January 17, 2021

Orchestrated Excitatory and Inhibitory Learning Rules Lead to the Unsupervised Emergence of Up-states and Balanced Network Dynamics

Self-sustaining dynamics maintained through recurrent connections are of fundamental importance to cortical function. We show that Up-states -an example of self-sustained network dynamics- autonomously emerge in cortical circuits across three weeks of ex vivo development, establishing the presence of unsupervised synaptic learning rules that lead to globally stable emergent dynamics. Computational models of excitatory-inhibitory networks have established that four sets of weights (WEE, WEI, WIE, WII) cooperate to generate stable self-sustained dynamics, but have not addressed how a family of learning rules can operate in parallel at all four weight classes to achieve self-sustained inhibition-stabilized regimes. Using numerical and analytical methods we show that standard homeostatic rules cannot account for the emergence of self-sustained dynamics due to the paradoxical effect. We derived a novel family of homeostatic learning rules that operate in parallel at all four synaptic classes, which robustly lead to the emergence of Up-states and balanced excitation-inhibition.

 bioRxiv Subject Collection: Neuroscience

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