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| Hauptseite > Publikationsdatenbank > Dynamics of sensory stimulus representations in recurrent neural networks and in mice |
| Hauptseite > Publikationsdatenbank > Dynamics of sensory stimulus representations in recurrent neural networks and in mice |
| Poster (After Call) | FZJ-2025-03251 |
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2025
Abstract: Different stimuli elicit different transient neural responses in the brain. But how is theinformation represented in the parallel neuronal activity and how is it reshaped by the dynamics oflocal recurrent circuits? We investigate these questions in Neuropixels recordings of awake behavingmice and recurrent neural network models by inferring stimulus classes (e.g., visual or tactile) from thenetwork activity. We derive a mean-field theory that reduces the dynamics of complex networks to onlythree relevant dynamical quantities that fully determine the separability of stimulus representations.These dynamical quantities geometrically represent the length of the neural state vector, given by themean population activity $R$, and the typical overlaps Q= and Q≠ of neural state vectors representingthe variability of responses within or across stimulus classes, respectively.Mean-field theory predicts the time evolution of $R$, $Q^{=}$ , and $Q^{\neq}$ and enables us to quantitatively explainexperimental observables. The analytical theory predicts the temporal dynamics of stimulus separabilityas an interplay of firing rate dynamics and overlaps. It reveals how inhibitory balancing controls the timecourse of $R$ and chaotic dynamics control $Q^{=}$ and $Q^{\neq}$ , exposing the mechanisms underlying separabilitybetween stimuli.The analysis of mutual information of an optimally trained readout on the population signal revealsa trade-off between more information conveyed with an increasing number of stimuli, and stimuli be-coming less separable due to their increased overlap in the finite dimensional neuronal space. We findthat the experimentally observed small population activity $R$ is located in a regime where informationgrows extensively with the number of stimuli, which is sharply separated from a second regime, in whichinformation converges to zero, revealing a crucial advantage of sparse coding. Our work thus providesa novel understanding of separability of stimuli shaped by collective network dynamics.
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