Biology subjects

Gerstner, W.

Publications and source records attributed to Gerstner, W..

16 recordsLinked to original sources

bioRxiv2024

Critical periods support representation learning in a model of cortical processing

The emergence of abstract object representations in the mammalian ventral visual stream remains a central challenge for biologically plausible learning theories. While deep artificial networks trained via backpropagation achieve high performance, the algorithm lacks biological realism and conflicts with the staggered timeline of critical periods observed in cortical development. Here, we present a hierarchical model of the visual stream that learns invariant representations using only local synaptic plasticity rules modulated by lateral predictive signals. We demonstrate that imposing staggered critical periods -- where plasticity windows open and close sequentially from V1 to inferotemporal cortex -- significantly enhances representation quality for local learning rules, whereas it degrades performance in backpropagation-based networks. Furthermore, this sequential regime improves the learning efficiency in terms of number of synaptic updates, suggesting a metabolic advantage consistent with evolutionary constraints. We validate the functional utility of these acquired representations through reinforcement learning agents that successfully solve navigation and visual discrimination tasks without further fine-tuning of the visual encoder. These findings suggest that staggered critical periods are not merely a developmental constraint but a functional mechanism that enables efficient, local, and metabolically economical learning in hierarchical neural systems.

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bioRxiv2024

Biologically informed cortical models predict optogenetic perturbations

A recurrent neural network fitted to large electrophysiological datasets may help us understand the chain of cortical information transmission. In particular, successful network reconstruction methods should enable a model to predict the response to optogenetic perturbations. We test recurrent neural networks (RNNs) fitted to electrophysiological datasets on unseen optogenetic interventions, and measure that generic RNNs used predominantly in the field generalize poorly on these perturbations. Our alternative RNN model adds biologically informed inductive biases like structured connectivity of excitatory and inhibitory neurons, and spiking neuron dynamics. We measure that some biological inductive biases improve the model prediction on perturbed trials in a simulated dataset, and a dataset recorded in mice in vivo. Furthermore, we show in theory and simulations that gradients of the fitted RNN can be used to target micro-perturbations in the recorded circuits, and discuss the potential utility to bias an animals behavior and study cortical circuit mechanisms.

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bioRxiv2024

Two-factor synaptic consolidation reconciles robust memory with pruning and homeostatic scaling

Memory consolidation involves a process of engram reorganization and stabilization that is thought to occur primarily during sleep through a combination of neural replay, homeostatic plasticity, synaptic maturation, and pruning. From a computational perspective, however, this process remains puzzling, as it is unclear how the underlying mechanisms can be incorporated into a common mathematical model of learning and memory. Here, we propose a solution by deriving a consolidation model that uses replay and two-factor synapses to store memories in recurrent neural networks with sparse connectivity and maximal noise robustness. The model offers a unified account of experimental observations of consolidation, such as multiplicative homeostatic scaling, task-driven synaptic pruning, increased neural stimulus selectivity, and preferential strengthening of weak memories. The model further predicts that intrinsic synaptic noise scales sublinearly with synaptic strength; this is supported by a meta-analysis of published synaptic imaging datasets.

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bioRxiv2024

Representational similarity modulates neural and behavioral signatures of novelty

Novelty signals in the brain modulate learning and drive exploratory behaviors in humans and animals. While the perceived novelty of a stimulus is known to depend on previous experience, the effect of stimulus representations on novelty computation remains elusive. In particular, existing models of novelty computation fail to account for the effects of stimulus similarities that are abundant in naturalistic environments and tasks. Here, we present a unifying, biologically plausible model that captures how stimulus representations modulate novelty signals in the brain and influence novelty-driven learning and exploration. By applying our model to two publicly available data sets, we quantify and explain (i) how generalization across similar visual stimuli affects novelty responses in the mouse visual cortex, and (ii) how generalization across nearby locations impacts mouse exploration in an unfamiliar environment. Our model unifies and explains distinct neural and behavioral signatures of novelty, and enables theory-driven experiment design to investigate the neural mechanisms of novelty computation.

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bioRxiv2024

Linking Neural Manifolds to Principles of Circuit Structure

The classic view of cortical circuits composed of precisely tuned neurons hardly accounts for large-scale recordings indicating that neuronal populations are heterogeneous and exhibit activity patterns evolving on low-dimensional manifolds. Using a modelling approach, we connect these two contrasting views. Our recurrent spiking network models explicitly link the circuit structure with the low-dimensional dynamics of the population activity. Importantly, we show that different circuit models can lead to equivalent low-dimensional dynamics. Nevertheless, we design a method for retrieving the circuit structure from large-scale recordings and test it on simulated data. Our approach not only unifies cortical circuit models with established models of collective neuronal dynamics, but also paves the way for identifying elements of circuit structure from large-scale experimental recordings.

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bioRxiv2023

A dynamic attractor network model of memory formation, reinforcement and forgetting

Empirical evidence shows that memories that are frequently revisited are easy to recall, and that familiar items involve larger hippocampal representations than less familiar ones. In line with these observations, here we develop a modelling approach to provide a mechanistic hypothesis of how hippocampal neural assemblies evolve differently, depending on the frequency of presentation of the stimuli. For this, we added an online Hebbian learning rule, background firing activity, neural adaptation and heterosynaptic plasticity to a rate attractor network model, thus creating dynamic memory representations that can persist, increase or fade according to the frequency of presentation of the corresponding memory patterns. Specifically, we show that a dynamic interplay between Hebbian learning and background firing activity can explain the relationship between the memory assembly sizes and their frequency of stimulation. Frequently stimulated assemblies increase their size independently from each other (i.e. creating orthogonal representations that do not share neurons, thus avoiding interference). Importantly, connections between neurons of assemblies that are not further stimulated become labile so that these neurons can be recruited by other assemblies, providing a neuronal mechanism of forgetting.

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bioRxiv2022

Remembering the "When": Hebbian Memory Models for the Time of Past Events

Humans and animals can remember how long ago specific events happened. Little is known about the neural mechanisms that enable remembering the "when" of memories stored for long durations in the episodic memory system - in contrast to interval-timing on the order of seconds and minutes. Based on a systematic exploration of neural coding, association and retrieval schemes, we develop model classes that span the space of possible mechanisms for the reconstruction of the time of past events. In concrete examples we show how network architecture, Hebbian plasticity, synaptic pruning or systems consolidation allow the retrieval of the time of past events. In a simulation, we demonstrate how these mechanisms would enable food caching animals like corvids to remember what they cached, where, and how long ago. To dissociate different hypotheses, we propose three kinds of novel, non-verbal experiments, that can be run with humans and animals. Our simulations predict the experimental results for different classes of models. Our study shows that remembering the "when" can be implemented by many biologically plausible mechanisms and that carefully designed experiments are needed to pin down the actual neural implementation of the memory for the time of past events in different species. Author summaryWas it yesterday, a week ago, or perhaps even longer since I last ate pizza? Questions like these are typically easy to answer, because humans have a good sense of how long ago specific events happened. Research involving animals, such as food-caching birds, has revealed that they, too, can remember the "when" of past events. So far, little is known about the neuronal mechanisms that enable remembering the "when". In this study, we explore systematically concrete hypotheses of how neural circuits, synaptic plasticity and ongoing restructuring of neural circuits allow remembering the "when". We propose experiments, that could be done with humans or animals, to discriminate different biologically plausible mechanisms for remembering the "when".

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bioRxiv2022

Fast Adaptation to Rule Switching using Neuronal Surprise

In humans and animals, surprise is a physiological reaction to an unexpected event, but how surprise can be linked to plausible models of neuronal activity is an open problem. We propose a self-supervised spiking neural network model where a surprise signal is extracted from an increase in neural activity after an imbalance of excitation and inhibition. The surprise signal modulates synaptic plasticity via a three-factor learning rule which increases plasticity at moments of surprise. The surprise signal remains small when transitions between sensory events follow a previously learned rule but increases immediately after rule switching. In our model, previously learned rules are protected against overwriting when learning a new rule. Our surprise-modulated spiking network model makes a step towards solving the stability-plasticity dilemma in neuroscience and the problem of continual learning in machine learning.

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bioRxiv2022

The curse of optimism: a persistent distraction by novelty

Humans successfully explore their environment to find extrinsic rewards, even when exploration requires several intermediate reward-free decisions. It has been hypothesized that intrinsic rewards such as novelty guide this reward-free exploration. However, different intrinsic rewards lead to different exploration strategies, some prone to suboptimal attraction to irrelevant stochastic stimuli, sometimes called the noisy TV problem. Here, we ask whether humans show a similar attraction to reward-free stochasticity and, if so, which type of intrinsic reward guides their exploration. We design a multi-step decision-making paradigm where human participants search for rewarding states in an environment with a highly stochastic but reward-free sub-region. We show that (i) participants persistently explore the stochastic sub-region and (ii) their decisions are best explained by algorithms driven by novelty but not by optimal algorithms driven by information gain. Our results suggest that humans use suboptimal but computationally cheap strategies for exploration in complex environments.

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bioRxiv2021

Surprise: a unified theory and experimental predictions

Surprising events trigger measurable brain activity and influence human behavior by affecting learning, memory, and decision-making. Currently there is, however, no consensus on the definition of surprise. Here we identify 16 mathematical definitions of surprise in a unifying framework, show how these definitions relate to each other, and prove under what conditions they are indistinguishable. We classify these surprise measures into four main categories: (i) change-point detection surprise, (ii) information gain surprise, (iii) prediction surprise, and (iv) confidence-correction surprise. We design experimental paradigms where different categories make different predictions: we show that surprise-modulation of the speed of learning leads to sensible adaptive behavior only for change-point detection surprise whereas surprise-seeking leads to sensible exploration strategies only for information gain surprise. However, since neither change-point detection surprise nor information gain surprise perfectly reflect the definition of surprise in natural language, a combination of prediction surprise and confidence-correction surprise is needed to capture intuitive aspects of surprise perception. We formalize this combination in a new definition of surprise with testable experimental predictions. We conclude that there cannot be a single surprise measure with all functions and properties previously attributed to surprise. Consequently, we postulate that multiple neural mechanisms exist to detect and signal different aspects of surprise. Author noteAM is grateful to Vasiliki Liakoni, Martin Barry, and Valentin Schmutz for many useful discussions in the course of the last few years, and to Andrew Barto for insightful discussions through and after EPFL Neuro Symposium 2021 on "Surprise, Curiosity and Reward: from Neuroscience to AI". We thank K. Robbins and collaborators for their publicly available experimental data (Robbins et al., 2018). All code needed to reproduce the results reported here will be made publicly available after publication acceptance. This research was supported by Swiss National Science Foundation (no. 200020_184615). Correspondence concerning this article should be addressed to Alireza Modirshanechi, School of Computer and Communication Sciences and School of Life Sciences, EPFL, Lausanne, Switzerland. E-mail: alireza.modirshanechi@epfl.ch.

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bioRxiv2021

When shared concept cells support associations: theory of overlapping memory engrams

Assemblies of neurons, called concepts cells, encode acquired concepts in human Medial Temporal Lobe. Those concept cells that are shared between two assemblies have been hypothesized to encode associations between concepts. Here we test this hypothesis in a computational model of attractor neural networks. We find that for concepts encoded in sparse neural assemblies there is a minimal fraction cmin of neurons shared between assemblies below which associations cannot be reliably implemented; and a maximal fraction cmax of shared neurons above which single concepts can no longer be retrieved. In the presence of a periodically modulated background signal, such as hippocampal oscillations, recall takes the form of association chains reminiscent of those postulated by theories of free recall of words. Predictions of an iterative overlap-generating model match experimental data on the number of concepts to which a neuron responds. Authors contributionsAll authors contributed to conception of the study and writing of the manuscript. CG and TS developed the theory. CG wrote the code for all figures. EDF and RQQ provided the experimental data. EDF and CG analyzed the data. WG and CG developed algorithms to fit the experimental data.

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bioRxiv2020

Divergent sensory processing converges in frontal cortex for a planned motor response

The neuronal mechanisms generating a delayed motor response initiated by a sensory cue remain elusive. Here, we tracked the precise sequence of cortical activity in mice transforming a brief whisker stimulus into delayed licking using wide-field calcium imaging, multi-region high-density electrophysiology and time-resolved optogenetic manipulation. Rapid activity evoked by whisker deflection acquired two prominent features for task performance: i) an enhanced excitation of secondary whisker motor cortex, suggesting its important role connecting whisker sensory processing to lick motor planning, and ii) a transient reduction of activity in orofacial sensorimotor cortex, which contributed to suppressing premature licking. Subsequent widespread cortical activity during the delay period largely correlated with anticipatory movements, but when these were accounted for, a focal sustained activity remained in frontal cortex, which was causally essential for licking in the response period. Our results demonstrate key cortical nodes for motor plan generation and timely execution in delayed goal-directed licking.

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bioRxiv2020

Novelty is not Surprise: Exploration and learning in human sequential decision-making

Classic reinforcement learning (RL) theories cannot explain human behavior in response to changes in the environment or in the absence of external reward. Here, we design a deep sequential decision-making paradigm with sparse reward and abrupt environmental changes. To explain the behavior of human participants in these environments, we show that RL theories need to include surprise and novelty, each with a distinct role. While novelty drives exploration before the first encounter of a reward, surprise increases the rate of learning of a world-model as well as of model-free action-values. Even though the world-model is available for model-based RL, we find that human decisions are dominated by model-free action choices. The world-model is only marginally used for planning but is important to detect surprising events. Our theory predicts human action choices with high probability and allows us to dissociate surprise, novelty, and reward in EEG signals.

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bioRxiv2019

Adult dentate gyrus neurogenesis: A functional model

In adult dentate gyrus neurogenesis, the link between maturation of newborn neurons and their function, such as behavioral pattern separation, has remained puzzling. By analyzing a theoretical model, we show that the switch from excitation to inhibition of the GABAergic input onto maturing newborn cells is crucial for their proper functional integration. When the GABAergic input is excitatory, cooperativity drives the growth of synapses such that newborn cells become sensitive to stimuli similar to those that activate mature cells. When GABAergic input switches to inhibitory, competition pushes the configuration of synapses onto newborn cells towards stimuli that are different from previously stored ones. This enables the maturing newborn cells to code for concepts that are novel, yet similar to familiar ones. Our theory of newborn cell maturation explains both how adult-born dentate granule cells integrate into the preexisting network and why they promote separation of similar but not distinct patterns.

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bioRxiv2018

Stability of working memory in continuous attractor networks under the control of short-term plasticity

Continuous attractor models of working-memory store continuous-valued information in continuous state-spaces, but are sensitive to noise processes that degrade memory retention. Short-term synaptic plasticity of recurrent synapses has previously been shown to affect continuous attractor systems: short-term facilitation can stabilize memory retention, while short-term depression possibly increases continuous attractor volatility. However, it currently remains unclear to which degree these two short-term plasticity mechanisms interact, what their combined quantitative effect on working memory stability is, and whether these effects persist in neuronal networks with spike-based transmission. Here, we present a comprehensive description of the effects of short-term plasticity on noise-induced memory degradation in one-dimensional continuous attractor models. Our theoretical description, applicable to spiking and rate-based models alike, accurately describes the slow dynamics of stored memory positions in separate processes of diffusion due to spiking variability and drift due to sparse connectivity and neuronal heterogeneity. We find that facilitation decreases both diffusion and directed drifts, while short-term depression tends to increase both. Using mutual information, we evaluate the combined impact of short-term facilitation and depression on the ability of networks to retain stable working memory. Finally, our theory establishes links to experiments: we are able to predict the sensitivity of continuous working memory to distractor inputs and place constraints on network and synapse properties necessary to implement stable working memory.\n\nAuthor summaryThe ability to transiently memorize positions in the visual field is crucial for behavior. Models and experiments have shown that such memories can be maintained in networks of cortical neurons with a continuum of possible activity states, that reflects the continuum of positions in the environment. However, the accuracy of positions stored in such networks will degrade over time due to the noisiness of neuronal signaling and imperfections of the biological substrate. Previous work in simplified models has shown that synaptic short-term plasticity could stabilize this degradation by dynamically up- or down-regulating the strength of synaptic connections, thereby \"pinning down\" memorized positions. Here, we present a general theory that accurately predicts the extent of this \"pinning down\" by short-term plasticity in a broad class of biologically plausible models, thereby untangling the interplay of varying biological sources of noise with short-term plasticity. Importantly, our work provides a direct and novel theoretical link from the microscopic substrate of working memory - neurons and synaptic connections - to observable behavioral correlates. This allows us to constrain properties of cortical networks that are currently hard to assess experimentally, which we hope will help guide future theoretical and experimental work.

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bioRxiv2017

The temporal paradox of Hebbian learning and homeostatic plasticity

Hebbian plasticity, a synaptic mechanism which detects and amplifies co-activity between neurons, is considered a key ingredient underlying learning and memory in the brain. However, Hebbian plasticity alone is unstable, leading to runaway neuronal activity, and therefore requires stabilization by additional compensatory processes. Traditionally, a diversity of homeostatic plasticity phenomena found in neural circuits are thought to play this role. However, recent modelling work suggests that the slow evolution of homeostatic plasticity, as observed in experiments, is insufficient to prevent instabilities originating from Hebbian plasticity. To remedy this situation, we suggest that homeostatic plasticity is complemented by additional rapid compensatory processes, which rapidly stabilize neuronal activity on short timescales.

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