Biology subjects

Clopath, C.

Publications and source records attributed to Clopath, C..

At least 19 recordsLinked to original sources

bioRxiv2026

Predictive dendrites as a foundation for biological learning

A fundamental challenge for neuroscience is to identify laws of synaptic plasticity that are consistent with experimental observations at the level of individual neurons, whilst also explaining the ability of neural networks to learn complex tasks. Here we investigate a candidate law in which neurons adjust synaptic strengths to minimize the mismatch between somatic and dendritic activity. We demonstrate that models based on this rule can effectively adjust synaptic weights across network layers to learn non-linear tasks. Moreover, minimizing mismatch between spiking activity and dendritic inputs leads to integrate-and-fire dynamics and spike-timing-dependent plasticity. The model reproduces the experimentally observed dependencies of plasticity upon initial synaptic weights and spike patterns, whilst also generating a series of testable predictions. Our results show that a single principle - minimization of somato-dendritic mismatch - links learning at the network level with fundamental properties of individual neurons, including membrane potential dynamics and canonical features of synaptic plasticity.

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bioRxiv2026

Prefrontal control of hippocampal map reorganization during sleep

During wakefulness, exposure to novel experiences induces hippocampal remapping, in which place cell ensembles reorganize to form distinct representations of new information. Classical models of systems consolidation posit that subsequent sleep stabilizes these wake-formed representations and supports their gradual transfer to medial prefrontal cortex (mPFC) networks primarily via bottom-up hippocampal reactivations. Using miniscope Ca2+ imaging of CA1 place cells, we monitored hippocampal activity as mice explored a familiar environment followed by a similar novel one. As expected, place cells remapped in the novel context. However, a single episode of post-encoding sleep selectively reversed remapping dynamics by promoting the reinstatement of familiar place cell representations in the novel environment, an effect that was absent in the wake condition. This neural reinstatement was accompanied by behavioral indicators of familiarity, including reduced exploration and rearing. Chemogenetic inactivation of the mPFC during post-encoding sleep abolished this generalization effect, and instead preserved the novel hippocampal map. Thus, our results extend current models by showing that sleep not only consolidates wake-established hippocampal patterns, but actively shapes hippocampal maps via prefrontal control, thereby supporting memory generalization.

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bioRxiv2026

Transient excitability and synaptic consolidation support stable memory despite neural drift

Long-term memories remain behaviorally stable despite turnover in the neuronal populations that encode them. This representational drift differs across brain regions: hippocampal representations can reconfigure within hours, whereas cortical ensembles remain comparatively stable over days to weeks. This asymmetry poses a challenge for systems consolidation, in which hippocampal activity is thought to instruct the formation of persistent cortical memory traces. We developed a two-region recurrent network model of the hippocampus (HPC) and anterior cingulate cortex (ACC) incorporating region-specific synaptic plasticity, excitability-dependent neuronal allocation, hippocampal-to-cortical coupling, and activity-dependent intrinsic plasticity. When the regions evolved independently, faster synaptic turnover in HPC produced pronounced drift, whereas persistent ACC connectivity preserved a more stable cortical ensemble. In the intact circuit, hippocampal input recruited ACC neurons but also propagated hippocampal variability into cortex, destabilizing the emerging cortical engram and impairing memory expression. A transient increase in the intrinsic excitability of recruited ACC neurons counteracted this instability by promoting repeated reactivation during an early consolidation window, stabilizing cortical ensemble membership without preventing hippocampal drift. Simulated erasure of learning-induced potentiation further reproduced the early dependence of memory on hippocampal, but not ACC, plasticity. Together, these results support a sequential mechanism in which hippocampal recruitment, transient cortical intrinsic plasticity, and persistent cortical synaptic plasticity transform a dynamic hippocampal representation into a stable cortical memory trace.

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bioRxiv2026

A common structure in recurrent networks supports neural sequence generation locally and in downstream neurons

Neural sequences, characterized by neurons or groups of neurons that fire one after the other, have been observed in multiple brain regions, across species, and are known to underlie a diversity of brain functions. To flexibly support behaviour and cognition, neural sequences exhibit much variability in properties like their temporal width, baseline, and peak firing rate. Despite this variability and the central role that sequences play in supporting brain function, a framework that explains how flexible sequences are generated and dynamically maintained within a circuit is still missing. Here we go beyond traditional approaches that investigate a one-to-one relationship between network connectivity and specific sequential dynamics. Instead, we train recurrent neural network models to generate a repertoire of sequential dynamics and characterize the obtained connectivity matrices. We found that different connectivity matrices can generate the same neural sequence, yet all connectivity matrices that generate a specific sequence share a common connectivity profile, defined here as the average weight between pairs of neurons as a function of their distance in the sequence ordering. It is the connectivity profile, as opposed to the connectivity matrix, that serves as a fingerprint of the sequential dynamics and shapes the network response to perturbations of the neural activity. Our model predictions were consistent with results obtained from experimental data recorded across brain regions and across species. Finally, we demonstrated that neural sequences can facilitate and constrain the formation of a large repertoire of sequences in downstream brain regions, with the potential of acting as scaffolds for a wide range of computations. Altogether, our results explain how network connectivity can generate a diversity of neural sequences across circuits and how those sequences can be flexibly adapted. Our framework reveals sequences as a common algorithm to support brain function across brain regions and species.

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bioRxiv2026

Transcription-specific synaptic plasticity mediates engram computations

Memory is encoded by sparse ensembles of neurons. While activity-dependent transcription and learning-induced synaptic plasticity are causally related to memory formation and recall, the link between the transcriptional regulation of synaptic plasticity and memory computations remains elusive. In particular, even though transcriptionally defined neuronal ensembles within a memory engram exhibit specific forms of synaptic plasticity and support distinct behavioral outputs, it is still unclear whether transcription-specific synaptic plasticity drives ensemble computations, rather than merely serving as a marker of ensemble identity. Here, we demonstrate that transcription-specific synaptic plasticity enables ensemble computations essential for learning-induced adaptive behaviors. In the mouse dentate gyrus (DG), we found that neuronal ensembles genetically defined by Fos-dependent transcription engage plasticity in feedforward excitatory synapses, whereas those defined by Npas4-dependent transcription engage plasticity in recurrent inhibitory synapses. We modeled spiking neural networks with transcription-specific synaptic plasticity and observed that Fos- and Npas4-dependent ensembles emerged following learning and stabilized with memory consolidation. Our computational model predicted that blocking Fos- or Npas4-dependent synaptic plasticity disrupts memory generalization and discrimination, respectively. By acutely deleting Fos or Npas4 in the DG to selectively block Fos- or Npas4-dependent synaptic plasticity, we conducted contextual fear conditioning experiments whose results supported our computational model's prediction. Our study provides causal evidence that specific transcriptional programs induced in distinct neuronal ensembles differentially engage synaptic plasticity and thereby regulate memory computations.

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bioRxiv2026

Intrinsic representational drift from predictive excitatory-inhibitory plasticity

Neural representations drift gradually over time even under stable environmental conditions, but the synaptic mechanisms driving this drift remain unclear. Here we show that representational drift can arise intrinsically from predictive synaptic plasticity in spiking excitatory-inhibitory networks. During repeated exposure to unchanged input patterns, individual neurons gradually changed their preferred pattern while ensemble-level coding remained stable. These changes in preference were preceded by a weakening of the net excitatory-inhibitory drive supporting the current preference relative to competing patterns, and this relative drive predicted changes on the following trial. Extending the model to hippocampal place coding reproduced experience-dependent tuning-curve drift in CA1, including the dissociation between elapsed time and intervening exposure. At the population level, drift was expressed as coordinated rotation and translation of neural state space. Thus, representational drift can emerge as an intrinsic consequence of predictive E/I plasticity that maintains balanced, selective population codes.

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bioRxiv2026

Few-shot learning of predictive features with dendrites and behavioural timescale synaptic plasticity in the hippocampus

When an animal enters a new environment, neurons in the hippocampus begin to map out the space. They become selectively responsive to features like the animals location, the position of rewards, and the presence of stimuli relevant to navigating or performing a specific task. With experience, hippocampal neurons also develop behaviour-related biases. It is thus believed that the hippocampus encodes multi-sensory, behaviourally-relevant cognitive maps of environments that are critical to navigation, learning and guiding behaviour. The predictive learning hypothesis proposes that these complex maps emerge because a core goal of the brain is to learn to predict the features of its environment. In sensory cortex, predictive learning provides a compelling explanation of anticipatory and error-like sensory responses. Pyramidal neurons receive top-down and bottom-up inputs to their proximal basal and distal apical dendrites, respectively. These complementary inputs streams are thought to enable them to act as comparison units, signaling discrepancies between predictive and sensory inputs. In the hippocampus, however, the potential link between pyramidal neurons and predictive learning is still underexplored. Here, we investigate the possibility that pyramidal neurons perform a similar comparator function in the hippocampus. In our model, two-compartment pyramidal neurons receive sensory information about salient features of the environment at their distal apical dendrites which is compared to tuned spatial inputs received more proximally to their cell body. We demonstrate how predictive learning implemented in this circuit using behavioural timescale synaptic plasticity and distal apical inhibition can explain a variety of spatial and behaviourally-relevant features encoded in the hippocampus. We also lay out key predictions for validating our model experimentally. As such, our work helps bridge an important gap in the literature on predictive learning in the hippocampus and set the stage for more robust experimental validation of this prominent hypothesis.

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bioRxiv2026

Predictability of evoked activity governs the geometry of spontaneous neural manifolds

Spontaneous neural activity reflects sensory experience yet occupies a lower-dimensional subspace than evoked responses. However, the principle governing which components of experience are incorporated into this intrinsic activity has remained unknown. Here, we propose that temporal predictability is one of the organizing principles that selects which evoked dimensions enter spontaneous neural manifolds. We show that local predictive synaptic plasticity provides a circuit mechanism that selectively embeds components of evoked activity that are predictable on the intrinsic timescale of recurrent dynamics, while excluding unpredictable fluctuations. As a consequence, the dimensionality of spontaneous activity is not fixed but depends on environmental timescales: rapidly fluctuating inputs are excluded, whereas slowly varying components are retained as contextual dimensions. This framework reproduces on- and off-manifold coding observed in visual cortex, and reconciles apparently conflicting developmental observations that spontaneous and evoked activity become both more similar and more geometrically distinct over maturation. Together, these results identify temporal predictability as a key principle linking environmental statistics to the geometry of intrinsic population dynamics.

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bioRxiv2026

Branch-specific plasticity explains distal enrichment of retinotopically displaced inputs in visual cortex

Neurons distribute synaptic inputs across their dendritic tree. In layer 2/3 pyramidal cells of primary visual cortex, spines on distal dendrites share somatic orientation preference but have receptive fields displaced in retinotopic space, which supports tuning to visual edges. However, it is not known how synaptic plasticity rules can lead to specialization of tuning properties across dendritic compartments. We demonstrate an experimentally grounded model of compartment-specific spike-timing dependent plasticity (STDP) that accounts for the enrichment of retinotopically-displaced inputs on distal branches. Our previous experimental work revealed compartment-specific calcium signals that predict reduced STDP-mediated depression but preserved potentiation. Based on these findings, we built an STDP model with compartment-specific properties, in which some distal branches are relatively resistant to STDPmediated depression. Synapses on these branches are more likely to stabilize inputs with weaker correlations to postsynaptic spiking. Using a visual input model, we show that compartment-specific reduction in STDP-mediated depression recapitulates in vivo experimental measurements of spine tuning. Furthermore, our experimental results show that reduced STDP-mediated depression is restricted to distal dendritic compartments with complex branching structure and not observed in other distal branches. Therefore, our model makes an untested prediction that complex branches will be hotspots for retinotopically-displaced inputs.

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bioRxiv2026

Functional reorganization of motor cortex connectivity during learning

Learning new tasks requires the brain to reshape the flow of neural activity, but how these changes arise from dynamic neural connectivity remains unclear. Here, we used two-photon photostimulation and calcium imaging to map learning-related changes in connectivity in layer 2/3 of mouse motor cortex, induced by learning of an optical brain-computer interface (BCI) task. Mice rapidly (within minutes) learned to change activity in a conditioned neuron to earn rewards. Activity changes were sparse; the conditioned neuron increased activity more than surrounding neurons. Mapping connectivity before and after learning revealed changes in motor cortex connectivity, enriched in neurons that were active before trial initiation, analogous to motor cortex populations that are active preceding movement. Motor cortex plasticity reroutes preparatory activity to neurons that are active later and control the conditioned neuron. Our findings show how rapid learning can be achieved through structured changes in motor cortex connectivity.

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bioRxiv2025

Decoding medial entorhinal cortical dynamics produces planning-like alternations in hippocampal theta sequences

The hippocampus is central to memory and spatial planning. A prominent candidate mechanism supporting navigational decision making is hippocampal theta sequences--brief ([~]120ms) place-cell sequences representing future trajectories--which have been interpreted as planning signals based on their alternation between maze arms in T-maze tasks. However, recent work suggests that intrinsic left-right alternation in medial entorhinal cortex (MEC) theta sequences may underlie this phenomenon. Here, we test this hypothesis using a model of MEC theta dynamics in a T-maze and show that standard Bayesian decoding yields hippocampal theta sequences that alternate between arms. Analysis of simultaneous MEC-hippocampal recordings reveals tight coupling of MEC and hippocampus and coordinated left-right alternation. Notably, this alternation occurs not only at choice points but also during inbound and forced-turn trials. These findings suggest that left-right alternation in hippocampal theta sequences is a ubiquitous phenomenon driven by MEC dynamics, challenging the interpretation that these specifically reflect intentional decision-making or planning processes.

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bioRxiv2025

Why motor learning involves multiple systems: an algorithmic perspective

The initial stage of learning motor skills involves exploring vast action spaces, making it impractical to learn the value of every possible action independently. This poses a challenge for standard reinforcement learning approaches, which excel in constrained domains but struggle when the space of possible actions is high-dimensional. Recent work in machine learning has sought to mitigate this problem by combining deep re-inforcement learning with a supervised learning system that reduces the complexity of the control policy space by learning low-dimensional embeddings of an action space. Here, we propose that in the mammalian brain, the cortico-cerebellar network learns these low-dimensional action embeddings in a supervised way, while the basal ganglia learn value and policies in this action embedding space using reinforcement learning. We trained this model on reaching tasks and show that, contrary to traditional models of the basal ganglia, it recapitulates features of neural activity whereby similar reaching movements are associated with similar neural activity patterns in the basal ganglia. We also demonstrate a link between learning these low-dimensional action embeddings and both generalisation and the limits of multi-task adaptation in human behavioural studies. Through this framework, we propose a novel computational view of how key motor regions of the brain interact to efficiently learn a new skill.

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bioRxiv2025

A frontal motor circuit for economic decisions and actions

Flexible behaviour requires transforming abstract cognitive representations, such as value preferences, into concrete motor actions. During economic decision-making, individuals evaluate options to guide choices and then transform these choices into specific actions to obtain rewards. Understanding how neural circuits convert these abstract economic decisions into spatial actions remains challenging because decision formation and motor planning are typically intertwined. Here we introduce a mouse task that temporally dissociates value-guided decisions from spatial action planning, and show that a frontal motor network implements the transformation across decision stages through dynamic circuit reconfiguration. Using cortex-wide imaging and optogenetic perturbations, we identified a frontal motor circuit that was causally required for both abstract and motor stages of choice. During the abstract decision stage, neurons in this circuit encoded option values and economic choices independently of sensorimotor contingencies, and unilateral silencing impaired decisions without spatial bias. In contrast, during spatial planning, value and spatial signals were non-linearly integrated to guide action selection, and unilateral silencing produced an ipsilateral bias. A dynamical model captured this transformation, predicting a mode switch from cooperative interhemispheric maintenance of economic choice to competitive, lateralized control of actions, which we validated with simultaneous bilateral recordings. These findings demonstrate how frontal motor circuits reconfigure their interactions to bridge abstract cognition and concrete actions, providing a circuit-level mechanism for flexible, value-guided behaviour.

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bioRxiv2025

Flexible selection of working memory representations to reduce cognitive cost

Across psychology 1-4, economics 5,6, machine learning 7, and neuroscience 8,9, it is well established that choosing a good internal representation can make complex problems easier to solve. Thus, intelligent behavior depends not only on what is stored in working memory, but on the format in which it is stored. Selecting a lower-load format can substantially reduce the mental effort required for a task, since working-memory load is a major contributor to cognitive cost 10-12. Whether non-human animals make such representational choices, and how these choices are implemented in neural circuits, has remained unclear. Here we show that rats flexibly adjust the format of working memory to reduce cognitive cost. In an egocentric task that encourages an action-based code, rats maintained the relevant information as a stable motor plan supported by frontal cortex. When the same behavioral problem was reformulated in allocentric coordinates, the action-based code became costly, and rats instead stored a sensory trace in auditory cortex. A cost-sensitive model explains which internal format should be used under each task variant and predicted that simplifying the allocentric task would reduce the advantage of a sensory code and drive reinstatement of frontal motor planning; this prediction was confirmed, as rats rapidly returned to a motorplan representation and the frontal cortex again became necessary for performance. These results demonstrate that flexible representational selection is not unique to humans 13 but is present in rodents and reflected in circuit-level reallocation of working memory. This establishes a neural basis for classic theories linking problem representation to computational efficiency and provides a path toward a circuit-level understanding of how the brain selects internal formats to reduce cognitive cost.

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bioRxiv2025

Rapidly Reconfigurable Dynamic Computing in Neural Networks with Fixed Synaptic Connectivity

Learning and memory in the brains neocortex have long been hypothesised to be primarily mediated by synaptic plasticity. Extensive research in artificial neural networks has shown that training networks by adjusting connection weights faces computational challenges, including large parameter spaces and the tendency of new learning to interfere with previous learning (catastrophic forgetting). We propose that the brain, which is resistant to these challenges, can also learn by modulating the excitability of each neuron in a network rather than changing synaptic strengths. We show here that learning a task-specific set of bias currents enables a feedforward or recurrent network with fixed and randomly assigned connections to perform well on and switch between dozens of tasks, including regression, classification, autonomous time series generation, a game and robotic control. Bias-only learning also provides a novel mechanistic explanation for representational drift. It directly links the noise robustness of neuronal representations on short and long time scales to the ability of neural circuits to preserve learned information while remaining adaptable. We postulate that subcortical structures, such as the basal ganglia or cerebellum, may provide similar bias inputs to the neocortex for rapid task learning and robustness against interference.

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bioRxiv2025

Homeostatic Binary Networks: A simple framework for learning with overlapping patterns

Memories are rarely stored in isolation: experiences overlap in time and context, leading to neuronal activity patterns that share elements across episodes. While such overlap supports generalization and abstraction, it also increases interference and threatens representational stability. Here we introduce Homeostatic Binary Networks (HBNs), a minimal recurrent framework that combines binary activity, adjustable inhibition, Hebbian learning, and homeostatic plasticity to address these challenges. First, we formalize an Episode Generation Protocol (EGP) that creates compositional episodes with controllable overlap and noise, and define a corresponding semantic structure as conditional probabilities between concepts. We then show analytically and through simulations that recurrent synapses converge to conditional firing probabilities, thereby encoding asymmetric semantic relationships across concepts. These recurrent dynamics enable reliable recall and replay of overlapping episodes without representational collapse. Finally, by incorporating feed-forward plasticity with a neuronal maturity mechanism, output neurons form selective receptive fields in a one-shot manner and refine them through replay, yielding robust unsupervised classification of overlapping episodes. Together, our results demonstrate how simple principles such as neural and synaptic competition can support the stable representation and organization of overlapping memories, providing a mechanistic bridge between episodic and semantic structure in memory systems.

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bioRxiv2025

From episodes to concepts and back: Semantic representations in episodic memory enhance recall, replay, and compositional consolidation

Semantic knowledge is thought to emerge through the consolidation of episodic experience, yet the biological mechanisms by which reusable semantic representations are extracted from complex episodes remain unclear. Conversely, semantic representations can themselves be found within the medial temporal lobe, raising the questions of how they arise there and why structured semantic overlap should benefit an episodic memory system. We propose that replay establishes a shared semantic vocabulary between MTL and CTX through two complementary processes. First, episodic replay enables neocortex to extract reusable semantic concepts from overlapping episodes via a form of dictionary learning. Second, spontaneous cortical activity replays these learned concepts back to MTL, allowing semantic representations to become part of the episodic code. We implement this framework in a circuit model using local Hebbian plasticity and homeostatic competition. The model reproduces key experimental observations, including concept-cell-like representations, increased overlap between related memories following consolidation, and synaptic reorganization associated with systems consolidation. Functionally, embedding semantic representations within episodic memory improves recall by providing an error-correcting scaffold and enhancing replay consistency, inducing a generalization bias in the consolidation of compositional representations. This further explains experimental effects hard to reconcile with standard accounts of consolidation, such as the impact of disrupting semantics in episodic recall and semantic deficits following medial temporal lobe lesions. More broadly, our results suggest that consolidation progressively builds a shared vocabulary between episodic and semantic memory, allowing structure extracted from past experience to organize the representation and consolidation of future memories.

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bioRxiv2025

Predictive learning enables compositional representations

The brain builds predictive models to plan future actions. These models generalize remarkably well to new environments, but it is unclear how neural circuits acquire this flexibility. Compositional representations, which have been observed in the brain, could explain this adaptability. They enable a process called compositional generalization, where independent modules performing different computations can be selected to perform novel composite tasks. In this work, we show that compositional representations emerge in recurrent neural networks (RNNs) trained solely to predict future sensory inputs. We trained an RNN to predict frames in a visual environment defined by independent latent factors and their corresponding dynamics. We found that the network learned to solve this task by developing a compositional model. Specifically, it had disentangled representations of the latent factors, and formed distinct, modular clusters, each implementing a single dynamic. The network autonomously selected which cluster to use according to the sensory inputs, without task labels, using competitive dynamics between clusters. This modular and disentangled architecture enabled the network to perform compositional generalization, accurately predicting outcomes in novel contexts composed of unseen combinations of dynamics. Our findings explain how an unsupervised mechanism can learn the modular causal structure of an environment in a compositional code.

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