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This research explores a new mechanism for maintaining balanced state dynamics in neural networks without strong external input currents.

February 14, 2026PLoS Computational BiologyOpen Access

A theory for self-sustained balanced states in absence of strong external currents

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This research explores a new mechanism for maintaining balanced state dynamics in neural networks without strong external input currents.
Utilized numerical simulations of recurrent neural networks with short-term synaptic depression (STD).
Analyzed dynamics of a densely connected network of N rate-neuron models.
Characterized transition regions and chaos regimes based on synaptic strength.
Applied Dynamical Mean Field approaches to study the Rate Chaos regime.
Identified two distinct activity regimes based on synaptic strength: homogeneous fixed point and Rate Chaos.
Observed a shrinking transition region as network size increases, eventually vanishing in the thermodynamic limit.
Demonstrated that STD sustains stable irregular activity without the necessity of strong external currents.

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Novel mechanism reveals how networks sustain activity without strong input currents, implying biological plausibility.

Angulo‐García et al. (2026) studied this question.

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