Synaptic dynamics as convolutional units | bioRxiv

Synaptic dynamics as convolutional units | bioRxiv

Synaptic dynamics differ markedly across connections and strongly regulate how action potentials are being communicated. To model the range of synaptic dynamics observed in experiments, we develop a flexible mathematical framework based on a linear-nonlinear operation. This model can capture various experimentally observed features of synaptic dynamics and different types of heteroskedasticity. Despite its conceptual simplicity, we show it is more adaptable than previous models. Combined with a standard maximum likelihood approach, synaptic dynamics can be accurately and efficiently characterized using naturalistic stimulation patterns. These results make explicit that synaptic processing bears algorithmic similarities with information processing in convolutional neural networks.

Date: 2020/06/06 21:51

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