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A theory for Hebbian Learning in recurrent E-I networks

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  • UserSamuel Eckmann (Max Planck Institute for Brain Research, Frankfurt am Main, Germany)
  • ClockFriday 18 December 2020, 09:00-10:00
  • HouseOnline on Zoom (recorded).

If you have a question about this talk, please contact Yul Kang .

The Stabilized Supralinear Network is a model of recurrently connected excitatory (E) and inhibitory (I) neurons that can explain many cortical phenomena such as response normalization and inhibitory stabilization. However, the network’s connectivity is designed by hand, based on experimental measurements. How the connectivity can be learned from the sensory input statistics in a biologically plausible way is unknown. Here we present a recurrent E-I network model where all synaptic connections are simultaneously plastic. We employ local Hebbian plasticity rules and develop a theoretical framework that explains how neurons’ receptive fields decorrelate and become self-stabilized by recruiting co-tuned inhibition. As in the Stabilized Supralinear Network, the circuit’s response is normalized – the response to a combined stimulus is equal to a weighted sum of the individual stimulus responses.

In summary, we introduce a biologically plausible theoretical framework to model plasticity in fully plastic recurrent E-I networks. While the connectivity is derived from the sensory input statistics, the circuit performs meaningful computations. Our work provides a mathematical framework of plasticity in recurrent networks, which has previously only been studied numerically and can serve as the basis for a new generation of brain-inspired unsupervised machine learning algorithms.

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This talk is part of the Computational Neuroscience series.

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