17–18 Jun 2025
Virtual
Europe/Berlin timezone

A 3D Liquid Hippocampus Model for Memory Replay and Learning

P-5
18 Jun 2025, 14:53
2m
Zoom

Zoom

Poster & advertisement flash talk Poster teasers

Speaker

Xiao Shao

Description

We introduce a fully spiking, three-dimensional model of the hippocampus–neocortex loop that captures the complementary learning systems (CLS) functions [1] of rapid pattern separation, sequence completion and slow cortical consolidation. Dentate gyrus (DG), CA3 and CA1 are represented as spatially distinct 3-D liquid-state networks [2] whose connectivity mirrors hippocampal anatomy: extremely sparse feed-forward DG → CA3 projections support pattern separation, dense recurrent links within CA3 enable auto-completion, and plastic CA1 → cortex pathways transfer reactivated traces to long-term memory. Intrinsic θ–γ oscillations emerge from local inhibition and periodically trigger sharp-wave replay events. The model is embedded in a minimal T-maze task in which an agent’s position is encoded by place-cell activity and decoded by a basal-ganglia read-out that selects actions through deep learning. Preliminary simulations indicate that sparse DG input markedly enhances CA3 pattern separation, while replay-driven potentiation at CA1 → cortex synapses clearly improves post-sleep maze performance. Conversely, reducing DG sparsity, which is an analogue of early Alzheimer pathology, induces pronounced place-field overlap, increases recall errors and degrades spatial navigation. These results demonstrate that large-scale, 3-D spiking substrates can reproduce key hippocampal dynamics and their behavioural consequences, positioning NEST as a practical platform for studying memory consolidation, replay and disease-related degeneration in silico.

References

[1] McClelland J L, McNaughton B L, O'Reilly R C. Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory[J]. Psychological review, 1995, 102(3): 419.
[2] Maass W, Natschläger T, Markram H. Real-time computing without stable states: A new framework for neural computation based on perturbations[J]. Neural computation, 2002, 14(11): 2531-2560.

Preferred form of presentation Poster & advertising flash talk
Topic area Simulator technology and performance
Keywords Liquid state machine, Hippocampus, neocortex, complementary learning systems
Speaker time zone UTC+9
I agree to the copyright and license terms Yes
I agree to the declaration of honor Yes

Primary authors

Xiao Shao Prof. Danilo Vargas (Kyushu University)

Presentation materials

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