Abstract
Recent advances in techniques for chronic recording from multiple extracellular microelectrodes allow simultaneous observation of firings of substantial populations of neurons. We describe a new conceptual representation of cooperative behavior within the observed neuronal population. This representation leads to a new technique for detecting and studying functional neuronal assemblies that are characterized by temporally related firing patterns. The representation may be applied to both dynamic and long-term aspects of cooperativity. The basic idea is to map activity of neurons into motions of particles in a multidimensional Euclidean space. Each neuron is represented by a point particle located in this space. In the simplest version of the mapping, each nerve impulse results in an increment in a <charge> associated with that particle; between firings the charges decay. The force exerted by any such particle on any other is, by analogy with some physical forces, proportional to the product of their charges and may depend on the Euclidean distance separating them. The force on a particle directly affects its velocity rather than its acceleration, as with actual particles in a viscous medium. These forces result in aggregation of those particles that correspond to neurons tending to fire together; separate clusters represent independent cooperative groups. Modification of the charges and forces permits inclusion of inhibitory interactions. Identification, measurement, and display of the resulting clusters can be performed with any of a number of algorithms. We illustrate the application of this approach to populations of computer-simulated neurons having both direct and indirect excitatory coupling.
