Topic: Synfire chains

The cell assembly hypothesis postulates that groups of coordinated neurons form the basis of information processing. Here, we test this hypothesis by analyzing massively parallel spiking activity recorded in monkey motor cortex during a reach-to-grasp experiment for the presence of significant ms-precise spatio-temporal spike patterns (STPs). For this purpose, the parallel spike trains were analyzed for STPs by the SPADE method (Stella et al, 2019, Biosystems), which detects, counts and evaluates spike patterns for their significance by the use of surrogates (Stella et al, 2022 eNeuro). As a r

Artificial neural networks (ANNs) achieve remarkable results on various tasks, but understanding the computational mechanisms underlying their performance remains difficult. Furthermore, traditionally employed artificial networks have little in common with real biological networks. Overcoming this difference, machine-learning-based training methods for spiking neuronal networks (SNNs) have been developed to create functional networks, enabling the investigation of these neural networks with neuroscientific analysis methods. Here we analyze one such SNN trained with backpropagation based on a

Synfire chains have been postulated as a model for stable propagation of synchronous spikes through the cortical networks [1,2,3]. Synfire-chain-like activity can also be found in spiking artificial neural networks trained for a classification task [4]. Understanding the mechanism for generating such activity would provide better insights into the functioning of real brains and artificial neural networks. Here we consider an analytically tractable network of binary units to study the conditions for the emergence of synchronous spikes and their stable propagation. Our network is organized in la