Imperial College London

Professor Claudia Clopath

Faculty of EngineeringDepartment of Bioengineering

Professor of Computational Neuroscience
 
 
 
//

Contact

 

+44 (0)20 7594 1435c.clopath Website

 
 
//

Location

 

Royal School of Mines 4.09Royal School of MinesSouth Kensington Campus

//

Summary

 

Publications

Publication Type
Year
to

86 results found

Cone I, Clopath C, 2024, Latent representations in hippocampal network model co-evolve with behavioral exploration of task structure, Nature Communications, Vol: 15, ISSN: 2041-1723

To successfully learn real-life behavioral tasks, animals must pair actions or decisions to the task’s complex structure, which can depend on abstract combinations of sensory stimuli and internal logic. The hippocampus is known to develop representations of this complex structure, forming a so-called “cognitive map”. However, the precise biophysical mechanisms driving the emergence of task-relevant maps at the population level remain unclear. We propose a model in which plateau-based learning at the single cell level, combined with reinforcement learning in an agent, leads to latent representational structures codependently evolving with behavior in a task-specific manner. In agreement with recent experimental data, we show that the model successfully develops latent structures essential for task-solving (cue-dependent “splitters”) while excluding irrelevant ones. Finally, our model makes testable predictions concerning the co-dependent interactions between split representations and split behavioral policy during their evolution.

Journal article

Feitosa Tomé D, Zhang Y, Aida T, Mosto O, Lu Y, Chen M, Sadeh S, Roy DS, Clopath Cet al., 2024, Dynamic and selective engrams emerge with memory consolidation, Nature Neuroscience, ISSN: 1097-6256

Episodic memories are encoded by experience-activated neuronal ensembles that remain necessary and sufficient for recall. However, the temporal evolution of memory engrams after initial encoding is unclear. In this study, we employed computational and experimental approaches to examine how the neural composition and selectivity of engrams change with memory consolidation. Our spiking neural network model yielded testable predictions: memories transition from unselective to selective as neurons drop out of and drop into engrams; inhibitory activity during recall is essential for memory selectivity; and inhibitory synaptic plasticity during memory consolidation is critical for engrams to become selective. Using activity-dependent labeling, longitudinal calcium imaging and a combination of optogenetic and chemogenetic manipulations in mouse dentate gyrus, we conducted contextual fear conditioning experiments that supported our model’s predictions. Our results reveal that memory engrams are dynamic and that changes in engram composition mediated by inhibitory plasticity are crucial for the emergence of memory selectivity.

Journal article

Asabuki T, Clopath C, 2024, Embedding stochastic dynamics of the environment in spontaneous activity by prediction-based plasticity, eLife, ISSN: 2050-084X

Journal article

Gonzalo Cogno S, Obenhaus HA, Lautrup A, Jacobsen RI, Clopath C, Andersson SO, Donato F, Moser M-B, Moser EIet al., 2024, Minute-scale oscillatory sequences in medial entorhinal cortex, Nature, Vol: 625, Pages: 338-344, ISSN: 0028-0836

The medial entorhinal cortex (MEC) hosts many of the brain's circuit elements for spatial navigation and episodic memory, operations that require neural activity to be organized across long durations of experience1. Whereas location is known to be encoded by spatially tuned cell types in this brain region2,3, little is known about how the activity of entorhinal cells is tied together over time at behaviourally relevant time scales, in the second-to-minute regime. Here we show that MEC neuronal activity has the capacity to be organized into ultraslow oscillations, with periods ranging from tens of seconds to minutes. During these oscillations, the activity is further organized into periodic sequences. Oscillatory sequences manifested while mice ran at free pace on a rotating wheel in darkness, with no change in location or running direction and no scheduled rewards. The sequences involved nearly the entire cell population, and transcended epochs of immobility. Similar sequences were not observed in neighbouring parasubiculum or in visual cortex. Ultraslow oscillatory sequences in MEC may have the potential to couple neurons and circuits across extended time scales and serve as a template for new sequence formation during navigation and episodic memory formation.

Journal article

Myers-Joseph D, Wilmes KA, Fernandez-Otero M, Clopath C, Khan AGet al., 2023, Disinhibition by VIP interneurons is orthogonal to cross-modal attentional modulation in primary visual cortex, Neuron, ISSN: 0896-6273

Attentional modulation of sensory processing is a key feature of cognition; however, its neural circuit basis is poorly understood. A candidate mechanism is the disinhibition of pyramidal cells through vasoactive intestinal peptide (VIP) and somatostatin (SOM)-positive interneurons. However, the interaction of attentional modulation and VIP-SOM disinhibition has never been directly tested. We used all-optical methods to bi-directionally manipulate VIP interneuron activity as mice performed a cross-modal attention-switching task. We measured the activities of VIP, SOM, and parvalbumin (PV)-positive interneurons and pyramidal neurons identified in the same tissue and found that although activity in all cell classes was modulated by both attention and VIP manipulation, their effects were orthogonal. Attention and VIP-SOM disinhibition relied on distinct patterns of changes in activity and reorganization of interactions between inhibitory and excitatory cells. Circuit modeling revealed a precise network architecture consistent with multiplexing strong yet non-interacting modulations in the same neural population.

Journal article

Radulescu CI, Doostdar N, Zabouri N, Melgosa-Ecenarro L, Wang X, Sadeh S, Pavlidi P, Airey J, Kopanitsa M, Clopath C, Barnes SJet al., 2023, Age-related dysregulation of homeostatic control in neuronal microcircuits, Nature Neuroscience, Vol: 26, Pages: 2158-2170, ISSN: 1097-6256

Neuronal homeostasis prevents hyperactivity and hypoactivity. Age-related hyperactivity suggests homeostasis may be dysregulated in later life. However, plasticity mechanisms preventing age-related hyperactivity and their efficacy in later life are unclear. We identify the adult cortical plasticity response to elevated activity driven by sensory overstimulation, then test how plasticity changes with age. We use in vivo two-photon imaging of calcium-mediated cellular/synaptic activity, electrophysiology and c-Fos-activity tagging to show control of neuronal activity is dysregulated in the visual cortex in late adulthood. Specifically, in young adult cortex, mGluR5-dependent population-wide excitatory synaptic weakening and inhibitory synaptogenesis reduce cortical activity following overstimulation. In later life, these mechanisms are downregulated, so that overstimulation results in synaptic strengthening and elevated activity. We also find overstimulation disrupts cognition in older but not younger animals. We propose that specific plasticity mechanisms fail in later life dysregulating neuronal microcircuit homeostasis and that the age-related response to overstimulation can impact cognitive performance.

Journal article

Maes A, Barahona M, Clopath C, 2023, Long- and short-term history effects in a spiking network model of statistical learning, Scientific Reports, Vol: 13, Pages: 1-14, ISSN: 2045-2322

The statistical structure of the environment is often important when making decisions. There are multiple theories of howthe brain represents statistical structure. One such theory states that neural activity spontaneously samples from probabilitydistributions. In other words, the network spends more time in states which encode high-probability stimuli. Starting fromthe neural assembly, increasingly thought of to be the building block for computation in the brain, we focus on how arbitraryprior knowledge about the external world can both be learned and spontaneously recollected. We present a model basedupon learning the inverse of the cumulative distribution function. Learning is entirely unsupervised using biophysical neuronsand biologically plausible learning rules. We show how this prior knowledge can then be accessed to compute expectationsand signal surprise in downstream networks. Sensory history effects emerge from the model as a consequence of ongoinglearning.

Journal article

Gallinaro J, Scholl B, Clopath C, 2023, Synaptic weights that correlate with presynaptic selectivity increase decoding performance, PLoS Computational Biology, Vol: 19, Pages: 1-18, ISSN: 1553-734X

The activity of neurons in the visual cortex is often characterized by tuning curves, which are thought to be shaped by Hebbian plasticity during development and sensory experience. This leads to the prediction that neural circuits should be organized such that neurons with similar functional preference are connected with stronger weights. In support of this idea, previous experimental and theoretical work have provided evidence for a model of the visual cortex characterized by such functional subnetworks. A recent experimental study, however, have found that the postsynaptic preferred stimulus was defined by the total number of spines activated by a given stimulus and independent of their individual strength. While this result might seem to contradict previous literature, there are many factors that define how a given synaptic input influences postsynaptic selectivity. Here, we designed a computational model in which postsynaptic functional preference is defined by the number of inputs activated by a given stimulus. Using a plasticity rule where synaptic weights tend to correlate with presynaptic selectivity, and is independent of functional-similarity between pre- and postsynaptic activity, we find that this model can be used to decode presented stimuli in a manner that is comparable to maximum likelihood inference.

Journal article

Rigby M, Grillo FW, Compans B, Neves G, Gallinaro J, Nashashibi S, Horton S, Machado PMP, Carbajal MA, Vizcay-Barrena G, Levet F, Sibarita J-B, Kirkland A, Fleck RA, Clopath C, Burrone Jet al., 2023, Multi-synaptic boutons are a feature of CA1 hippocampal connections in the stratum oriens, CELL REPORTS, Vol: 42, ISSN: 2211-1247

Journal article

Wilmes K, Clopath C, 2023, Dendrites help mitigate the plasticity-stability dilemma, Scientific Reports, Vol: 13, Pages: 1-15, ISSN: 2045-2322

With Hebbian learning ‘who fires together wires together’, well-known problems arise. Hebbian plasticity can cause unstable network dynamics and overwrite stored memories. Because the known homeostatic plasticity mechanisms tend to be too slow to combat unstable dynamics, it has been proposed that plasticity must be highly gated and synaptic strengths limited. While solving the issue of stability, gating and limiting plasticity does not solve the stability-plasticity dilemma. We propose that dendrites enable both stable network dynamics and considerable synaptic changes, as they allow the gating of plasticity in a compartment-specific manner. We investigate how gating plasticity influences network stability in plastic balanced spiking networks of neurons with dendrites. We compare how different ways to gate plasticity, namely via modulating excitability, learning rate, and inhibition increase stability. 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 the coupling between dendrite and soma is critical for the plasticity-stability trade-off. Finally, we show that spatially restricted plasticity additionally improves stability.

Journal article

Delamare G, Zaki Y, Cai DJ, Clopath Cet al., 2023, Drift of neural ensembles driven by slow fluctuations of intrinsic excitability, eLife, ISSN: 2050-084X

Representational drift refers to the dynamic nature of neural representations in the brain despitethe behavior being seemingly stable. Although drift has been observed in many different brain regions, the mechanisms underlying it are not known. Since intrinsic neural excitability is suggested toplay a key role in regulating memory allocation, fluctuations of excitability could bias the reactivation of previously stored memory ensembles and therefore act as a motor for drift. Here, we proposea rate-based plastic recurrent neural network with slow fluctuations of intrinsic excitability. We firstshow that subsequent reactivations of a neural ensemble can lead to drift of this ensemble. Themodel predicts that drift is induced by co-activation of previously active neurons along with neuronswith high excitability which leads to remodelling of the recurrent weights. Consistent with previousexperimental works, the drifting ensemble is informative about its temporal history. Crucially, weshow that the gradual nature of the drift is necessary for decoding temporal information from theactivity of the ensemble. Finally, we show that the memory is preserved and can be decoded by anoutput neuron having plastic synapses with the main region.

Journal article

Zador A, Escola S, Richards B, Ölveczky B, Bengio Y, Boahen K, Botvinick M, Chklovskii D, Churchland A, Clopath C, DiCarlo J, Ganguli S, Hawkins J, Körding K, Koulakov A, LeCun Y, Lillicrap T, Marblestone A, Olshausen B, Pouget A, Savin C, Sejnowski T, Simoncelli E, Solla S, Sussillo D, Tolias AS, Tsao Det al., 2023, Catalyzing next-generation Artificial Intelligence through NeuroAI, Nature Communications, Vol: 14, Pages: 1-7, ISSN: 2041-1723

Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in NeuroAI. A core component of this is the embodied Turing test, which challenges AI animal models to interact with the sensorimotor world at skill levels akin to their living counterparts. The embodied Turing test shifts the focus from those capabilities like game playing and language that are especially well-developed or uniquely human to those capabilities - inherited from over 500 million years of evolution - that are shared with all animals. Building models that can pass the embodied Turing test will provide a roadmap for the next generation of AI.

Journal article

Bono J, Zannone S, Pedrosa V, Clopath Cet al., 2023, Learning predictive cognitive maps with spiking neurons during behaviour and replays, eLife, Vol: 12, ISSN: 2050-084X

The hippocampus has been proposed to encode environments using a representation that contains predictive information about likely future states, called the successor representation. However, it is not clear how such a representation could be learned in the hippocampal circuit. Here, we propose a plasticity rule that can learn this predictive map of the environment using a spiking neural network. We connect this biologically plausible plasticity rule to reinforcement learning, mathematically and numerically showing that it implements the TD-lambda algorithm. By spanning these different levels, we show how our framework naturally encompasses behavioral activity and replays, smoothly moving from rate to temporal coding, and allows learning over behavioral timescales with a plasticity rule acting on a timescale of milliseconds. We discuss how biological parameters such as dwelling times at states, neuronal firing rates and neuromodulation relate to the delay discounting parameter of the TD algorithm, and how they influence the learned representation. We also find that, in agreement with psychological studies and contrary to reinforcement learning theory, the discount factor decreases hyperbolically with time. Finally, our framework suggests a role for replays, in both aiding learning in novel environments and finding shortcut trajectories that were not experienced during behavior, in agreement with experimental data.

Journal article

Fuchsberger T, Clopath C, Jarzebowski P, Brzosko Z, Wang H, Paulsen Oet al., 2022, Postsynaptic burst reactivation of hippocampal neurons enables associative plasticity of temporally discontiguous inputs, ELIFE, Vol: 11, ISSN: 2050-084X

Journal article

Feulner B, Perich MG, Chowdhury RH, Miller LE, Gallego JA, Clopath Cet al., 2022, Small, correlated changes in synaptic connectivity may facilitate rapid motor learning, Nature Communications, Vol: 13, ISSN: 2041-1723

Animals can rapidly adapt their movements to external perturbations. This adaptation is paralleled by changes in single neuron activity in the motor cortices. Behavioural and neural recording studies suggest that when animals learn to counteract a visuomotor perturbation, these changes originate from altered inputs to the motor cortices rather than from changes in local connectivity, as neural covariance is largely preserved during adaptation. Since measuring synaptic changes in vivo remains very challenging, weused a modular recurrent network model to compare the expected neural activity changes following learning through altered inputs (Hinput) and learning through local connectivity changes (Hlocal). Learning under Hinput produced small changes in neural activity and largely preserved the neural covariance, in good agreement with neural recordings in monkeys. Surprisingly given the presumed dependence of stable neural covariance onpreserved circuit connectivity, Hlocal led to only slightly larger changes in neural activity and covariance compared to Hinput. This similarity is due to Hlocal only requiring small, correlated connectivity changes to counteract the perturbation, which provided the network with significant robustness against simulated synaptic noise. Simulations of tasks that impose increasingly larger behavioural changes revealed a growing difference betweenHinput and Hlocal, which could be exploited when designing future experiments.

Journal article

Sadeh S, Clopath C, 2022, Contribution of behavioural variability to representational drift, eLife, Vol: 11, Pages: 1-28, ISSN: 2050-084X

Neuronal responses to similar stimuli change dynamically over time, raising the question of how internal representations can provide a stable substrate for neural coding. Recent work has suggested a large degree of drift in neural representations even in sensory cortices, which are believed to store stable representations of the external world. While the drift of these representations is mostly characterized in relation to external stimuli, the behavioural state of the animal (for instance, the level of arousal) is also known to strongly modulate the neural activity. We therefore asked how the variability of such modulatory mechanisms can contribute to representational changes. We analysed large-scale recording of neural activity from the Allen Brain Observatory, which was used before to document representational drift in the mouse visual cortex. We found that, within these datasets, behavioural variability significantly contributes to representational changes. This effect was broadcasted across various cortical areas in the mouse, including the primary visual cortex, higher order visual areas, and even regions not primarily linked to vision like hippocampus. Our computational modelling suggests that these results are consistent with independent modulation of neural activity by behaviour over slower time scales. Importantly, our analysis suggests that reliable but variable modulation of neural representations by behaviour can be misinterpreted as representational drift, if neuronal representations are only characterized in the stimulus space and marginalised over behavioural parameters.

Journal article

Hashemi P, Clopath C, Reneaux M, Hersey M, Berger S, Mena S, Buchanan AM, Ou Y, Tavakoli N, Reagan Let al., 2022, A tale of two transmitters: serotonin and histamine as in vivo biomarkers of chronic stress in mice, Journal of Neuroinflammation, Vol: 19, ISSN: 1742-2094

Background: Stress-induced mental illnesses (mediated by neuroinflammation) pose one of the world’s most urgent public health challenges. A reliable in vivo chemical biomarker of stress would significantly improve the clinical communities’ diagnostic and therapeutic approaches to illnesses like depression. Methods: Male and female C57BL/6J mice underwent a chronic stress paradigm. We paired innovative in vivo serotonin and histamine voltammetric measurement technologies, behavioral testing, and cutting-edge mathematical methods to correlate chemistry to stress and behavior. Results: Inflammation-induced increases in hypothalamic histamine were co-measured withdecreased in vivo extracellular hippocampal serotonin in mice that underwent a chronic stress paradigm, regardless of behavioral phenotype. In animals with depression phenotypes, correlations were found between serotonin and the extent of behavioral indices of depression. We created a high accuracy algorithm that could predict whether animals had been exposed to stress or not based solely on the serotonin measurement. We next developed a model of serotonin and histamine modulation, which predicted that stress-induced neuroinflammation increases histaminergic activity, serving to inhibit serotonin. Finally, we created a mathematical index of stress, Si and predicted that during chronic stress, where Si is high, simultaneously increasing serotonin and decreasing histamine is the most effective chemical strategy to restoring serotonin to pre-stress levels. When we pursued this idea pharmacologically, our experiments were nearly identical to the model’s predictions. Conclusions: This work shines the light on two biomarkers of chronic stress, histamine and serotonin, and implies that both may be important in our future investigations of the pathology and treatment of inflammation-induced depression.

Journal article

Wert-Carvajal C, Reneaux M, Tchumatchenko T, Clopath Cet al., 2022, Dopamine and serotonin interplay for valence-based spatial learning, CELL REPORTS, Vol: 39, ISSN: 2211-1247

Journal article

Hertäg L, Clopath C, 2022, Prediction-error neurons in circuits with multiple neuron types: formation, refinement, and functional implications., Proceedings of the National Academy of Sciences of USA, Vol: 119, Pages: e2115699119-e2115699119, ISSN: 0027-8424

SignificanceAn influential idea in neuroscience is that neural circuits do not only passively process sensory information but rather actively compare them with predictions thereof. A core element of this comparison is prediction-error neurons, the activity of which only changes upon mismatches between actual and predicted sensory stimuli. While it has been shown that these prediction-error neurons come in different variants, it is largely unresolved how they are simultaneously formed and shaped by highly interconnected neural networks. By using a computational model, we study the circuit-level mechanisms that give rise to different variants of prediction-error neurons. Our results shed light on the formation, refinement, and robustness of prediction-error circuits, an important step toward a better understanding of predictive processing.

Journal article

Wood KC, Angeloni CF, Oxman K, Clopath C, Geffen MNet al., 2022, Neuronal activity in sensory cortex predicts the specificity of learning in mice, Nature Communications, Vol: 13, ISSN: 2041-1723

Learning to avoid dangerous signals while preserving normal responses to safe stimuli is essential for everyday behavior and survival. Following identical experiences, subjects exhibit fear specificity ranging from high (specializing fear to only the dangerous stimulus) to low (generalizing fear to safe stimuli), yet 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.

Journal article

Poort J, Wilmes KA, Blot A, Chadwick A, Sahani M, Clopath C, Mrsic-Flogel TD, Hofer SB, Khan AGet al., 2022, Learning and attention increase visual response selectivity through distinct mechanisms., Neuron, Vol: 110, Pages: 689-697.e6, ISSN: 0896-6273

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 occur in the same circuit, it is unknown whether they rely on similar mechanisms. We imaged 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 stimuli but from selective enhancement and suppression during attention. Learning and attention differentially affected interactions between excitatory and PV, SOM, and VIP inhibitory cells. Circuit modeling revealed that cell class-specific top-down inputs best explained attentional modulation, while reorganization of local functional connectivity accounted for learning-related changes. Thus, distinct mechanisms underlie increased discriminability of relevant sensory stimuli across longer and shorter timescales.

Journal article

Feitosa Tome D, Sadeh S, Clopath C, 2022, Coordinated hippocampal-thalamic-cortical communication crucial for engram dynamics underneath systems consolidation, Nature Communications, Vol: 13, ISSN: 2041-1723

Systems consolidation refers to the time-dependent reorganization of memory representations or engrams across brain regions. Despite recent advancements in unravelling this process, the exact mechanisms behind engram dynamics and the role of associated pathways remain largely unknown. Here we propose a biologically-plausible computational model to address this knowledge gap. By coordinating synaptic plasticity timescales and incorporating a hippocampus-thalamus-cortex circuit, our model is able to couple engram reactivations across these regions and thereby reproduce key dynamics of cortical and hippocampal engram cells along with their interdependencies. Decoupling hippocampal-thalamic-cortical activity disrupts systems consolidation. Critically, our model yields testable predictions regarding hippocampal and thalamic engram cells, inhibitory engrams, thalamic inhibitory input, and the effect of thalamocortical synaptic coupling on retrograde amnesia induced by hippocampal lesions. Overall, our results suggest that systems consolidation emerges from coupled reactivations of engram cells in distributed brain regions enabled by coordinated synaptic plasticity timescales in multisynaptic subcortical-cortical circuits.

Journal article

Lee S, Mannelli SS, Clopath C, Goldt S, Saxe Aet al., 2022, Maslow's Hammer for Catastrophic Forgetting: Node Re-Use vs Node Activation, 38th International Conference on Machine Learning (ICML), Publisher: JMLR-JOURNAL MACHINE LEARNING RESEARCH, ISSN: 2640-3498

Conference paper

Boboeva V, Clopath C, 2021, Free recall scaling laws and short-term memory effects in a latching attractor network, Proceedings of the National Academy of Sciences of USA, Vol: 118, Pages: 1-10, ISSN: 0027-8424

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 abstractrandom matrix models could explain such laws, the possibility oftheir implementation in large networks of interacting neurons has sofar remained underexplored. We study an attractor network modelof long-term memory endowed with firing rate adaptation and globalinhibition. Under appropriate conditions, the transitioning behaviourof the network from memory to memory is constrained by limit cyclesthat prevent the network from recalling all memories, with scalingsimilar to what has been found in experiments. When the model is supplemented with a heteroassociative learning rule, complementing thestandard autoassociative learning rule, as well as short-term synaptic facilitation, our model reproduces other key findings in the freerecall literature, namely serial position effects, contiguity and forwardasymmetry effects, as well as the semantic effects found to guidememory recall. The model is consistent with a broad series of manipulations aimed at gaining a better understanding of the variablesthat affect recall, such as the role of rehearsal, presentation ratesand (continuous/end-of-list) distractor conditions. We predict thatrecall capacity may be increased with the addition of small amountsof noise, for example in the form of weak random stimuli during recall. Finally, we predict that although the statistics of the encodedmemories has a strong effect on the recall capacity, the power lawsgoverning recall capacity may still be expected to hold.

Journal article

Geiller T, Sadeh S, Clopath C, Losonczy Aet al., 2021, Local circuit amplification of spatial selectivity in the hippocampus, Nature, Vol: 601, Pages: 105-109, ISSN: 0028-0836

Local circuit architecture facilitates the emergence of feature selectivity in the cerebral cortex1. In the hippocampus, it remains unknown whether local computations supported by specific connectivity motifs2 regulate the spatial receptive fields of pyramidal cells3. Here we developed an in vivo electroporation method for monosynaptic retrograde tracing4 and optogenetics manipulation at single-cell resolution to interrogate the dynamic interaction of place cells with their microcircuitry during navigation. We found a local circuit mechanism in CA1 whereby the spatial tuning of an individual place cell can propagate to a functionally recurrent subnetwork5 to which it belongs. The emergence of place fields in individual neurons led to the development of inverse selectivity in a subset of their presynaptic interneurons, and recruited functionally coupled place cells at that location. Thus, the spatial selectivity of single CA1 neurons is amplified through local circuit plasticity to enable effective multi-neuronal representations that can flexibly scale environmental features locally without degrading the feedforward input structure.

Journal article

Gallinaro JV, Clopath C, 2021, Memories in a network with excitatory and inhibitory plasticity are encoded in the spiking irregularity, PLoS Computational Biology, Vol: 17, Pages: 1-19, ISSN: 1553-734X

Cell assemblies are thought to be the substrate of memory in the brain. Theoretical studies have previously shown that assemblies can be formed in networks with multiple types of plasticity. But how exactly they are formed and how they encode information is yet to be fully understood. One possibility is that memories are stored in silent assemblies. Here we used a computational model to study the formation of silent assemblies in a network of spiking neurons with excitatory and inhibitory plasticity. We found that even though the formed assemblies were silent in terms of mean firing rate, they had an increased coefficient of variation of inter-spike intervals. We also found that this spiking irregularity could be read out with support of short-term plasticity, and that it could contribute to the longevity of memories.

Journal article

Sadeh S, Clopath C, 2021, Excitatory-inhibitory balance modulates the formation and dynamics of neuronal assemblies in cortical networks, Science Advances, Vol: 7, Pages: 1-16, ISSN: 2375-2548

Repetitive activation of subpopulations of neurons leads to the formation of neuronal assemblies, which can guide learning and behavior. Recent technological advances have made the artificial induction of such assemblies feasible, yet how various parameters of perturbation can be optimized for such induction is not clear. We found that the regime of cortical networks in terms of their excitatory-inhibitory balance can modulate the formation and dynamics of assemblies. Networks with dominant excitatory interactions enabled a fast formation of assemblies, and this was accompanied by recruitment of other non-perturbed neurons, thus leading to some degree of nonspecific assembly formation. On the other hand, strong excitatory-inhibitory interaction recruited lateral inhibition, which slowed down the formation of assemblies but constrained them to the perturbed neurons. Our results suggest that these two regimes can be suitable for different computational and cognitive tasks with different trade-offs between speed and specificity. More generally, our work provides a framework to study network-wide behaviorally-relevant plasticity in biologically realistic networks.

Journal article

Prince LY, Bacon T, Humphries R, Tsaneva-Atanasova K, Clopath C, Mellor JRet al., 2021, Separable actions of acetylcholine and noradrenaline on neuronal ensemble formation in hippocampal CA3 circuits, PLoS Computational Biology, Vol: 17, Pages: 1-37, ISSN: 1553-734X

In the hippocampus, episodic memories are thought to be encoded by the formation of ensembles of synaptically coupled CA3 pyramidal cells driven by sparse but powerful mossy fiber inputs from dentate gyrus granule cells. The neuromodulators acetylcholine and noradrenaline are separately proposed as saliency signals that dictate memory encoding but it is not known if they represent distinct signals with separate mechanisms. Here, we show experimentally that acetylcholine, and to a lesser extent noradrenaline, suppress feed-forward inhibition and enhance Excitatory–Inhibitory ratio in the mossy fiber pathway but CA3 recurrent network properties are only altered by acetylcholine. We explore the implications of these findings on CA3 ensemble formation using a hierarchy of models. In reconstructions of CA3 pyramidal cells, mossy fiber pathway disinhibition facilitates postsynaptic dendritic depolarization known to be required for synaptic plasticity at CA3-CA3 recurrent synapses. We further show in a spiking neural network model of CA3 how acetylcholine-specific network alterations can drive rapid overlapping ensemble formation. Thus, through these distinct sets of mechanisms, acetylcholine and noradrenaline facilitate the formation of neuronal ensembles in CA3 that encode salient episodic memories in the hippocampus but acetylcholine selectively enhances the density of memory storage.

Journal article

Kaleb K, Pedrosa V, Clopath C, 2021, Network-centered homeostasis through inhibition maintains hippocampal spatial map and cortical circuit function, CELL REPORTS, Vol: 36, ISSN: 2211-1247

Journal article

Gogianu F, Berariu T, Rosca M, Clopath C, Busoniu L, Pascanu Ret al., 2021, Spectral normalisation for deep reinforcement learning: an optimisation perspective, International Conference on Machine Learning (ICML), Publisher: JMLR-JOURNAL MACHINE LEARNING RESEARCH, Pages: 1-11, ISSN: 2640-3498

Most of the recent deep reinforcement learningadvances take an RL-centric perspective and focus on refinements of the training objective. Wediverge from this view and show we can recoverthe performance of these developments not bychanging the objective, but by regularising thevalue-function estimator. Constraining the Lipschitz constant of a single layer using spectralnormalisation is sufficient to elevate the performance of a Categorical-DQN agent to that of amore elaborated RAINBOW agent on the challenging Atari domain. We conduct ablation studiesto disentangle the various effects normalisationhas on the learning dynamics and show that issufficient to modulate the parameter updates torecover most of the performance of spectral normalisation. These findings hint towards the needto also focus on the neural component and itslearning dynamics to tackle the peculiarities ofDeep Reinforcement Learning.

Conference paper

This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.

Request URL: http://wlsprd.imperial.ac.uk:80/respub/WEB-INF/jsp/search-html.jsp Request URI: /respub/WEB-INF/jsp/search-html.jsp Query String: respub-action=search.html&id=00790252&limit=30&person=true