Biology subjects

Clopath, C.

Publications and source records attributed to Clopath, C..

At least 73 records · Page 4Linked to original sources

bioRxiv2021

A rapid and efficient learning rule for biological neural circuits

The dominant view in neuroscience is that changes in synaptic weights underlie learning. It is unclear, however, how the brain is able to determine which synapses should change, and by how much. This uncertainty stands in sharp contrast to deep learning, where changes in weights are explicitly engineered to optimize performance. However, the main tool for that, backpropagation, has two problems. One is neuro-science related: it is not biologically plausible. The other is inherent: networks trained with this rule tend to forget old tasks when learning new ones. Here we introduce the Dendritic Gated Network (DGN), a variant of the Gated Linear Network, which offers a biologically plausible alternative to backpropagation. DGNs combine dendritic gating (whereby interneurons target dendrites to shape neuronal responses) with local learning rules to yield provably efficient performance. They are significantly more data efficient than conventional artificial networks, and are highly resistant to forgetting. Consequently, they perform well on a variety of tasks, in some cases better than backpropagation. Importantly, DGNs have structural and functional similarities to the cerebellum, a link that we strengthen by using in vivo two-photon calcium imaging to show that single interneurons suppress activity in individual dendritic branches of Purkinje cells, a key feature of the model. Thus, DGNs leverage targeted dendritic inhibition and local learning - two features ubiquitous in the brain - to achieve fast and efficient learning.

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bioRxiv2021

Dopamine and serotonin interplay for valence-based spatial learning

Dopamine and serotonin are important modulators of synaptic plasticity and their action has been linked to our ability to learn the positive or negative outcomes or valence learning. In the hippocampus, both neuromodulators affect long-term synaptic plasticity but play different roles in the encoding of uncertainty or predicted reward. Here, we examine the differential role of these modulators on learning speed and cognitive flexibility in a navigational model. We compare two reward-modulated spike time-dependent plasticity (R-STDP) learning rules to describe the action of these neuromodulators. Our results show that the interplay of dopamine (DA) and serotonin (5-HT) improves overall learning performance and can explain experimentally reported differences in spatial task performance. Furthermore, this system allows us to make predictions regarding spatial reversal learning.

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bioRxiv2021

Learning and attention increase visual response selectivity through distinct mechanisms

Selectivity of cortical neurons for sensory stimuli can increase across days as animals learn their behavioral relevance, and across seconds when animals switch attention. While both phenomena are expressed in the same cortical circuit, it is unknown whether they rely on similar mechanisms. We imaged activity of the same neuronal populations in primary visual cortex as mice learned a visual discrimination task and subsequently performed an attention switching task. Selectivity changes due to learning and attention were uncorrelated in individual neurons. Selectivity increases after learning mainly arose from selective suppression of responses to one of the task relevant stimuli but from selective enhancement and suppression during attention. Learning and attention differentially affected interactions between excitatory and PV, SOM and VIP inhibitory cell classes. Circuit modelling revealed that cell class-specific top-down inputs best explained attentional modulation, while the reorganization of local functional connectivity accounted for learning related changes. Thus, distinct mechanisms underlie increased discriminability of relevant sensory stimuli across longer and shorter time scales.

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bioRxiv2020

Coordinated hippocampal-thalamic-cortical communication crucial for engram dynamics underneath systems consolidation

Systems consolidation refers to the reorganization of memory over time across brain regions. Despite recent advancements in unravelling engrams and circuits essential for this process, the exact mechanisms behind engram cell dynamics and the role of associated pathways remain poorly understood. Here, we propose a computational model to address this knowledge gap that consists of a multi-region spiking recurrent neural network subject to biologically-plausible synaptic plasticity mechanisms. By coordinating the timescales of synaptic plasticity throughout the network and incorporating a hippocampus-thalamus-cortex circuit, our model is able to couple engram reactivations across these brain regions and thereby reproduce key dynamics of cortical and hippocampal engram cells along with their interdependencies. Decoupling hippocampal-thalamic-cortical activity disrupts engram dynamics and systems consolidation. Our modeling work also yields several testable predictions: engram cells in mediodorsal thalamus are activated in response to partial cues in recent and remote recall and are crucial for systems consolidation; hippocampal and thalamic engram cells are essential for coupling engram reactivations between subcortical and cortical regions; inhibitory engram cells have region-specific dynamics with coupled reactivations; inhibitory input to mediodorsal thalamus is critical for systems consolidation; and thalamocortical synaptic coupling is predictive of cortical engram dynamics and the retrograde amnesia pattern induced by hippocampal damage. Overall, our results suggest that systems consolidation emerges from concerted interactions among engram cells in distributed brain regions enabled by coordinated synaptic plasticity timescales in multisynaptic subcortical-cortical circuits.

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bioRxiv2020

Free recall scaling laws and short-term memory effects in a latching attractor network

Despite the complexity of human memory, paradigms like free recall have revealed robust qualitative and quantitative characteristics, such as power laws governing recall capacity. Although abstract random matrix models could explain such laws, the possibility of their implementation in large networks of interacting neurons has so far remained unexplored. We study an attractor network model of long-term memory endowed with firing rate adaptation and global inhibition. Under appropriate conditions, the transitioning behaviour of the network from memory to memory is constrained by limit cycles that prevent the network from recalling all memories, with scaling similar to what has been found in experiments. When the model is supplemented with a heteroassociative learning rule, complementing the standard autoassociative learning rule, as well as short-term synaptic facilitation, our model reproduces other key findings in the free recall literature, namely serial position effects, contiguity and forward asymmetry effects, as well as the semantic effects found to guide memory recall. The model is consistent with a broad series of manipulations aimed at gaining a better understanding of the variables that affect recall, such as the role of rehearsal, presentation rates and (continuous/end-of-list) distractor conditions. We predict that recall capacity may be increased with the addition of small amounts of noise, for example in the form of weak random stimuli during recall. Moreover, we predict that although the statistics of the encoded memories has a strong effect on the recall capacity, the power laws governing recall capacity may still be expected to hold.

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bioRxiv2020

Voltage-based inhibitory synaptic plasticity: network regulation, diversity, and flexibility

Neural networks are highly heterogeneous while homeostatic mechanisms ensure that this heterogeneity is kept within a physiologically safe range. One of such homeostatic mechanisms, inhibitory synaptic plasticity, has been observed across different brain regions. Computationally, however, inhibitory synaptic plasticity models often lead to a strong suppression of neuronal diversity. Here, we propose a model of inhibitory synaptic plasticity in which synaptic updates depend on presynaptic spike arrival and postsynaptic membrane voltage. Our plasticity rule regulates the network activity by setting a target value for the postsynaptic membrane potential over a long timescale. In a feedforward network, we show that our voltage-dependent inhibitory synaptic plasticity (vISP) model regulates the excitatory/inhibitory ratio while allowing for a broad range of postsynaptic firing rates and thus network diversity. In a feedforward network in which excitatory and inhibitory neurons receive correlated input, our plasticity model allows for the development of co-tuned excitation and inhibition, in agreement with recordings in rat auditory cortex. In recurrent networks, our model supports memory formation and retrieval while allowing for the development of heterogeneous neuronal activity. Finally, we implement our vISP rule in a model of the hippocampal CA1 region whose pyramidal cell excitability differs across cells. This model accounts for the experimentally observed variability in pyramidal cell features such as the number of place fields, the fields sizes, and the portion of the environment covered by each cell. Importantly, our model supports a combination of sparse and dense coding in the hippocampus. Therefore, our voltage-dependent inhibitory plasticity model accounts for network homeostasis while allowing for diverse neuronal dynamics observed across brain regions.

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bioRxiv2020

Stability by gating plasticity in recurrent neural networks

With Hebbian learning who fires together wires together, well-known problems arise. Hebbian plasticity can cause unstable network dynamics and overwrite stored memories. Unstable dynamics can partly be addressed with homeostatic plasticity mechanisms. Unfortunately, the time constants of homeostatic mechanisms required in network models are much shorter than those measured experimentally. We propose that homeostatic time constants can be slow if plasticity is gated. We investigate how gating plasticity influences network stability and memories in plastic balanced spiking networks of neurons with dendrites. We compare how different factors such as excitability, learning rate, and inhibition lift the requirements for homeostatic time constants. We investigate how dendritic versus perisomatic gating allows for different amounts of weight changes in stable networks. We suggest that the compartmentalisation of pyramidal cells enables dendritic synaptic changes while maintaining stability. We show that spatially restricted plasticity improves stability. Finally, we compare how different gates protect memories. Significance statementHow does the brain maintain stable neural activity in the presence of synaptic changes? This question has been studied extensively in the past, but we argue that one crucial aspect is missing in previous studies. While all theoretical work has assumed plasticity to be on all the time, plasticity is in fact heavily gated. In this light, we must reconsider the theories on stability and homeostasis of neural activity. In particular, theoretical studies show that neural networks undergoing plasticity require fast compensatory homeostatic mechanisms to be stable. However, experimentally measured homeostatic processes operate on much slower time scales. We studied how the gating of plasticity can improve network stability and thereby reduce the discrepancy in the homeostatic time constant between models and experiments.

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bioRxiv2020

Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons

Sequential behaviour is often compositional and organised across multiple time scales: a set of individual elements developing on short time scales (motifs) are combined to form longer functional sequences (syntax). Such organisation leads to a natural hierarchy that can be used advantageously for learning, since the motifs and the syntax can be acquired independently. Despite mounting experimental evidence for hierarchical structures in neuroscience, models for temporal learning based on neuronal networks have mostly focused on serial methods. Here, we introduce a network model of spiking neurons with a hierarchical organisation aimed at sequence learning on multiple time scales. Using biophysically motivated neuron dynamics and local plasticity rules, the model can learn motifs and syntax independently. Furthermore, the model can relearn sequences efficiently and store multiple sequences. Compared to serial learning, the hierarchical model displays faster learning, more flexible relearning, increased capacity, and higher robustness to perturbations. The hierarchical model redistributes the variability: it achieves high motif fidelity at the cost of higher variability in the between-motif timings.

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bioRxiv2020

Network-centered homeostasis through inhibition maintains hippocampal spatial map and cortical circuit function

Despite ongoing experiential change, neural activity maintains remarkable stability. Such stability is thought to be mediated by homeostatic plasticity and is deemed to be critical for normal neural function. However, what aspect of neural activity does homeostatic plasticity conserve, and how it still maintains the flexibility necessary for learning and memory, is not fully understood. Homeostatic plasticity is often studied in the context of neuron-centered control, where the deviations from the target activity for each individual neuron are suppressed. However, experimental studies suggest that there are additional, network-centered mechanisms. These may act through the inhibitory neurons, due to their dense network connectivity. Here we use a computational framework to study a potential mechanism for such homeostasis, using experimentally inspired, input-dependent inhibitory plasticity (IDIP). In a hippocampal CA1 spiking model, we show that IDIP in combination with place tuned input can explain the formation of active and silent place cells, as well as place cells remapping following optogenetic silencing of active place cells. Furthermore, we show that IDIP can also stabilise recurrent network dynamics, as well as preserve network firing rate heterogeneity and stimulus representation. Interestingly, in an associative memory task, IDIP facilitates persistent activity after memory encoding, in line with some experimental data. Hence, the establishment of global network balance with IDIP has diverse functional implications and may be able to explain experimental phenomena across different brain areas.

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bioRxiv2020

Neuronal activity in sensory cortex predicts the specificity of learning.

Learning to avoid dangerous signals while preserving normal responses to safe stimuli is essential for everyday behavior and survival. Fear learning has a high level of inter-subject variability. Following identical experiences, subjects exhibit fear specificities ranging from high (specializing fear to only the dangerous stimulus) to low (generalizing fear to safe stimuli). Pathological fear generalization underlies emotional disorders, such as post-traumatic stress disorder. The neuronal basis of fear specificity remains unknown. Here, we identified the neuronal code that underlies inter-subject variability in fear specificity using longitudinal imaging of neuronal activity before and after differential fear conditioning in the auditory cortex of mice. Neuronal activity prior to, but not after learning predicted the level of specificity following fear conditioning across subjects. Stimulus representation in auditory cortex was reorganized following conditioning. However, the reorganized neuronal activity did not relate to the specificity of learning. These results present a novel neuronal code that determines individual patterns in learning.

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bioRxiv2020

Transient and layer-specific reduction in neocortical PV inhibition during sensory association learning

Sensory and motor learning reorganizes neocortical circuitry, particularly manifested in the strength of excitatory synapses. Prior studies suggest reduced inhibition can facilitate glutamatergic synapse plasticity during learning, but the role of specific inhibitory neurons in this process has not been well-documented. Here we investigate whether inhibition from parvalbumin (PV)-expressing neurons is altered in primary somatosensory cortex in mice trained in a whisker-based reward-association task. Anatomical and electrophysiological analyses show PV input to L2/3, but not L5, pyramidal (Pyr) neurons is rapidly suppressed during early stages of sensory training, effects that are reversed after longer training periods. Importantly, sensory stimulation without reward does not alter PV-mediated inhibition. Computational modeling indicates that reduced PV inhibition in L2/3 selectively enables an increase in translaminar recurrent activity, also observed during SAT. PV disinhibition in superficial layers of the neocortex may be one of the earliest changes in learning-dependent rewiring of the cortical column. Impact statementTactile learning is associated with reduced PV inhibition in superficial layers of somatosensory cortex. Modeling studies suggest that PV disinhibition can support prolonged recurrent activity initiated by thalamic input.

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bioRxiv2020

Neural manifold under plasticity in a goal driven learning behaviour

Neural activity is often low dimensional and dominated by only a few prominent neural covariation patterns. It has been hypothesised that these covariation patterns could form the building blocks used for fast and flexible motor control. Supporting this idea, recent experiments have shown that monkeys can learn to adapt their neural activity in motor cortex on a timescale of minutes, given that the change lies within the original low-dimensional subspace, also called neural manifold. However, the neural mechanism underlying this within-manifold adaptation remains unknown. Here, we show in a computational model that modification of recurrent weights, driven by a learned feedback signal, can account for the observed behavioural difference between within- and outside-manifold learning. Our findings give a new perspective, showing that recurrent weight changes do not necessarily lead to change in the neural manifold. On the contrary, successful learning is naturally constrained to a common subspace.

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bioRxiv2020

Theory of Neuronal Perturbome: Linking Connectivity to Coding via Perturbations

To unravel the functional properties of the brain, we need to untangle how neurons interact with each other and coordinate in large-scale recurrent networks. One way to address this question is to measure the functional influence of individual neurons on each other by perturbing them in vivo. Application of such single-neuron perturbations in mouse visual cortex has recently revealed feature-specific suppression between excitatory neurons, despite the presence of highly specific excitatory connectivity, which was deemed to underlie feature-specific amplification. Here, we studied which connectivity profiles are consistent with these seemingly contradictory observations, by modelling the effect of single-neuron perturbations in large-scale neuronal networks. Our numerical simulations and mathematical analysis revealed that, contrary to the prima facie assumption, neither inhibition-dominance nor broad inhibition alone were sufficient to explain the experimental findings; instead, strong and functionally specific excitatory-inhibitory connectivity was necessary, consistent with recent findings in the primary visual cortex of rodents. Such networks had a higher capacity to encode and decode natural images in turn, which was accompanied by the emergence of response gain nonlinearities at the population level. Our study provides a general computational framework to investigate how single-neuron perturbations are linked to cortical connectivity and sensory coding, and paves the road to map the perturbome of neuronal networks in future studies.

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bioRxiv2020

The dendritic spatial code: branch-specific place tuning and its experience-dependent decoupling

Dendrites of pyramidal neurons integrate different sensory inputs, and non-linear dendritic computations drive feature selective tuning and plasticity. Yet little is known about how dendrites themselves represent the environment, the degree to which they are coupled to their soma, and how that coupling is sculpted with experience. In order to answer these questions, we developed a novel preparation in which we image soma and connected dendrites in a single plane across days using in vivo two-photon microscopy. Using this preparation, we monitored spatially tuned activity in area CA3 of the hippocampus in head-fixed mice running on a linear track. We identified "place dendrites", which can stably and precisely represent both familiar and novel spatial environments. Dendrites could display place tuning independent of their connected soma and even their sister dendritic branches, the first evidence for branch-specific tuning in the hippocampus. In a familiar environment, spatially tuned somata were more decoupled from their dendrites as compared to non-tuned somata. This relationship was absent in a novel environment, suggesting an experience dependent selective gating of dendritic spatial inputs. We then built a data-driven multicompartment computational model that could capture the experimentally observed correlations. Our model predicts that place cells exhibiting branch-specific tuning have more flexible place fields, while neurons with homogenous or co-tuned dendritic branches have higher place field stability. These findings demonstrate that spatial representation is organized in a branch-specific manner within dendrites of hippocampal pyramidal cells. Further, spatial inputs from dendrites to soma are selectively and dynamically gated in an experience-dependent manner, endowing both flexibility and stability to the cognitive map of space. One sentence summaryHippocampal pyramidal cells show branch-specific tuning for different place fields, and their coupling to their soma changes with experience of an environment.

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bioRxiv2019

Interneuron-specific plasticity at parvalbumin and somatostatin inhibitory synapses onto CA1 pyramidal neurons shapes hippocampal output

The formation and maintenance of spatial representations within hippocampal cell assemblies is strongly dictated by patterns of inhibition from diverse interneuron populations. Although it is known that inhibitory synaptic strength is malleable, induction of long-term plasticity at distinct inhibitory synapses and its regulation of hippocampal network activity is not well understood. Here, we show that inhibitory synapses from parvalbumin and somatostatin expressing interneurons undergo long-term depression and potentiation respectively (PV-iLTD and SST-iLTP) during physiological activity patterns. Both forms of plasticity rely on T-type calcium channel activation to confer synapse specificity but otherwise employ distinct mechanisms. Since parvalbumin and somatostatin interneurons preferentially target perisomatic and distal dendritic regions respectively of CA1 pyramidal cells, PV-iLTD and SST-iLTP coordinate a reprioritisation of excitatory inputs from entorhinal cortex and CA3. Furthermore, circuit-level modelling reveals that PV-iLTD and SST-iLTP cooperate to stabilise place cells while facilitating representation of multiple unique environments within the hippocampal network.

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bioRxiv2019

Learning spatiotemporal signals using a recurrent spiking network that discretizes time

Learning to produce spatiotemporal sequences is a common task the brain has to solve. The same neural substrate may be used by the brain to produce different sequential behaviours. The way the brain learns and encodes such tasks remains unknown as current computational models do not typically use realistic biologically-plausible learning. Here, we propose a model where a spiking recurrent network of excitatory and inhibitory biophysical neurons drives a read-out layer: the dynamics of the recurrent network is constrained to encode time while the read-out neurons encode space. Space is then linked with time through plastic synapses that follow common Hebbian learning rules. We demonstrate that the model is able to learn spatiotemporal dynamics on a timescale that is behaviourally relevant. Learned sequences are robustly replayed during a regime of spontaneous activity.\n\nAuthor summaryThe brain has the ability to learn flexible behaviours on a wide range of time scales. Previous studies have successfully build spiking network models that learn a variety of computational tasks. However, often the learning involved is not local. Here, we investigate a model using biological-plausible plasticity rules for a specific computational task: spatiotemporal sequence learning. The architecture separates time and space into two different parts and this allows learning to bind space to time. Importantly, the time component is encoded into a recurrent network which exhibits sequential dynamics on a behavioural time scale. This network is then used as an engine to drive spatial read-out neurons. We demonstrate that the model can learn complicated spatiotemporal spiking dynamics, such as the song of a bird, and replay the song robustly.

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bioRxiv2018

Inhibitory microcircuits for top-down plasticity of sensory representations

Humans and animals are remarkable at detecting stimuli that predict rewards. While the underlying neural mecha-nisms are unknown, reward influences plasticity of sensory representations in early sensory areas. The underlying changes in excitatory and inhibitory circuitry are however unclear. Recently, experimental findings suggest that the inhibitory circuits can regulate learning. In addition, the inhibitory neurons in superficial layers are highly modulated by diverse long-range inputs, including reward signals. We, therefore, hypothesise that plasticity of in-terneuron circuits plays a major role in adjusting stimulus representations. We investigate how top-down modulation by rewards can interact with local excitatory and inhibitory plasticity to induce long-lasting changes in sensory circuitry. Using a computational model of layer 2/3 primary visual cortex, we demonstrate how interneuron networks can store information about the rewarded stimulus to instruct long-term changes in excitatory connectivity in the absence of further reward. In our model, stimulus-tuned somatostatin-positive interneurons (SSTs) develop strong connections to parvalbumin-positive interneurons (PVs) during reward presentation such that they selectively dis-inhibit the pyramidal layer henceforth. This triggers plasticity in the excitatory neurons, which leads to increased stimulus representation. We make specific testable predictions in terms of the activity of different neuron types. We finally show that this two-stage model allows for translation invariance of the learned representation.

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bioRxiv2018

Interplay between somatic and dendritic inhibition promotes the emergence and stabilization of place fields

During exploration of novel environments, place fields are rapidly formed in hippocampal CA1 neurons. Place cell firing rate increases in early stages of exploration of novel environments but returns to baseline levels in familiar environments. However, although similar in amplitude and width, place fields in familiar environments are more stable than in novel environments. We propose a computational model of the hippocampal CA1 network, which describes the formation, the dynamics and the stabilization of place fields. We show that although somatic disinhibition is sufficient to form place cells, dendritic inhibition along with synaptic plasticity is necessary for stabilization. Our model suggests that place cell stability is due to large excitatory synaptic weights and large dendritic inhibition. We show that the interplay between somatic and dendritic inhibition balances the increased excitatory weights, so that place cells return to their baseline firing rate after exploration. Our model suggests that different types of interneurons are essential to unravel the mechanisms underlying place field plasticity. Finally, we predict that artificial induced dendritic events can shift place fields even after place field stabilization.

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