Excitable neuronal assemblies with adaptation as a building block of brain circuits for velocity-controlled signal propagation

Setareh, Hesam; Deger, Moritz; Gerstner, Wulfram

doi:10.1371/journal.pcbi.1006216

Cited by 13 publications

(12 citation statements)

References 88 publications

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“…This is in contrast with previous studies, where often a recursive least squares method is used to train the weights of the recurrent network [20,22,36,45]. Hardcoding a weight structure into the recurrent network has been shown to result in a similar sequential dynamics [16,17,46]. Studies that do incorporate realistic plasticity rules are mostly focusing on purely feedforward synfire chains [47][48][49], generating sequential dynamics.…”

Section: Discussionmentioning

confidence: 88%

“…Another example is sequential dynamics, where longer time scales are obtained by clusters of neurons that activate each other in a sequence. This sequential dynamics can emerge by a specific connectivity in the excitatory neurons [16,17] or in the inhibitory neurons [18,19]. However, it is unclear how the brain learns these dynamics, as most of the current approaches use non biologically plausible ways to set or "train" the synaptic weights.…”

Section: Introductionmentioning

confidence: 99%

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Learning spatiotemporal signals using a recurrent spiking network that discretizes time

2020

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Learning to produce spatiotemporal sequences is a common task that the brain has to solve. The same neurons may be used 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 spiking neurons drives a read-out layer: the dynamics of the driver recurrent network is trained to encode time which is then mapped through the read-out neurons to encode another dimension, such as space or a phase. Different spatiotemporal patterns can be learned and encoded through the synaptic weights to the read-out neurons that follow common Hebbian learning rules. We demonstrate that the model is able to learn spatiotemporal dynamics on time scales that are behaviourally relevant and we show that the learned sequences are robustly replayed during a regime of spontaneous activity.

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Section: Discussionmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Learning spatiotemporal signals using a recurrent spiking network that discretizes time

2020

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“…Such multi-scale structure of the autocorrelation could be advantageous for network computations that require expressive dynamics over multiple timescales, as it is often the case in motor control. Indeed, adaptation has been proposed to play a role in sequential memory retrieval [64], slow activity propagation [65], perceptual bistability [66] and decision making [67]. Moreover, SFA has beneficial consequences both for reservoir computing approaches [17] and for spiking neuron-based machine learning architectures [68].…”

Section: Discussionmentioning

confidence: 99%

How single neuron properties shape chaotic dynamics and signal transmission in random neural networks

2019

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While most models of randomly connected neural networks assume single-neuron models with simple dynamics, neurons in the brain exhibit complex intrinsic dynamics over multiple timescales. We analyze how the dynamical properties of single neurons and recurrent connections interact to shape the effective dynamics in large randomly connected networks. A novel dynamical mean-field theory for strongly connected networks of multi-dimensional rate neurons shows that the power spectrum of the network activity in the chaotic phase emerges from a nonlinear sharpening of the frequency response function of single neurons. For the case of two-dimensional rate neurons with strong adaptation, we find that the network exhibits a state of “resonant chaos”, characterized by robust, narrow-band stochastic oscillations. The coherence of stochastic oscillations is maximal at the onset of chaos and their correlation time scales with the adaptation timescale of single units. Surprisingly, the resonance frequency can be predicted from the properties of isolated neurons, even in the presence of heterogeneity in the adaptation parameters. In the presence of these internally-generated chaotic fluctuations, the transmission of weak, low-frequency signals is strongly enhanced by adaptation, whereas signal transmission is not influenced by adaptation in the non-chaotic regime. Our theoretical framework can be applied to other mechanisms at the level of single neurons, such as synaptic filtering, refractoriness or spike synchronization. These results advance our understanding of the interaction between the dynamics of single units and recurrent connectivity, which is a fundamental step toward the description of biologically realistic neural networks.

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“…Modelling studies so far have either focused on the study of sequential dynamics [38][39][40][41] or on motif acquisition [27][28][29]. This paper introduces an explicitly hierarchical model as a fundamental building block for the learning and replay of sequential dynamics of a compositional nature.…”

Section: From Serial To Hierarchical Modellingmentioning

confidence: 99%

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

2021

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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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Excitable neuronal assemblies with adaptation as a building block of brain circuits for velocity-controlled signal propagation

Cited by 13 publications

References 88 publications

Learning spatiotemporal signals using a recurrent spiking network that discretizes time

Learning spatiotemporal signals using a recurrent spiking network that discretizes time

How single neuron properties shape chaotic dynamics and signal transmission in random neural networks

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

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