Communicating with Living Neuronal Networks on Microelectrode Arrays for Computation and Control

Robert L. Ortman, Christopher J. Rozell, Ganesh Kumar Venayagamoorthy and Steve M. Potter

Effective input patterns must evoke computationally useful responses in cultures of rat cortical neurons on MEAs. Candidate electrical stimulation patterns are evaluated for evoked-response separability and reliability via a support vector machine (SVM)-based method. Genetic algorithm is used to construct subsets of highly separable patterns.

Computation Using Latency Dynamics in Living and Artificial Neural Networks

Riley T. Zeller-Townson, Ganesh Kumar Venaygamoorthy and Steve M. Potter

Spiking Neural Networks use the precise timing of action potentials to convey meaning. The conduction delays between neurons are one set of parameters that can be tuned to improve network performance on computational tasks, however no biologically inspired delay learning rules have been adopted by the artificial neural network community. This work shows the computational properties of delay update rules that are based on how delay change in living neural networks, as well as how the actual biological data can be used to improve performance for a prediction task.

Computation with Action Potential Delay Dynamics

Riley T. Zeller-Townson, Jonathan P. Newman, Ganesh K. Venayagamoorthy and Steve M. Potter

Computation with Action Potential Delay Dynamics

Biologically Inspired Artificial Neural Networks, such as Spiking Neural Networks (SNNs), promise to provide significant advances over classic Artificial Neural Networks (ANNs) by performing computations in ways similar to the living brain. SNNs use discrete action potentials, which require a finite amount of time to travel between neurons. Most SNNs assume this axonal conduction delay to be constant, in spite of growing biological evidence that this conduction delay shows both long term and short term plasticity. We are working to explore the computational implications of these dynamics.

Reservoir-Computing-Based, Biologically-Inspired Artificial Neural Network (BIANN) for Online Modeling of a Single Machine Infinite Bus (SMIB) System

Jing Dai, Ronald G Harley, Ganesh Kumar Venayagamoorthy and Steve M. Potter

BIANN uses biologically plausible spiking neuron models. It bridges the gap between oversimplified ANNs and living neural networks. Effective encoding, decoding and training mechanisms for BIANN still need to be developed. A reservoir computing based training approach is proposed for the BIANN to serve as a novel modeling and control tool for practical applications. The BIANN is able to provide accurate one and five steps-ahead predictions of the rotor speed and terminal voltage of a generator in a SMIB, for online monitoring purposes.