Work

Are cortical microcircuits optimized for information flux?—A simulation-based reverse engineering study

Year: 2026

Type: article

Abstract: Introduction: A sufficiently large information flux in recurrent neural networks, quantified by the mutual information between successive network states, has been proposed as a marker of rich information-processing capabilities. This raises the question of whether biological neural networks, such as cortical microcircuits, may be structurally organized to enhance information flux. Methods: To investigate this possibility, we study a simplified model of the cortical layer 5 architecture, in which a densely and strongly interconnected core population is embedded within a larger supporting network. Results: Surprisingly, we find that the embedding network exerts a pronounced flux-enhancing effect on the core dynamics. Systematic reverse-engineering analyses reveal that the embedding network primarily generates heterogeneous negative mean inputs that shift the core neurons into a higher-flux operating regime. Replacing the full time-dependent embedding signals by corresponding constant biases preserves this flux-enhancing effect, indicating that the detailed signal dynamics are not essential under the tested conditions. We further show that the information flux can be increased even beyond the biologically embedded case by applying optimized uniform or neuron-specific biases to the core neurons, and that similarly high values can emerge from a simple local adaptive-bias rule. Additional controls show that adaptive regulation and independently tuned inhibitory targeting further improve the embedded network, whereas removing the privileged strong-weight organization of the core abolishes the embedding advantage. Discussion: These results suggest that the surrounding network acts as a distributed regulator that prevents a strongly recurrent core from becoming trapped in saturated, low-flux states, rather than as an optimizer of information flux. Our findings are relevant both for the functional interpretation of biological neural circuits and for the design of artificial recurrent systems such as reservoir computers.

Source: Frontiers in Computational Neuroscience

DOI: 10.3389/fncom.2026.1886601