October 8, 2025Open Access

Effective computations for hippocampal place cell phenomena in sparse untrained random networks

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The random network model successfully accounts for various place cell phenomena, indicating flexibility.
Using simulations, the model explains positional tuning and remapping in a biologically plausible manner.
The study highlights the significance of using divided normalization for managing excitatory activity in networks.
Synaptic plasticity at network connections enhances understanding of exhibit overdispersion and memory tagging.

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Analysis examines place cell phenomena in sparse networks, suggesting synaptic plasticity influences connectivity.

Hurtado et al. (2025) studied this question.

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