Topic: membrane potentials

Hippocampal ripple oscillations (140–220 Hz) have been implicated in important cognitive functions such as memory consolidation (Buzsáki 1989). Their generating mechanism however remains unclear, although various models have been proposed. We suggest that transient features of ripples can guide model selection: So far only the “bifurcation-based” inhibitory network model (Donoso et al. 2018, Brunel and Hakim 1999) could reproduce the experimentally observed intra-ripple frequency accommodation (IFA) — an asymmetry in the instantaneous ripple frequency in response to transient, sharp wave-like

Gradient-based optimization powered by the backpropagation algorithm proved to be the pivotal method in the training of non-spiking artificial neural networks. At the same time, spiking neural networks hold the promise for efficient processing of real-world sensory data by communicating using discrete events in continuous time. We derive the backpropagation algorithm for a recurrent network of spiking (leaky integrate-and-fire) neurons with hard thresholds and show that the backward dynamics amount to an event-based backpropagation of errors through time. Our derivation uses the jump condition