William Chapman Neuromorphic Computing
23 peer-reviewed · 1 book chapter

Publications

Full record, most recent first. Expand any entry for the citation and a one-line summary. Also on Google Scholar.

‡ marks a mentee as first or co-first author.

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23

Charge-Domain Leaky-Integrate-and-Fire Neuron with Tunable Parameters Using Ferroelectric Non-Volatile Capacitors

P. Megginson, M. Vadlamani, J. Jia, G. W. Chapman, S. Cardwell, S. Yu

ICONS 2026
Abstract

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 2026 PDF ↗
Abstract

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

NICE 2026
Abstract

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

GOMACTech 2026 PDF ↗
Abstract

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

ICONS 2025 PDF ↗
Abstract

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.

18

Embedded Neurally Inspired Visual Processing

G. W. Chapman, F. S. Chance

GLSVLSI 2025 PDF ↗
Abstract

Invited paper, Great Lakes Symposium on VLSI 2025, pp. 893–897. How insect-inspired visual circuits map onto embedded, energy-constrained silicon.

17

Self-Supervised Mapping and Localization by Predictive Learning

G. W. Chapman, A. S. Alexander, F. S. Chance, M. E. Hasselmo

ICONS 2024 PDF ↗
Abstract

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

NICE 2024 PDF ↗
Abstract

Neuro-Inspired Computational Elements 2024, pp. 1–7. Intracellular dynamics recover trainability in recurrent networks with binary activations.

15

Predictive Learning by a Burst-Dependent Learning Rule

G. W. Chapman, M. E. Hasselmo

Neurobiol. Learn. Mem. 2023 PDF ↗
Abstract

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

Cell Reports 2022 PDF ↗
Abstract

Cell Reports 38(10):110504. Parietal populations combine self-motion with goal structure rather than encoding either alone.

13

Neural Responses in Retrosplenial Cortex Associated with Environmental Alterations

L. C. Carstensen, A. S. Alexander, G. W. Chapman, A. J. Lee, M. E. Hasselmo

iScience 2021 PDF ↗
Abstract

iScience 24:103377.

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

Neuroscience 2020 PDF ↗
Abstract

Review: what neural models could say about metabotropic neuromodulation, and where they are silent.

11

Neurophysiological Coding of Space and Time in Hippocampus, Entorhinal and Retrosplenial Cortex

A. S. Alexander, J. C. Robinson, H. Dannenberg, N. R. Kinsky, S. J. Levy, W. Mau, G. W. Chapman, D. W. Sullivan, M. E. Hasselmo

Brain Neurosci. Adv. 2020 PDF ↗
10

Neuronal Representation of Environmental Boundaries in Egocentric Coordinates

J. R. Hinman, G. W. Chapman, M. E. Hasselmo

Nature Communications 2019 PDF ↗
Abstract

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

Science Advances 2019 PDF ↗
Abstract

Science Advances, July 2019.

8

Systemic Anxiolytics Decrease Entorhinal Theta Frequency without Affecting Grid Fields

C. K. Monaghan, G. W. Chapman, M. E. Hasselmo

Neuroscience 2017 PDF ↗
Abstract

Neuroscience 364:60–70. Theta frequency and grid geometry dissociate under pharmacological manipulation.

7

Multiple Running Speed Signals in Medial Entorhinal Cortex

J. R. Hinman, M. P. Brandon, J. R. Climer, G. W. Chapman, M. E. Hasselmo

Neuron 2016 PDF ↗
Abstract

Neuron 91(3):666–679. More than one speed code coexists in entorhinal cortex.

6

Post-Inhibitory Rebound Spikes in Rat Medial Entorhinal Layer II/III Principal Cells

M. Ferrante, C. F. Shay, Y. Tsuno, G. W. Chapman, M. E. Hasselmo

Cerebral Cortex 2016 PDF ↗
Abstract

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

Brain Research 2015 PDF ↗
Abstract

Brain Research 1621:355–367.

4

Rebound Spiking Properties of Mouse Medial Entorhinal Cortex Neurons in Vivo

Y. Tsuno, G. W. Chapman, M. E. Hasselmo

Eur. J. Neurosci. 2015 PDF ↗
Abstract

European Journal of Neuroscience 42:2974–2984.

3

Rebound Spiking in Layer II Entorhinal Stellate Cells: Possible Mechanism of Grid Cell Function

C. F. Shay, M. Ferrante, G. W. Chapman, M. E. Hasselmo

Neurobiol. Learn. Mem. 2015 PDF ↗
2

Gray & White Matter Tissue Contrast Differentiates MCI Converters from Non-Converters

A. L. Jefferson, K. A. Gifford, S. Damon, G. W. Chapman, D. Liu, J. Sparling, V. Dobromyslin, D. Salat

Brain Imaging Behav. 2015 PDF ↗
Abstract

Brain Imaging and Behavior 9:141–148. From the earlier clinical-imaging work at the start of my career.

1

Subjective Memory Complaint Relates Only to Verbal Episodic Memory in MCI

K. Gifford, D. Liu, S. M. Damon, G. W. Chapman, R. R. Romano, L. R. Samuels, Z. Lu, A. L. Jefferson

J. Alzheimers Dis. 2015 PDF ↗
Abstract

Journal of Alzheimer’s Disease 44:309–318.

Book chapters

2024 Coding of Space and Time for Memory Function M. E. Hasselmo, J. C. Robinson, P. A. LaChance, L. K. Wilmerding, G. W. Chapman In Time, Space and Memory · Oxford University Press