Learning Hippo: Multi-attractor Dynamics and Stability Effects in a Biologically Detailed CA3 Extension of Hopfield Networks
Daniele Corradetti, Renato Corradetti

TL;DR
This paper introduces a biologically detailed extension of Hopfield networks for CA3, demonstrating multi-attractor dynamics, associative recall, and reduced variance, highlighting architecture-specific effects in pattern completion tasks.
Contribution
It presents a novel biologically detailed CA3 model with multiple neuron types and plasticity rules, revealing new multi-attractor and recall behaviors absent in minimal models.
Findings
Multi-attractor cross-seed behavior at K=5 with realistic inhibition.
Target-selective associative recall in paired memory at K≥5.
Reduced cross-seed variance compared to minimal models.
Abstract
We present a biologically detailed extension of the classical Hopfield/Marr auto-associative memory model for CA3, implementing ten populations (two asymmetric pyramidal subtypes, eight GABAergic interneuron classes), forty-seven compartments, multi-rule plasticity (recurrent Hebb, BCM anti-saturation, mossy-fiber short-term, endocannabinoid iLTD, burst-gated Hebb), and a bimodal cholinergic encoding/consolidation cycle. Evaluated on pattern completion across auto-associative, associative, and temporal regimes, and on a controlled inhibitory-proportion manipulation at , the full architecture exhibits \emph{three qualitative signatures absent from a minimal Hopfield baseline}: (i)~multi-attractor cross-seed behaviour at with biologically realistic inhibitory proportions, where two of five seeds converge to positive attractors with margin (Cohen's…
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