Small networks of empirically derived adaptive elements simulate some higher-order features of classical conditioning. - Dean V. Buonomano; Douglas A. Baxter; John H. Byrne (Neural Networks, 1990)

A learning rule based on empirically-derived activity-dependent neuromodulation supports operant conditioning in a small network. - Jennifer L. Raymond; Douglas A. Baxter; Dean V. Buonomano; John H. Byrne (Neural Networks, 1992)

Neural Network Model of the Cerebellum: Temporal Discrimination and the Timing of Motor Responses. - Dean V. Buonomano; Michael D. Mauk (Neural Comput., 1994)

Response to letter by Gaudiano et al. - Jennifer L. Raymond; Douglas A. Baxter; Dean V. Buonomano; John H. Byrne (Neural Networks, 1994)

A Neural Network Modell of Temporal Code Generation and Position Invariant Pattern Recognition. - Dean V. Buonomano; Michael Merzenich (Neural Comput., 1999)

Mechanisms and significance of spike-timing dependent plasticity. - Uma R. Karmarkar; Mark T. Najarian; Dean V. Buonomano (Biol. Cybern., 2002)

Unsupervised Formation of Vocalization-Sensitive Neurons: A Cortical Model Based on Short-Term and Homeostatic Plasticity. - Tyler P. Lee; Dean V. Buonomano (Neural Comput., 2012)

Encoding Time in Feedforward Trajectories of a Recurrent Neural Network Model. - N. F. Hardy; Dean V. Buonomano (Neural Comput., 2018)

Encoding time in neural dynamic regimes with distinct computational tradeoffs. - Shanglin Zhou; Sotiris C. Masmanidis; Dean V. Buonomano (PLoS Comput. Biol., 2022)