Topic: spike patterns

Spatiotemporal patterns of neuronal activity are hypothesized to be essential for information processing in the brain. They suggest the existence of cell assemblies, groups of co-active neurons that represent a distinct cognitive unit, potentially related to specific behaviors or representations. Conventionally, cell assemblies are defined by strong structural connections, such that the stimulation of a portion of its members should transiently activate the entire assembly. Their exact composition and computational role, however, have yet to be fully clarified. We present the detection of spik

Temporal sequences are an important feature of neural information processing in biology. Neurons can fire a spike with millisecond precision, and, at the network level, repetitions of spatiotemporal spike patterns are observed in neurobiological data. However, methods for detecting precise temporal patterns in neural activity suffer from high computational complexity and poor robustness to noise, and quantitative detection of these repetitive patterns remains an open problem. Here, we propose a new method to extract spike patterns embedded in raster plots using a 1D convolutional autoencoder w