Timing is a critical element of most behaviors and of many cognitive tasks, yet we have little understanding of how neural circuits estimate, remember and control time intervals or generate temporal sequences of activity. A new class of network models will be studied that show great promise for uncovering the dynamic mechanisms, operating at the neural circuit level, supporting sequence generation and timing computations. These models will be built from a generic network structure through the addition of tuned feedback loops. This allows the same network to perform many different tasks and resembles the reconfiguration of existing circuits that occurs when a new task is learned. Constructing realistic models that perform complex tasks raises the possibility of making deep connections between the dynamic mechanisms operating in model and real neuronal networks. Three steps are required to fulfill this promise, and these are the three specific aims of the proposal. First, a study will be undertaken to determine what motor and cognitive tasks network models are capable of performing and to relate their level of performance to that of animals and humans performance analogous tasks. Second, the models will be made realistic enough to make definitive and predicted statements about experimental data. Third, an approach will be developed so that the network models can function as a tool to uncover the dynamic mechanisms operating in real neural circuits. For this purpose a collaboration has been established with experimental colleagues acquiring multi-electrode recordings from monkeys performing delayed-reaching tasks. Successfully accomplishing these three aims will significantly advance understanding of how timing arises from neural circuit dynamics and lead to hypotheses about circuit malfunctions that cause defects in timing estimation and movement initiation and control.

Public Health Relevance

The proposed research will use network models and parallel analyses of models and experimental data to reveal the dynamic mechanisms by which motor sequences are initiated and carried out and other timing-related tasks are performed. Parkinson's disease is characterized by difficulty in initiating voluntary movements and by abnormalities of timing estimation. This work thus has the potential to illuminate our understanding of motor and cognitive tasks involving timing in both healthy and diseased states.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH093338-04
Application #
8613321
Study Section
Special Emphasis Panel (ZRG1-IFCN-H (02))
Program Officer
Glanzman, Dennis L
Project Start
2011-05-01
Project End
2016-02-29
Budget Start
2014-03-18
Budget End
2015-02-28
Support Year
4
Fiscal Year
2014
Total Cost
$390,140
Indirect Cost
$140,140
Name
Columbia University
Department
Neurosciences
Type
Schools of Medicine
DUNS #
621889815
City
New York
State
NY
Country
United States
Zip Code
10032
Rubin, Ran; Abbott, L F; Sompolinsky, Haim (2017) Balanced excitation and inhibition are required for high-capacity, noise-robust neuronal selectivity. Proc Natl Acad Sci U S A 114:E9366-E9375
Babadi, Baktash; Abbott, L F (2016) Stability and Competition in Multi-spike Models of Spike-Timing Dependent Plasticity. PLoS Comput Biol 12:e1004750
Churchland, Anne K; Abbott, L F (2016) Conceptual and technical advances define a key moment for theoretical neuroscience. Nat Neurosci 19:348-9
Gabitto, Mariano I; Pakman, Ari; Bikoff, Jay B et al. (2016) Bayesian Sparse Regression Analysis Documents the Diversity of Spinal Inhibitory Interneurons. Cell 165:220-233
Lalazar, Hagai; Abbott, L F; Vaadia, Eilon (2016) Tuning Curves for Arm Posture Control in Motor Cortex Are Consistent with Random Connectivity. PLoS Comput Biol 12:e1004910
Abbott, L F; DePasquale, Brian; Memmesheimer, Raoul-Martin (2016) Building functional networks of spiking model neurons. Nat Neurosci 19:350-5
Sussillo, David; Churchland, Mark M; Kaufman, Matthew T et al. (2015) A neural network that finds a naturalistic solution for the production of muscle activity. Nat Neurosci 18:1025-33
Kato, Saul; Xu, Yifan; Cho, Christine E et al. (2014) Temporal responses of C. elegans chemosensory neurons are preserved in behavioral dynamics. Neuron 81:616-28
Stern, M; Sompolinsky, H; Abbott, L F (2014) Dynamics of random neural networks with bistable units. Phys Rev E Stat Nonlin Soft Matter Phys 90:062710
Kennedy, Ann; Wayne, Greg; Kaifosh, Patrick et al. (2014) A temporal basis for predicting the sensory consequences of motor commands in an electric fish. Nat Neurosci 17:416-22

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