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| Hauptseite > Publikationsdatenbank > Transient Recurrent Dynamics Shape Representations in Mice |
| Hauptseite > Publikationsdatenbank > Transient Recurrent Dynamics Shape Representations in Mice |
| Conference Presentation (After Call) | FZJ-2025-02174 |
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2025
Abstract: Different stimuli evoke transient neural responses, but how is stimulus information represented and reshaped by local recurrent circuits? We address this question using Neuropixels recordings from awake mice and recurrent network models, inferring stimulus classes (e.g., visual or tactile) from activity. A two-replica mean-field theory reduces complex network dynamics to three key quantities: the mean population activity ($R$) and overlaps ($Q^{=}$, $Q^{\neq}$), reflecting response variability within and across stimulus classes. The theory predicts the time evolution of $R$, $Q^{=}$, and $Q^{\neq}$. Validated in experiments, it reveals how inhibitory balancing governs the dynamics of $R$, while chaotic dynamics shape overlaps, providing insights into the mechanisms underlying transient stimulus separation. The analysis of mutual information of an optimally trained population activity readout reveals that sparse coding (small $R$) allows the optimal information representation of multiple stimuli.
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