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Posts classified under: Decision Making

Laura DeNardo, Ph.D.

Laura DeNardo, Ph.D.

Publications

A selected list of publications:

Laura A DeNardo, Cindy D Liu, William E Allen, Eliza L Adams, Drew Friedmann, Ehsan Dadgar-Kiani, Lisa Fu, Casey J Guenthner, Jin Hyung Lee, Marc Tessier-Lavigne, Liqun Luo   Temporal Evolution of Cortical Ensembles Promoting Remote Memory Retrieval , 2018; .
Berns Dominic S, DeNardo Laura A, Pederick Daniel T, Luo Liqun   Teneurin-3 controls topographic circuit assembly in the hippocampus Nature, 2018; 554(7692): 328-333.
Savas Jeffrey N, Wang Yi-Zhi, DeNardo Laura A, Martinez-Bartolome Salvador, McClatchy Daniel B, Hark Timothy J, Shanks Natalie F, Cozzolino Kira A, Lavallée-Adam Mathieu, Smukowski Samuel N, Park Sung Kyu, Kelly Jeffery W, Koo Edward H, Nakagawa Terunaga, Masliah Eliezer, Ghosh Anirvan, Yates John R   Amyloid Accumulation Drives Proteome-wide Alterations in Mouse Models of Alzheimer’s Disease-like Pathology Cell reports, 2017; 21(9): 2614-2627.
Allen William E, DeNardo Laura A, Chen Michael Z, Liu Cindy D, Loh Kyle M, Fenno Lief E, Ramakrishnan Charu, Deisseroth Karl, Luo Liqun   Thirst-associated preoptic neurons encode an aversive motivational drive Science (New York, N.Y.), 2017; 357(6356): 1149-1155.
DeNardo Laura, Luo Liqun   Genetic strategies to access activated neurons Current opinion in neurobiology, 2017; 45(6356): 121-129.
Savas JN, Ribeiro LF, Wierda KD, Wright R, DeNardo-Wilke LA, Chamma I, Wang Yi-Zhi, Zemla R, Lavallee- Adam M, Vennekens KM, O’Sullivan ML, Antonios JK, Hall EA, Thoumine O, Attie AD, Yates JR, Ghosh A, De Wit J   The Sorting Receptor SorCS1 Regulates Trafficking of Neurexin and AMPA receptors, Neuron, 2015; 87: 764-80.
DeNardo Laura A, Berns Dominic S, DeLoach Katherine, Luo Liqun   Connectivity of mouse somatosensory and prefrontal cortex examined with trans-synaptic tracing Nature neuroscience, 2015; 18(11): 1687-1697.
DeNardo Laura A, de Wit Joris, Otto-Hitt Stefanie, Ghosh Anirvan   NGL-2 regulates input-specific synapse development in CA1 pyramidal neurons Neuron, 2012; 76(4): 762-75.
Wilke Scott A, Hall Benjamin J, Antonios Joseph K, Denardo Laura A, Otto Stefanie, Yuan Bo, Chen Fading, Robbins Elissa M, Tiglio Katie, Williams Megan E, Qiu Zilong, Biederer Thomas, Ghosh Anirvan   NeuroD2 regulates the development of hippocampal mossy fiber synapses Neural development, 2012; 7(4): 9.
Dean Buonomano, Ph.D.

Dean Buonomano, Ph.D.

Faculty Member

Biography

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.

Publications

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.
Alicia Izquierdo, Ph.D.

Alicia Izquierdo, Ph.D.

Publications

A selected list of publications:

González VV, Zhang Y, Ashikyan SA, Rickard A, Yassine I, Romero-Sosa JL, Blaisdell AP, Izquierdo A (2024). A special role for anterior cingulate cortex, but not orbitofrontal cortex or basolateral amygdala, in choices involving information. Cerebral Cortex 34(4): bhae135.

Aguirre CG, Woo JH, Romero-Sosa JL, Rivera ZM, Tejada AN, Munier JJ, Perez J, Goldfarb M, Das K, Gomez M, Ye T, Pannu J, Evans K, O’Neill PR, Spigelman I, Soltani A, Izquierdo A (2024). Dissociable contributions of basolateral amygdala and ventrolateral orbitofrontal cortex to flexible learning under uncertainty. The Journal of Neuroscience 44(2): e0622232023.

Ye T, Romero-Sosa JL, Rickard A, Aguirre CG, Wikenheiser AM, Blair HT, Izquierdo A (2023). Theta oscillations in anterior cingulate cortex and orbitofrontal cortex differentially modulate accuracy and speed in flexible reward learning. Oxford Open Neuroscience 2: kvad005.

Hart EE, Blair GJ, O’Dell TJ, Blair HT, Izquierdo A (2020). Chemogenetic modulation and single-photon calcium imaging in anterior cingulate cortex reveal a mechanism for effort-based decisions. The Journal of Neuroscience 40 (29) 5628-5643.

Stolyarova A, Rakhshan M, Hart EE, O’Dell TJ, Peters MAK, Lau H, Soltani A, Izquierdo A (2019). Contributions of anterior cingulate cortex and basolateral amygdala to decision confidence and learning under uncertainty. Nature Communications 10: 4704.

Soltani A and Izquierdo A (2019). Adaptive learning under expected and unexpected uncertainty. Nat Rev Neurosci doi: 10.1038/s41583-019-0180-y.

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