-Temporal Activity Patterns
Bernstein Conference 2024
Synergistic short-term synaptic plasticity mechanisms for working memory
Bernstein Conference 2024
Top-down modulation shapes timescales via synaptic plasticity in cortical circuits with multiple interneuron types
Bernstein Conference 2024
Bayesian synaptic plasticity is energy efficient
COSYNE 2022
Clustered recurrent connectivity promotes the development of E/I co-tuning via synaptic plasticity
COSYNE 2022
Isolating the role of synaptic plasticity in hippocampal place codes
COSYNE 2022
Isolating the role of synaptic plasticity in hippocampal place codes
COSYNE 2022
Neuromodulation of synaptic plasticity rules avoids homeostatic reset of synaptic weights during switches in brain states
COSYNE 2022
Neuromodulation of synaptic plasticity rules avoids homeostatic reset of synaptic weights during switches in brain states
COSYNE 2022
A synaptic plasticity rule based on presynaptic variance to infer input reliability
COSYNE 2022
A synaptic plasticity rule based on presynaptic variance to infer input reliability
COSYNE 2022
Distinct Fos- and Npas4-mediated synaptic plasticity crucial for memory consolidation
COSYNE 2023
Place Field Dynamics as a Window on Synaptic Plasticity in the Hippocampus
COSYNE 2023
Stimulus selection and novelty detection via divergent synaptic plasticity in an olfactory circuit
COSYNE 2023
A family of synaptic plasticity rules shapes triplet motifs in recurrent networks
COSYNE 2025
Inhibitory synaptic plasticity allows disinhibitory recall of overlapping excitatory-inhibitory assemblies
COSYNE 2025
Systematic analysis of meta-learned synaptic plasticity rules reveals degeneracy and fragility
COSYNE 2025
Top-down modulation shapes the timescales of cortical circuits via synaptic plasticity
COSYNE 2025
40-Hz optogenetic stimulation rescues functional synaptic plasticity after stroke
FENS Forum 2024
An all-optical approach to disentangle the role of intrinsic and synaptic plasticity in sensorimotor learning
FENS Forum 2024
Amyloid-β production and function are needed for cyclic nucleotides-mediated enhancement of synaptic plasticity and memory
FENS Forum 2024
Astrocytic Foxo1 regulates hippocampal spinogenesis and synaptic plasticity and enhances fear memory
FENS Forum 2024
Astrocytic CB1 receptor effect upon synaptic plasticity in the medial prefrontal cortex is modulated by adenosine receptors
FENS Forum 2024
BDNF-induced synaptic plasticity: The role of mitochondrial fission
FENS Forum 2024
Cannabidiol modulated compensation of radiation-induced alterations in hippocampal synaptic plasticity and neuronal function
FENS Forum 2024
Changes in endocannabinoid-dependent synaptic plasticity in CA1 hippocampus of a mouse model of temporal lobe epilepsy
FENS Forum 2024
Characterising the role of Gadd45ɑ in mRNA stability in the context of synaptic plasticity
FENS Forum 2024
Chemogenetic activation of Gq in microglia leads to deficits in synaptic plasticity and neuronal communication
FENS Forum 2024
Contactin-2: Myelination dynamics and synaptic plasticity in hippocampal interneurons
FENS Forum 2024
D1/D5 dopamine receptors support postsynaptic long-term GABAergic synaptic plasticity in the hippocampus
FENS Forum 2024
Distinct frequency-dependent synaptic plasticity and NMDAR subunit content in the supra- and infrapyramidal blade of the dentate gyrus in freely behaving animals
FENS Forum 2024
Diurnal variations in the contribution of mGlu5 receptors to hippocampal synaptic plasticity
FENS Forum 2024
The dynamic nature of memory: Heterosynaptic plasticity and the temporal rules of memory cooperation and competition
FENS Forum 2024
The effect of ergothioneine on synaptic plasticity in the hippocampal CA1 region using Alzheimer’s disease mouse model
FENS Forum 2024
Epitranscriptomic regulation of synaptic plasticity via novel pharmacological tools
FENS Forum 2024