| 2007 |
|---|
| 8 | EE | Douglas A. Baxter,
John H. Byrne:
Short-term plasticity in a computational model of the tail-withdrawal circuit in Aplysia.
Neurocomputing 70(10-12): 1993-1999 (2007) |
| 2006 |
|---|
| 7 | EE | Enrico Cataldo,
John H. Byrne,
Douglas A. Baxter:
Computational Model of a Central Pattern Generator.
CMSB 2006: 242-256 |
| 2005 |
|---|
| 6 | EE | Enrico Cataldo,
Marcello Brunelli,
John H. Byrne,
Evyatar Av-Ron,
Yidao Cai,
Douglas A. Baxter:
Computational Model of Touch Sensory Cells (T Cells) of the Leech: Role of the Afterhyperpolarization (AHP) in Activity-Dependent Conduction Failure.
Journal of Computational Neuroscience 18(1): 5-24 (2005) |
| 5 | EE | David B. Pettigrew,
Paul Smolen,
Douglas A. Baxter,
John H. Byrne:
Dynamic Properties of Regulatory Motifs Associated with Induction of Three Temporal Domains of Memory in Aplysia.
Journal of Computational Neuroscience 18(2): 163-181 (2005) |
| 4 | EE | Randall D. Hayes,
John H. Byrne,
Steven J. Cox,
Douglas A. Baxter:
Estimation of single-neuron model parameters from spike train data.
Neurocomputing 65-66: 517-529 (2005) |
| 1994 |
|---|
| 3 | EE | Jennifer L. Raymond,
Douglas A. Baxter,
Dean V. Buonomano,
John H. Byrne:
Response to letter by Gaudiano et al.
Neural Networks 7(2): 406-407 (1994) |
| 1992 |
|---|
| 2 | EE | Jennifer L. Raymond,
Douglas A. Baxter,
Dean V. Buonomano,
John H. Byrne:
A learning rule based on empirically-derived activity-dependent neuromodulation supports operant conditioning in a small network.
Neural Networks 5(5): 789-803 (1992) |
| 1990 |
|---|
| 1 | EE | Dean V. Buonomano,
Douglas A. Baxter,
John H. Byrne:
Small networks of empirically derived adaptive elements simulate some higher-order features of classical conditioning.
Neural Networks 3(5): 507-523 (1990) |
