Discriminating neural ensemble patterns through dendritic computations in randomly connected feedforward networks.

ABSTRACT: Co-active or temporally ordered neural ensembles are a signature of salient sensory, motor, and cognitive events. Local convergence of such patterned activity as synaptic clusters on dendrites could help single neurons harness the potential of dendritic nonlinearities to decode neural activity patterns. We combined theory and simulations to assess the likelihood of whether projections from neural ensembles could converge onto synaptic clusters even in networks with random connectivity. Using rat hippocampal and cortical network statistics, we show that clustered convergence of axons from three to four different co-active ensembles is likely even in randomly connected networks, leading to representation of arbitrary input combinations in at least 10 target neurons in a 100,000 population. I

SUBMITTER: Somashekar BP 

PROVIDER: S-EPMC11759408 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature