Dynamics of Neural Networks on a Planar
Supported by the NSF 2008 Emerging Frontiers in Research and Innovation program.
Training, Identification, and Control
- Marsela Jorgolli, Harvard Physics
- Ian Manchester, MIT CSAIL/LIDS
- Alexandre Megretski, MIT LIDS
- Hongkun Park, Harvard Chemistry and Physics
- H. Sebastian Seung, MIT Brain & Cognitive Sciences, Howard Hughes Medical Institute
- Alex Shalek, Harvard
- Alec Shkolnik, MIT CSAIL
- Russ Tedrake, MIT CSAIL
- Mark Tobenkin, MIT CSAIL/LIDS
- Ashwin Vishwanathan, MIT Brain & Cognitive Sciences
- Jen Wang, MIT Brain & Cognitive Sciences
- Myung-Han Yoon, Harvard
[Team Members Only Area]
Micro-fabricated patch-clamp arrays have the potential to
revolutionize our understanding of learning and dynamics in networks
of cultured neurons by providing simultaneous intracellular
measurement and stimulation of tens to hundreds of connected
neurons. These intracellular electrodes expose and control neural
dynamics with temporal and spatial resolutions unparalleled by
extracellular and optical methods. We are developing these patch
clamp arrays atop a micro-fluidic substrate which, beyond facilitating
the patch-clamp seal, can be used for spatially-targeted application
and reuptake of chemical signals.
These devices enable a dream experiment. With this fantastically rich
and dense instrumentation, we can probe, excite, and even reshape the
dynamics of the neural network in ways that were previously
impossible. Combined with a novel and rigorous concept for nonlinear
system identification, we will develop high-fidelity models to
describe neural activity at the network level. These models can be
used to expose the topology of a cultured network, but also to
investigate fundamental issues in controllability and dynamics. We
intend to demonstrate our command of the network dynamics by closing a
feedback loop through our instrumentation to robustly produce an
activity pattern at a specified set of output neurons. Going further,
we will build upon optimization theory and computational theories of
reward-driven learning in the brain to operantly condition these
output activity patterns. A coupling between mathematical theories of
optimization and the learning dynamics of cultured neural networks
would transform our understanding of the brain - providing an
explanation of synaptic plasticity with close correspondence to
Beyond understanding biological networks, experiments with
reward-driven learning can begin to reveal the brain's secrets for
sequential decision making. In particular, engineering benchmarks from
control theory and robotics reveal limitations in state-of-the-art
optimization when the reward is nonconvex, the parameter space is
large, the optimal policies are discontinuous, and/or when the
dynamics of the plant contain feedback delay. With a trainable network
of real neurons at our command, we have an opportunity to carefully
explore nature's solution to these engineering challenges.
Ian R. Manchester, Mark M. Tobenkin, and Jennifer Wang.
Identification of nonlinear systems with stable oscillations.
[ http ]
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Laser-evoked synaptic transmission in cultured hippocampal neurons
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Journal of Neuroscience Methods, 183:165-175, 2009.
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Convex optimization in robust identification of nonlinear feedback.
In Proceedings of the 47th IEEE Conference on Decision and
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[ .pdf ]
A. Starovoytov, J. Choi, and H. S. Seung.
Light-directed electrical stimulation of neurons cultured on silicon
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