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September 5, 2025Open Access

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

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Rate Chaos occurs in recurrent neural networks without strong external inputs, indicating a new balance mechanism.
Dynamical Mean Field approaches reveal that synaptic adaptation allows networks to maintain stable activity over time.
The extent of stable states shrinks with larger network sizes, pointing to significant implications in neural dynamics.
The study extends the balanced network paradigm, offering potential biological relevance for cortical circuits.

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Implication

This theory proposes a novel mechanism using short-term synaptic depression to sustain activity in networks, indicating new insights for cortical computations.

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Angulo‐García et al. (2025) studied this question.

synapsesocial.com/papers/68bb46b56d6d5674bccfe6d4 https://doi.org/10.1101/2025.08.26.672268

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Implication

This theory proposes a novel mechanism using short-term synaptic depression to sustain activity in networks, indicating new insights for cortical computations.

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

Key Points

Discussion

Implication

Cite This Study

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

Key Points

Discussion

Implication

Cite This Study

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