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Local excitation and lateral inhibition enable the simultaneous processing of multiple signals in recurrent neural networks

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Vinogradov,  O       
Institutional Guests, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Levina,  A       
Institutional Guests, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Giannakakis, E., Vinogradov, O., Buendía, V., Khajehabdollahi, S., & Levina, A. (2024). Local excitation and lateral inhibition enable the simultaneous processing of multiple signals in recurrent neural networks. Journal of Computational Neuroscience, 52(Supplement 1): O5, S8-S9.


Cite as: https://hdl.handle.net/21.11116/0000-0010-302C-7
Abstract
Biological neural networks exhibit complex detailed connectivity pat- terns between different neuron sub-types. These topological features of brain circuits are commonly hypothesized to have different functional roles, but the details of their function remain poorly understood. One of the most consistently observed features of brain connectivity is the presence of functional clusters or assemblies of neurons characterized by strong connectivity and functional similarity [1]. Although such assemblies are observed across different neuronal subtypes and brain areas, their exact contribution to brain computations is not fully studied. We hypothesize that the neural dynamics associated with synapse-type specific clusters of E/I neurons can play an important role in the simul- taneous processing of multiple signals by recurrent neural networks. At first, we studied whether structured recurrent connectivity can enable the formation of E/I co-tuning and input selectivity in upstream areas. We show that established mechanisms of synaptic plasticity, known to produce E/I co-tuning in feedforward, low-noise networks (Fig. 1a), fail to do so when receiving noisy inputs from lower areas with random recurrent connectivity. We examine whether non-trivial connectivity can reverse these effects by optimizing the level of clustering between neurons that receive the same signal using simulation-based inference. We find that strong excitatory connectivity between neurons receiving the same inputs combined with less specific lateral inhibitory connectiv- ity (Fig. 1e) can fully restore the ability of synaptic plasticity to produce co-tuning and input selectivity in an upstream neuron [2]. We then study the effects of structured connectivity in a more challeng- ing computational task, the simultaneous processing of two chaotic time series. We use a balanced recurrent network as a reservoir in a classi- cal task of predicting the trajectory of chaotic attractor [3], with the modification of simultaneously predicting two attractors. For this, we split the network into two clusters, each responsible for processing one attractor (Fig. 1f). We investigate whether synaptic connections between the two clusters can benefit the reservoir’s performance. In particular, we hypothesize that different probabilities of connection within and between clusters for different types of synapses could improve the net- work’s ability to process complex dynamics. Thus, we use simulation- based inference to estimate the distribution of synapse type-specific clustering levels that lead to optimal network performance. We find that localized excitation, combined with more spread-out inhibition (the same overall pattern that was observed in the plastic network), signifi- cantly boosts the reservoir’s performance. Our findings suggest that a connectivity pattern that is commonly observed in cortical networks and is associated with distinct dynamics [4] can have functional implications for the simultaneous processing of different signals in recurrent networks. The fact that the same pattern is observed for different tasks indicates the possibility of a general prin- ciple linking E/I network topology and the dynamics it creates with the ability of recurrent networks to perform specific computations.