Posts classified under: Neurobiology

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.

Nicholas Brecha, Ph.D.


Neurochemical and Anatomical Pathways in the Vertebrate Retina that Mediate Vision Dr. Brecha’s major research interest is concerned with understanding the functional organization of the mammalian retina by elucidating its morphology and neurochemistry. Specific investigations are focused on defining the microcircuitry of the inner retina, evaluating the neurochemical organization and regulation of both its fast (amino acid) and slow (peptide) transmitter systems, and the function of bipolar, amacrine and ganglion cell populations, which are major retinal cell types that play critical roles in the processing of visual information. Recent investigations concerned with peptide-containing cell populations are defining the cellular expression patterns of tachykinin, somatostatin, neuropeptide Y and opiate receptors, and their functional role in modulating bipolar cell responsiveness. Morphological studies have shown that peptide receptor subtypes are selectively expressed by different populations of bipolar, amacrine and ganglion cells. These observations have provided important clues to the organization of the retinal microcircuits mediating different aspects of vision, as well as the sites of action of several previously identified retinal transmitter substances. A new research direction, developed over the past three years has been focused on determining the function of peptides in the retina. The rationale of these studies is to define the cellular actions of peptides found in the retina, which we hypothesize modulate cellular responsiveness, to influence ion channels and other intercellular messenger systems. Initial studies have focused on somatostatin; our findings demonstrate that this peptide inhibits both K+ and Ca2+ ion channels in the axonal terminals of bipolar cells and photoreceptors at low concentrations. Interestingly, these cells prominently express the somatostatin receptor subtype, sst2A suggesting this action is mediated through this receptor. These investigations provide further support for a role of somatostatin in the presynaptic modulation of transmitter release from retinal cells.