NSF ECCS-1545574
September 1, 2012 – August 31, 2017
$345,000
NSF
Division of Electrical and Communication Systems, Program on Control, Networks and Computational Intelligence
PI:
S. Ferrari
Co-PI:
P. Mazumder (University of Michigan)
Project Description
This project seeks to train the Central Complex (CX) of a behaving cockroach, by implementing a novel spike-based perturbative approach in-vivo. This approach relies on manipulating the neural activity rather than the synaptic weights, and determines patterns of spike trains that achieve the desired circuit-level response by modulating functional plasticity.
Research Goals
- In-vivo training of biological neurons in the cockroach CX
- Biophysically accurate and experimentally calibrated CX model
- Mathematical model of multiscale insect sensorimotor learning and behavior
- Translating cell-level neuronal activity and synaptic-level plasticity into system-level behavioral goals and adaptation
Peer-Reviewed Publications
- X. Zhang, G. Foderaro, C. Henriquez, S. Ferrari, “A Scalable Weight-Free Learning Algorithm for Regulatory Control of Cell Activity in Spiking Neuronal Networks,” International Journal of Neural Systems, Vol. 28, Iss. 02, March 2018. [PDF]
- P. Mazumder, D. Hu, I. Ebong, X. Zhang, Z. Xu, S. Ferrari, “Digital Implementation of a Virtual Insect Trained by Spike-timing Dependent Plasticity,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, Vol. 54, pp. 109-117, June 2016. [PDF]
- D. Hu, X. Zhang, Z. Xu, S. Ferrari, P. Mazumder, “Digital Implementation of a Spiking Neural Network (SNN) Capable of Spike-timing-dependent Plasticity (STDP) Learning,” Proc. of the IEEE 14th International Conference on Nanotechnology (IEEE NANO), Toronto, Canada, August 18-21, 2014. [PDF]
Presentations
- “Digital Implementation of a Spiking Neural Network (SNN) Capable of Spike-timing-dependent Plasticity (STDP) Learning,” Proc. of the IEEE 14th International Conference on Nanotechnology (IEEE NANO), Toronto, Canada, August 18-21, 2014. [PDF]