:Articles

31.  Johnson, H.A. and Buonomano, D.V. A  method for chronic stimulation of cortical organotypic cultures using implanted electrodes.  J. Neurosci. Methods 176: 136-143, 2009.   (PDF)

30.  van Wassenhove V, Buonomano DV, Shimojo S, Shams L. Distortions of Subjective Time Perception Within and Across Senses. PLoS ONE 3:e1437. 2008. (PDF)

29.  Buonomano, D.V. The biology of time across different scales. Nat. Chem. Bio. 3:594-597, 2007.  (PDF)

26.  Johnson, H.A. and Buonomano, D.V. Development and plasticity of spontaneous activity and UP states in cortical organotypic slices.  J. Neuroscience, 27: 5915-5925, 2007.   (PDF)

27.  Karmarkar, U.R. and Buonomano, D.V. Timing in the absence of clocks: Encoding time in neural network states. Neuron 53: 427-438, 2007.   (PDF)

26.  Karmarkar, U.R. and Buonomano, D.V. Bi-directional homeostatic inhibitory plasticity. European. J. Neuroscience, 23: 1575-1584, 2006.   (PDF)

25.  Eagleman DM, Tse PU, Janssen P, Nobre AC, Buonomano D, Holcombe AO. Time and the brain: how subjective time relates to neural time. J. Neuroscience 25: 10369–10371, 2005.  (PDF)

24.  Buonomano, D.V. A Learning Rule for the Emergence of Stable Dynamics and Timing in Recurrent Networks. J. Neurophysiol. 94: 2275-2283, 2005.  (PDF)

23.  Dong, H. and Buonomano, D.V. A technique for repeated recordings from cortical organotypic slices. J. Neurosci. Methods 146: 69-75, 2005.  (PDF)

22.  Marder, C.P. and Buonomano D.V. Timing and Balance of Inhibition Enhance the Effect of LTP on Cell Firing. J. Neurosci. 24: 8873-8884, 2004.  (PDF)

21.  Mauk M.D. and Buonomano D.V. The Neural Basis of Temporal Processing. Annual Rev. Neuroscience. 27: 304-340, 2004.  (PDF)

20.  Buonomano, D.V. Timing of Neural Responses in Cortical Organotypic Slices. Proc. Natl. Acad. Sci. USA. 100: 4897-4902, 2003.  (PDF)

19. Karmarkar, U. and Buonomano D.V. Temporal specificity of perceptual learning in an auditory discrimination task. Learning & Memory 10:141-147, 2003.  (PDF)

18.  Marder, C.P. and Buonomano D.V. Differential effects of short- and long-term potentiation on cell firing in the CA1 region of the hippocampus. J. Neurosci. 23: 112-121, 2003.  (PDF)

17.  Karmarkar, U.R. Najarian, M.T. and Buonomano, D.V. Mechanisms and significance of spike-timing dependent plasticity. Biol. Cybernetics 87: 373-382, 2002.  (PDF)

16.  Buonomano, D.V. and Karmarkar, U.R. How do we tell time? Neuroscientist 8: 42- 51, 2002.  (PDF)

15.  Karmarkar, U.R. and Buonomano, D.V. A Model of Spike-Timing Dependent Plasticity: One or Two Coincidence Detectors? J. Neurophysiol. 88:507-513, 2002. (PDF)

14.  Buonomano, D.V. Decoding temporal information: a model based on short-term synaptic plasticity. J. Neurosci. 20: 1129-1141, 2000.  (PDF)

13.  Buonomano, D.V. Distinct functional types of associative long-term potentiation in neocortical and hippocampal pyramidal neurons.  J. Neurosci. 19:6748-6754, 1999.  (PDF)

12.  Buonomano, D.V. and Merzenich, M.M. A neural network model of temporal code generation of position invariant pattern recognition. Neural Computation 11: 103-116, 1999. (PDF)

11.  Buonomano, D.V. and Merzenich M.M. Cortical plasticity: from synapses to maps. Annual Rev. Neuroscience 21: 149-186, 1998. (PDF)

10.  Buonomano, D.V. and Merzenich, M.M. Net interaction between different forms of short-term synaptic plasticity and slow-IPSPs in the hippocampus and auditory cortex. J. Neurophysiol. 80: 1765-1774,1998. (PDF)

9.   Buonomano, D.V., Hickmott, P.W., and Merzenich, M.M. Temporal to spatial transformations and context-sensitive plasticity in hippocampal slices.  Proc. Natl. Acad. Sci. USA. 94: 10403-10408, 1997. (PDF)

8.    Wright, B.A., Buonomano, D.V., Mahncke, H.W., Merzenich, M.M. Learning and generalization of auditory temporal-interval discrimination in humans. J. Neurosci. 17: 3956-3963, 1997. (PDF)

7.   Buonomano, D.V. and Merzenich, M.M. Associative synaptic plasticity in hippocampal CA1 neurons is not sensitive to unpaired presynaptic activity. J. Neurophysiol. 76: 631-636, 1996. (PDF)

6.   Buonomano, D.V. and Merzenich, M.M. Temporal information transformed into a spatial code by a neural network with realistic properties. Science 267: 1028-1030, 1995. (PDF)

5.    Buonomano, D.V. and Mauk, M.D. Neural network model of the cerebellum: temporal discrimination and the timing of motor responses. Neural Computation 6: 38-55, 1994. (PDF)

4.  Buonomano, D.V., Cleary, L.J. and Byrne, J.H. Inhibitory neuron produces heterosynaptic inhibition of the sensory-to-motor neuron in Aplysia. Brain Res.  577: 147 -150, 1992.

3.   Raymond, J.L., Baxter, D.A., Buonomano, D.V. and Byrne J.H. A learning rule based on empirically-derived activity-dependent neuromodulation supports operant conditioning in a small network. Neural Networks 5: 789-803, 1992.

2.   Buonomano, D.V, D.A. Baxter and Byrne, J.H. Simulations of small networks based on empirically derived adaptive elements predict some higher-order features of classical conditioning. Neural Networks 3: 507-527, 1990.

1.   Buonomano, D.V. and Byrne, J.H. Long-term synaptic changes produced by a cellular analog of classical conditioning in Aplysia. Science  249: 421-422, 1990. (PDF)

 :Chapters

7. Buonomano, D.V. and Carvalho T.P. (2007) Spike Timing Dependent Plasticity. In New Encyclopedia of Neuroscience. (ed. L.R. Squire), In press 2008.

6. Buonomano, D.V. and Johnson H.A. (2007) Cortical Plasticity: Mechanisms and Models. In New Encyclopedia of Neuroscience. (ed. L.R. Squire), In press 2008.

5. Raymond, J.L., Byrne, J.H. and Buonomano, D.V. Conditioning, Cellular and Network Schemes for Higher Order Features of Classical Conditioning. In Encyclopedia of Learning and Memory. (ed. J Byrne), pp. 98-100, 2003.

4. Buonomano, D.V. and Merzenich. M.M. Temporal Information processing: A Computational role for Paired-pulse Facilitation and Slow Inhibition.  In Neural-Network Models of Complex Behavior: Biobehavioral Foundations. (ed. J Donahoe, VP Dorsel), Elsevier - Amherst, pp. 129-139, 1997.

3. Baxter, D.A., Buonomano, D.V., Raymond, J.L., Cook, D.G., Kuenzi, F.M, Carew, T.J. and Byrne, J.H. Empirically derived adaptive elements and networks simulate associative learning. In Quantitative Analyses of Behavior: Neural Networks of Conditioning and Action (ed. ML Commons, S Grossberg, J. Staddon), Erlbaum - Hillsdale, pp. 13-52, 1991.

2. Byrne, J.H., Baxter, D.A., Buonomano, D.V., Cleary, L.J., Eskin, A., Goldsmith, J.R., McClendon, E., Nazif, F.A., Noel, F. and Scholz, K.P. Neural and molecular bases of nonassociative and associative learning in Aplysia, Ann. N.Y. Acad. Sci. 624: 124-148, 1991.

1. Byrne, J.H., Baxter, D.A., Buonomano, D.V. and Raymond, J.L. Neuronal and network determinants of simple and higher-order features of associative learning: Experimental and modeling approaches. In The Brain, Cold Spring Harbor Symposium on Quantitative Biology, Vol. 55, pp.175-186, 1991.