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Dean Buonomano, Ph.D.

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Los Angeles, CA 90095

Los Angeles, CA 90095

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Department / Division Affiliations
Professor, Behavioral Neuroscience, Neurobiology
Member, ACCESS Program: Dept. of Neurobiology, Access Biomedical Physics Home Area, Access Molecular, Cellular and Integrative Physiology Home Area, Access Neuroscience Home Area, Brain Research Institute, Neuroengineering Training Program, Neuroscience IDP


NEURAL DYNAMICS: THE NEURAL BASIS OF LEARNING AND MEMORY AND TEMPORAL PROCESSING Behavior and cognition are not the product of isolated neurons, but rather emerge from the dynamics of interconnected neurons embedded in complex recurrent networks. Significant progress has been made towards understanding cellular and synaptic properties in isolation, as well as in establishing which areas of the brain are active during specific tasks. However, elucidating how the activity of hundreds of thousands of neurons within local cortical circuits underlie computations remains an elusive and fundamental goal in neuroscience. The primary goal of my laboratory is to understand how functional computations emerge from networks of neurons. One computation we are particularly interested in is how the brain tells time. Temporal processing refers to your ability to distinguish the interval and duration of sensory stimuli, and is a fundamental component of speech and music perception. To answer these questions the main approaches in my laboratory involve: (1) In Vitro Electrophysiology: Using acute and chronic brain slices we study the spatio-temporal dynamics of cortical circuits, as well as the learning rules that allow networks to develop, organize and perform computations ??? that is, to learn. (2) Computer Simulations: Computer models are used to simulate how networks perform computations, as well as test and generate predictions in parallel with our experimental research. (3) Human Psychophysics: We also use human pyschophysical experiments to characterize learning and generalization of temporal tasks, such as interval discrimination.


van Wassenhove V, Buonomano DV, Shimojo S, Shams L. Distortions of subjective time perception within and across senses. PLoS ONE 2008; 3(1): e1437.
Johnson, Hope A. Buonomano, Dean V. Development and Plasticity of Spontaneous Activity and Up States in Cortical Organotypic Slices. J. Neurosci. 2007; 27(22): 5915-5925.
Buonomano, D. V. The biology of time across different scales. Nat Chem Biol. 2007; 3(10): 594-7.
Karmarkar, U. R. Buonomano, D. V. Timing in the absence of clocks: encoding time in neural network states. Neuron. 2007; 53(3): 427-38.
Karmarkar, U. R. Buonomano, D. V. Different forms of homeostatic plasticity are engaged with distinct temporal profiles. Eur J Neurosci 2006; 23(6): 1575-84.
Buonomano, D. V. A learning rule for the emergence of stable dynamics and timing in recurrent networks. J Neurophysiol 2005; 94(4): 2275-83.
Mauk, M. D. Buonomano, D. V. The Neural Basis of Temporal Processing. Annual Rev. Neuroscience 2004; 27: 304-340.
Karmarkar, U. R. Buonomano, D. V. Temporal specificity of perceptual learning in an auditory discrimination task. Learn Mem 2003; 10(2): 141-7.
Buonomano, D. V. Timing of Neural Responses in Cortical Organotypic Slices. Proc. Natl. Acad. Sci. USA 2003; 100: 4897-4902.
Karmarkar, U. R. Buonomano, D. V. A model of spike-timing dependent plasticity: one or two coincidence detectors?. J Neurophysiol 2002; 88(1): 507-13.
Buonomano, D. V. Karmarkar, U. R. How do we tell time?. Neuroscientist 2002; 8(1): 42-51.
Karmarkar, U. R. Najarian, M. T. Buonomano, D. V. Mechanisms and significance of spike-timing dependent plasticity. Biol Cybern 2002; 87(5-6): 373-82.
Buonomano, D. V. Decoding temporal information: a model based on short-term synaptic plasticity. J Neurosci 2000; 20: 1129-1141.
Buonomano, D. V. Distinct functional types of associative long-term potentiation in neocortical and hippocampal pyramidal neurons. J Neurosci 1999; 19: 6748-6754.
Buonomano, D. V. Merzenich, M. M. Cortical plasticity: from synapses to maps. Annual Rev. Neuroscience 1998; 21: 149-186.
Buonomano, D. V. Merzenich, M. M. Temporal information transformed into a spatial code by a neural network with realistic properties. Science 1995; 267: 1028-30.
Buonomano Dean V, Laje Rodrigo Population clocks: motor timing with neural dynamics. Trends in cognitive sciences. 2010; 14(12): 520-7.
Johnson Hope A, Goel Anubhuthi, Buonomano Dean V Neural dynamics of in vitro cortical networks reflects experienced temporal patterns. Nature neuroscience. 2010; 13(8): 917-9.
Liu Jian K, Buonomano Dean V Embedding multiple trajectories in simulated recurrent neural networks in a self-organizing manner. The Journal of neuroscience : the official journal of the Society for Neuroscience. 2009; 29(42): 13172-81.
Buonomano Dean V Harnessing chaos in recurrent neural networks. Neuron. 2009; 63(4): 423-5.
Buonomano Dean V, Bramen Jennifer, Khodadadifar Mahsa Influence of the interstimulus interval on temporal processing and learning: testing the state-dependent network model. Philosophical transactions of the Royal Society of London. Series B, Biological sciences. 2009; 364(1525): 1865-73.
Carvalho Tiago P, Buonomano Dean V Differential effects of excitatory and inhibitory plasticity on synaptically driven neuronal input-output functions. Neuron. 2009; 61(5): 774-85.
Buonomano Dean V, Maass Wolfgang State-dependent computations: spatiotemporal processing in cortical networks. Nature reviews. Neuroscience. 2009; 10(2): 113-25.
Johnson Hope A, Buonomano Dean V A method for chronic stimulation of cortical organotypic cultures using implanted electrodes. Journal of neuroscience methods. 2009; 176(2): 136-43.
Eagleman, D. M. Tse, P. U. Buonomano, D. Janssen, P. Nobre, A. C. Holcombe, A. O. Time and the brain: how subjective time relates to neural time. J Neurosci 2005; 25(45): 10369-71.
Dong, H. W. Buonomano, D. V. A technique for repeated recordings in cortical organotypic slices. J Neurosci Methods 2005; 146(1): 69-75.
Marder, C. P. Buonomano, D. V. Timing and balance of inhibition enhance the effect of long-term potentiation on cell firing. J Neurosci 2004; 24(40): 8873-84.
Marder, C. P. Buonomano, D. V. Differential effects of short- and long-term potentiation on cell firing in the CA1 region of the hippocampus. J Neurosci 2003; 23(1): 112-21.
Buonomano, D. V. Merzenich, M. A neural network model of temporal code generation and position-invariant pattern recognition. Neural Comput 1999; 11(1): 103-16.
Buonomano, D. V. Byrne, J. H. Long-term synaptic changes produced by a cellular analog of classical conditioning in Aplysia. Science 1990; 249(4967): 420-3.