Abstract

The mammalian hippocampus is involved in auto-association and hetero-association of declarative
memories. We employed a bio-inspired neural model of hippocampal CA1 region to systematically
evaluate its mean recall quality against different number of stored patterns, overlaps and active cells per
pattern. Model consisted of excitatory (pyramidal cells) and four types of inhibitory cells: axo-axonic,
basket, bistratified, and oriens lacunosum-moleculare cells. Cells were simplified compartmental models
with complex ion channel dynamics. Cells’ firing was timed to a theta oscillation paced by two distinct
neuronal populations exhibiting highly regular bursting activity, one tightly coupled to the trough and the
other to the peak of theta. During recall excitatory input to network excitatory cells provided context and
timing information for retrieval of previously stored memory patterns. Dendritic inhibition acted as a nonspecific
global threshold machine that removed spurious activity during recall. Simulations showed recall
quality improved when the network’s memory capacity increased as the number of active cells per pattern
decreased. Furthermore, increased firing rate of a presynaptic inhibitory threshold machine inhibiting a
network of postsynaptic excitatory cells has a better success at removing spurious activity at the network
level and improving recall quality than increased synaptic efficacy of the same threshold machine on the
same network of excitatory cells, while keeping its firing rate fixed.