Electrophysiologically Informed Neuromorphic Spiking Networks for Spatial Navigation

Lear Cohen, Hadar Cohen Duwek, Elishai Ezra Tsur
Proceedings of the First Workshop on NeuroAI Multimodal Intelligence @ AAAI 2026, PMLR 308:1-9, 2026.

Abstract

Spatial memory underlies the mental encoding, storage, and retrieval of spatial representations that support navigation in intricate environments. Conventional models of navigation accentuate the role of place and grid cells as principal neural substrates. However, recent findings in teleost fish, the most diverse vertebrate class, suggest that navigation in these species depends primarily on boundary vector cells (BVCs) and hydrostatic pressure (HP) cues. In this study, we designed a neuromorphic spiking neural network (SNN) for spatial navigation, directly informed by electrophysiological recordings from the goldfish telencephalon. Within this architecture, BVC populations mediated obstacle avoidance, while HP-sensitive units provided a vertical reference for goal-oriented trajectory planning. Our results demonstrate that efficient navigation can emerge without explicit positional coding, consistent with experimental observations of low firing rates and limited neuronal populations in the fish telencephalon. The proposed framework thus establishes a compact and biologically grounded model for fish-inspired neuromorphic navigation that remains robust and scalable across naturalistic conditions.

Cite this Paper

BibTeX
@InProceedings{pmlr-v308-cohen26a, title = {Electrophysiologically Informed Neuromorphic Spiking Networks for Spatial Navigation}, author = {Cohen, Lear and Duwek, Hadar Cohen and Tsur, Elishai Ezra}, booktitle = {Proceedings of the First Workshop on NeuroAI Multimodal Intelligence @ AAAI 2026}, pages = {1--9}, year = {2026}, editor = {Abbasi-Asl, Reza and Iqbal, Asim and Ito, Shinya and Arkhipov, Anton and Sanborn, Sophia}, volume = {308}, series = {Proceedings of Machine Learning Research}, month = {27 Jan}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v308/main/assets/cohen26a/cohen26a.pdf}, url = {https://proceedings.mlr.press/v308/cohen26a.html}, abstract = {Spatial memory underlies the mental encoding, storage, and retrieval of spatial representations that support navigation in intricate environments. Conventional models of navigation accentuate the role of place and grid cells as principal neural substrates. However, recent findings in teleost fish, the most diverse vertebrate class, suggest that navigation in these species depends primarily on boundary vector cells (BVCs) and hydrostatic pressure (HP) cues. In this study, we designed a neuromorphic spiking neural network (SNN) for spatial navigation, directly informed by electrophysiological recordings from the goldfish telencephalon. Within this architecture, BVC populations mediated obstacle avoidance, while HP-sensitive units provided a vertical reference for goal-oriented trajectory planning. Our results demonstrate that efficient navigation can emerge without explicit positional coding, consistent with experimental observations of low firing rates and limited neuronal populations in the fish telencephalon. The proposed framework thus establishes a compact and biologically grounded model for fish-inspired neuromorphic navigation that remains robust and scalable across naturalistic conditions.} }
Endnote
%0 Conference Paper %T Electrophysiologically Informed Neuromorphic Spiking Networks for Spatial Navigation %A Lear Cohen %A Hadar Cohen Duwek %A Elishai Ezra Tsur %B Proceedings of the First Workshop on NeuroAI Multimodal Intelligence @ AAAI 2026 %C Proceedings of Machine Learning Research %D 2026 %E Reza Abbasi-Asl %E Asim Iqbal %E Shinya Ito %E Anton Arkhipov %E Sophia Sanborn %F pmlr-v308-cohen26a %I PMLR %P 1--9 %U https://proceedings.mlr.press/v308/cohen26a.html %V 308 %X Spatial memory underlies the mental encoding, storage, and retrieval of spatial representations that support navigation in intricate environments. Conventional models of navigation accentuate the role of place and grid cells as principal neural substrates. However, recent findings in teleost fish, the most diverse vertebrate class, suggest that navigation in these species depends primarily on boundary vector cells (BVCs) and hydrostatic pressure (HP) cues. In this study, we designed a neuromorphic spiking neural network (SNN) for spatial navigation, directly informed by electrophysiological recordings from the goldfish telencephalon. Within this architecture, BVC populations mediated obstacle avoidance, while HP-sensitive units provided a vertical reference for goal-oriented trajectory planning. Our results demonstrate that efficient navigation can emerge without explicit positional coding, consistent with experimental observations of low firing rates and limited neuronal populations in the fish telencephalon. The proposed framework thus establishes a compact and biologically grounded model for fish-inspired neuromorphic navigation that remains robust and scalable across naturalistic conditions.
APA
Cohen, L., Duwek, H.C. & Tsur, E.E.. (2026). Electrophysiologically Informed Neuromorphic Spiking Networks for Spatial Navigation. Proceedings of the First Workshop on NeuroAI Multimodal Intelligence @ AAAI 2026, in Proceedings of Machine Learning Research 308:1-9 Available from https://proceedings.mlr.press/v308/cohen26a.html.

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