Lecture 12 - Neuron Modeling

In Week 12 we focussed on two papers. The first was "Quadratic Leaky Integrate-and-Fire Neural Network tuned with an Evolution-Strategy for a Simulated 3D Biped Walking Controller" (by Wiklendt) which can be found here. This paper developed a modified leaky integrate and fire neural network model to control a simulated biped. The model effectively used a three-layer neural model to control ten degrees of freedom in the simulated biped. Although the model was effectively an integrate and fire model, the authors made two nice simplifications that cleaned up the math enough that spike times could be calculated exactly, as opposed to being simulated numerically. The network learned using a (mu plus lambda) evolution strategy (ES) which was beyond the scope of our reading; I think next year this would make for a good reading. We were able to understand that essentially the evolution model is a cellular automaton that uses a fitness strategy to assess which neurons are weighted optimally for the desired outcome.

Overall the class seemed pretty down on this article since it used a virtual neural network to control a virtual biped: since neither the controller nor the network was "real", the class wondered what the practical application of this knowledge was.

The second article we read was the highly creative "Adaptive Flight Control with Living Neuronal Networks on Microelectrode Arrays" by DeMarse and Dockendorf at the University of Florida. The paper describes a setup in which a population of neurons is grown in a specially instrumented petri dish which can record electrical activity from the cells as they fire. The neural population is used to provide pitch and roll control for a virtual aircraft in a flight simulator game. Network training is achieved by providing the network with trains of high frequency pulses that force the network connectivity to change with respect to the stimulating electrode. What was neat about this paper was that the authors used the fact that the network could simultaneously encode two different control signals (pitch and roll) and that these could be assessed simultaneously by providing a single stimulus pulse to each of the control electrodes and then measuring the speed and extent of neural activations in response. The size and speed of the response was taken to encode the controls to the flight simulator. The class was generally positive on this paper (especially in comparison to the previous one, since the control signals were coming from a real source.

We also discussed whether this technology would make sense to bring to Temple University. There is some interest in my lab about growing neurons in vitro and measuring and manipulating their connectivity.