
Random feedforward connectivity, along with large enough neural ensembles (∼100 neurons for groups and ∼1000 neurons for sequences), is sufficient to lead to the convergence of groups or sequences consisting of 3-5 inputs onto small dendritic zones. This provides a substrate for downstream networks to decode arbitrary input patterns.


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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 3-4 different co-active ensembles is likely even in randomly connected networks, leading to representation of arbitrary input combinations in at least ten target neurons in a 100,000 population. In the presence of larger ensembles, spatiotemporally ordered convergence of 3-5 axons from temporally ordered ensembles is also likely. These active clusters result in higher neuronal activation in the presence of strong dendritic nonlinearities and low background activity. We mathematically and computationally demonstrate a tight interplay between network connectivity, spatiotemporal scales of subcellular electrical and chemical mechanisms, dendritic nonlinearities, and uncorrelated background activity. We suggest that dendritic clustered and sequence computation is pervasive, but its expression as somatic selectivity requires confluence of physiology, background activity, and connectomics.

### Competing Interest Statement

The authors have declared no competing interest.

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

likelihood-of-dendritic-computations-in-randomly-connected-feedforward-neural-networks


Likelihood of Dendritic Computations in Randomly Connected Feedforward Neural Networks
