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Dynamics of Neural Networks on a Planar Patch-Clamp Array
Training, Identification, and Control

Supported by the NSF 2008 Emerging Frontiers in Research and Innovation program.


Team Members


Project Vision

    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 optimization theory.

    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. arXiv:1103.5431v1, 2011. [ http ]

  • Mark Tobenkin, Ian R. Manchester, Jennifer Wang, Alex Megretski, and Russ Tedrake. Convex optimization in identification of stable non-linear state space models. In Proceedings of the 49th IEEE Conference on Decision and Control (CDC 2010), extended version available online: arXiv:1009.1670 [math.OC], Dec 2010. [ .pdf ]

  • Jennifer Wang, Mazahir T. Hasan, and H. Sebastian Seung. Laser-evoked synaptic transmission in cultured hippocampal neurons expressing channelrhodopsin-2 delivered by adeno-associated virus. Journal of Neuroscience Methods, 183:165-175, 2009. [ .pdf ]

  • Alexandre Megretski. Convex optimization in robust identification of nonlinear feedback. In Proceedings of the 47th IEEE Conference on Decision and Control, pages 1370-1374, Cancun, Mexico, Dec 9-11 2008. [ .pdf ]

  • A. Starovoytov, J. Choi, and H. S. Seung. Light-directed electrical stimulation of neurons cultured on silicon wafers. J. Neurophysiology, 93:1090-1098, 2005. [ .pdf ]

  • H.S. Seung. Learning in spiking neural networks by reinforcement of stochastic synaptic transmission. Neuron., 40(6):1063-73., Dec 18 2003. [ .pdf ]

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