Posts classified under: Affinity Groups

Steve Cannon, M.D., Ph.D.


The primary research interests of our laboratory are how ion channels regulate the electrical excitability of cells and how defects in these channels lead to human disease. In the past two decades, mutations of ion channel genes have been found to be the primary cause for over 100 human diseases. Our research program is focused on the mechanistic basis for a group of inherited conditions that alter the electrical excitability of skeletal muscle, including periodic paralysis and myotonia. We have characterized the gating defects of mutant channels, generated computational models of muscle excitability, and produced genetically-engineered mice to gain insights on the pathomechanisms of these disorders and to explore therapeutic interventions.

Mark Cohen, Ph.D.


Dr. Cohen received his undergraduate training in both engineering at MIT and biology at Stanford. His graduate work at the Rockefeller University concerned hormonally-modulated electrical signaling. He worked in the private sector from 1985 to 1990 developing applications and technology of magnetic resonance imaging, before accepting a faculty appointment at Harvard, where he directed the high-speed MR imaging laboratory, and ultimately contributing to the development of functional MRI performing seminal experiments in this field. Since arriving at UCLA in 1993 he has focused his work on applications and technologies of neuroimaging, and more recently, has been working toward the development of low-cost high performance MRI devices based on novel technologies adopted from low temperature physics.

Dean Buonomano, Ph.D.


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.


A selected list of publications:

Goel Anubhuti, Buonomano Dean V   Timing as an intrinsic property of neural networks: evidence from in vivo and in vitro experiments Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2014; 369(1637): 20120460.
Laje Rodrigo, Buonomano Dean V   Robust timing and motor patterns by taming chaos in recurrent neural networks Nature neuroscience, 2013; 16(7): 925-33.
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.
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.
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.
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.
Buonomano, D. V.   A learning rule for the emergence of stable dynamics and timing in recurrent networks, J Neurophysiol, 2005; 94(4): 2275-83.
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.
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.
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.
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.   A neural network model of temporal code generation and position-invariant pattern recognition, Neural Comput, 1999; 11(1): 103-16.
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, 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.