Abstract for Maida, Rowland, & Gunay

We are conducting simulation studies intended to acquire insights into the factors that enable and control coordinated, synchronized firing in artificial neural networks.  This talk explains the approach, justifies the approach by citing the empirical literature, and presents our pilot results.

We begin by reviewing the assumptions of the now classical, connectionist-PDP neural networks. For concreteness, we present one example of McClelland
and colleagues showing how a PDP network can model attentional focus and interference in the Stroop color-naming task.

With this concrete example, we point out aspects of the neural code that a connectionist neuron does - and does not - represent.  In particular, a connectionist neuron does not model the timing of a neural spike.  We cite empirical evidence that indicates that timing of neural spikes are important for cognitive models.  Spike timing seems to be needed to explain processing speed in the brain (cf., Thorpe, Sereno) and to account for data involving synchronized firing as the neural correlate
of attention.

We describe a minimal abstract neuron that does code for timing of neural spikes.  We then show simulation results that confirm the theoretical work of Hopfield, Gerstner, and colleagues.  These simulations show how synchronized firing can emerge from networks of unsynchronized neurons.