Accepted to the International Conference on Neuromorphic Systems 2026. A leaky-integrate-and-fire neuron in the charge domain, with time constants set by ferroelectric non-volatile capacitors rather than by digital state.
22
Learned Adaptive Properties for Mitigation of Weight Perturbations in Embedded Spiking Networks
‡ S. Luca, T. P. Xiao, F. S. Chance, S. Agarwal, C. Teeter, G. W. Chapman
Frontiers in Neuroscience, 11 March 2026, doi:10.3389/fnins.2026.1766765. Learned adaptation lets embedded spiking networks tolerate analog weight perturbation; mentee first author.
21
Graph Reservoir Networks for Prediction of Spatiotemporal Systems
G. W. Chapman, J. D. Smith, C. Teeter, N. D. Jackson
Neuro-Inspired Computational Elements, March 2026. Reservoir dynamics on a graph as a cheap surrogate for petascale spatiotemporal prediction; a patent application followed in February 2026.
20
Spiking Neural Networks for Efficient Streaming Event Detection
‡ G. W. Chapman, M. T. Gahl, W. S. Wahby, C. Teeter, F. S. Chance, S. Agarwal, T. P. Xiao
Government Microcircuit Applications & Critical Technology, March 2026. Streaming event detection with spiking networks under hardware constraints; a mentee is first author.
19
Lateral Divisive Inhibition for Dynamic Gain Control
‡ G. W. Chapman, J. A. Boyle, N. Gilbert, T. P. Xiao, S. Agarwal, F. S. Chance
Divisive rather than subtractive inhibition, applied laterally across a pixel array, keeps response range usable as input intensity swings. Presented at ICONS 2025, Bellevue WA; the corresponding patent application was filed in November 2025.
Prediction as the training signal for GPS-free mapping and localization — the bridge between the retrosplenial recordings and the edge-deployment work.
16
Biological Dynamics Enabling Training of Binary Recurrent Networks
G. W. Chapman, C. Teeter, S. Agarwal, T. P. Xiao, P. Hays, S. S. Musuvathy
A hierarchical module with recurrent feedback — a simplified neocortical microcircuit — predicts spatiotemporal trajectories at the input layer by minimising temporal error, and successive layers learn representations increasingly removed from raw sensory space.
14
Adaptive Integration of Self-Motion and Goals in Posterior Parietal Cortex
A. S. Alexander, J. C. Tung, G. W. Chapman, A. M. Conner, L. E. Shelley, M. E. Hasselmo, D. A. Nitz
Nature Communications 10:2772. Boundary coding referenced to the animal rather than to the room — the egocentric frame that later drives the mapping models.
9
Egocentric Boundary Vector Tuning of the Retrosplenial Cortex
A. S. Alexander, L. C. Carstensen, J. R. Hinman, F. Raudies, G. W. Chapman, M. E. Hasselmo
In vivo, in vitro and computational characterisation of rebound spiking — the intracellular dynamics that reappear in the binary-network hardware papers.
5
Head Direction Is Coded More Strongly than Movement Direction in Entorhinal Neurons
F. Raudies, M. P. Brandon, G. W. Chapman, M. E. Hasselmo
Accepted to the International Conference on Neuromorphic Systems 2026. A leaky-integrate-and-fire neuron in the charge domain, with time constants set by ferroelectric non-volatile capacitors rather than by digital state.
22
Learned Adaptive Properties for Mitigation of Weight Perturbations in Embedded Spiking Networks
‡ S. Luca, T. P. Xiao, F. S. Chance, S. Agarwal, C. Teeter, G. W. Chapman
Frontiers in Neuroscience, 11 March 2026, doi:10.3389/fnins.2026.1766765. Learned adaptation lets embedded spiking networks tolerate analog weight perturbation; mentee first author.
20
Spiking Neural Networks for Efficient Streaming Event Detection
‡ G. W. Chapman, M. T. Gahl, W. S. Wahby, C. Teeter, F. S. Chance, S. Agarwal, T. P. Xiao
Government Microcircuit Applications & Critical Technology, March 2026. Streaming event detection with spiking networks under hardware constraints; a mentee is first author.
19
Lateral Divisive Inhibition for Dynamic Gain Control
‡ G. W. Chapman, J. A. Boyle, N. Gilbert, T. P. Xiao, S. Agarwal, F. S. Chance
Divisive rather than subtractive inhibition, applied laterally across a pixel array, keeps response range usable as input intensity swings. Presented at ICONS 2025, Bellevue WA; the corresponding patent application was filed in November 2025.
Neuro-Inspired Computational Elements 2024, pp. 1–7. Intracellular dynamics recover trainability in recurrent networks with binary activations.
Predictive learning & dynamics
Hierarchical, feedback-driven models of neocortex that learn from temporal error — plus the rebound dynamics and reservoir surrogates that come out of them.
21
Graph Reservoir Networks for Prediction of Spatiotemporal Systems
G. W. Chapman, J. D. Smith, C. Teeter, N. D. Jackson
Neuro-Inspired Computational Elements, March 2026. Reservoir dynamics on a graph as a cheap surrogate for petascale spatiotemporal prediction; a patent application followed in February 2026.
17
Self-Supervised Mapping and Localization by Predictive Learning
G. W. Chapman, A. S. Alexander, F. S. Chance, M. E. Hasselmo
Prediction as the training signal for GPS-free mapping and localization — the bridge between the retrosplenial recordings and the edge-deployment work.
15
Predictive Learning by a Burst-Dependent Learning Rule
A hierarchical module with recurrent feedback — a simplified neocortical microcircuit — predicts spatiotemporal trajectories at the input layer by minimising temporal error, and successive layers learn representations increasingly removed from raw sensory space.
12
The Unexplored Territory of Neural Models: Potential Guides for Exploring Metabotropic Neuromodulation
M. E. Hasselmo, A. S. Alexander, A. Hoyland, J. C. Robinson, M. J. Bezaire, G. W. Chapman, A. Saudargiene, L. C. Carstensen, H. Dannenberg
In vivo, in vitro and computational characterisation of rebound spiking — the intracellular dynamics that reappear in the binary-network hardware papers.
4
Rebound Spiking Properties of Mouse Medial Entorhinal Cortex Neurons in Vivo
Nature Communications 10:2772. Boundary coding referenced to the animal rather than to the room — the egocentric frame that later drives the mapping models.
9
Egocentric Boundary Vector Tuning of the Retrosplenial Cortex
A. S. Alexander, L. C. Carstensen, J. R. Hinman, F. Raudies, G. W. Chapman, M. E. Hasselmo
Two early clinical-imaging papers sit outside these threads; both are in the numbered view.
Book chapters
2024Coding of Space and Time for Memory FunctionM. E. Hasselmo, J. C. Robinson, P. A. LaChance, L. K. Wilmerding, G. W. ChapmanIn Time, Space and Memory · Oxford University PressPDF ↗