Paradoxical Self-sustained Dynamics Emerge from Orchestrated Excitatory and Inhibitory Homeostatic Rules

SOLDADO MAGRANER, SARAY; SEAY, MICHAEL J.; LAJE, RODRIGO; BUONOMANO, DEAN

San Diego

Congreso; Society for Neuroscience Annual Meeting (SfN 2022); 2022

Society for Neuroscience

Cortical networks have the remarkable ability to self-assemble into dynamic regimes in which excitatory positive feedback is balanced by recurrent inhibition. This inhibition-stabilized regime is increasingly viewed as the default dynamic regime of the cortex but how it emerges in an unsupervised manner remains unknown. Theoretical work has established that four sets of weights (W E←E , W E←I , W I←E , W I←I ) must obey specific relationships to produce inhibition-stabilized dynamics, but it is not known how the brain autonomously sets the values of all four weight classes to be in the right regime. We prove that classic forms of homeostatic plasticity are unable to drive recurrent networks to an inhibition-stabilized regime due to the well known paradoxical effect. We next derive a novel family of cross-homeostatic rules that lead to the unsupervised emerge of inhibition-stablized networks. These rules shed new light on how the brain may reach its default dynamic state and provide a valuable tool to self-assemble artificial neural networks into ideal computational regimes.