Topic: binary neurons

Neurons with recurrent connectivity can store memory patterns as attractor states in their dynamics, forming a plausible basis for associative memory in the brain. Classical theoretical results concerning fully connected recurrent neural networks (RNNs) with binary neurons and Hebbian learning rules find that they can store at most O(N) memories, where N is the number of neurons. However, under the physiological constraint that neurons are sparsely connected, this capacity is dramatically reduced to O(K), where K is the average number of presynaptic neurons (estimated to be O(10^3 ∼ 10^4) in m

This study takes an analytic approach to delve into the dynamics of pyramidal neurons, focusing on interplay of the basal and perisomatic regions. A significant portion of recurrent excitatory connections converge onto the basal dendrites, generating N-Methyl-D-Aspartate (NMDA) driven depolarizations, also known as NMDA-spikes. The time-scale of NMDA-spikes significantly surpasses the neuronal membrane time-scale by a factor of five, underscoring their relevance in information integration. The voltage dynamics within the dendric subunits remain unaffected by somatic spikes, enabling their inde