A little over a month ago we took a peak at a 2004 paper from Dr. Gerald Edelman’s laboratory that updated Dr. Edelman’s group selection theory by including axon conductance delays and spike timing dependent plasticity (STDP) in a massive computer model of the cerebral cortex containing 100,000 neurons and 8.5 million synaptic connections. The first author of that paper, Dr. Eugene Izhikevich, published a new paper in 2006 (“Polychronization: Computation with Spikes” published February 2006 in Neural Computation) that focused on the remarkable properties that emerged from the addition of axon conductance delays and STDP in a highly simplified model of cerebral cortex containing 1,000 neurons. This is probably the key paper describing the author’s results and ideas surrounding what he calls “polychronization” (poly means many and chronous means time) or the spontaneous formation of neuronal groups defined as “small collectives of neurons having strong connections with matching conduction delays and exhibiting time-locked but not necessarily synchronous spiking activity” (they may fire at many different times).

Note: An appendix in the paper provides a MATLAB version of the model. In addition, MATLAB and C++ versions of the “Polychronization: Computation With Spikes (Izhikevich 2005)” model are available at SenseLab’s ModelDB.

The sparse network (0.1 probability of connection between any two neurons) of 1000 randomly connected spiking neurons included axon conductance delays and STDP. This relatively simple network composed of 80% excitatory and 20% inhibitory neurons displayed dynamics similar to those seen in the mammalian cerebral cortex including 4 Hertz delta oscillations, 40 Hertz gamma oscillations, and balanced excitation and inhibition. An important finding reported in this paper is that the number of coexisting polychronous groups may far exceed the number of neurons in the network. In other words, these highly dynamic and spontaneously formed groups have the potential to carry a huge amount of information.


Other related blog posts:

Neuronal Group Selection and Spike Timing Dependent Plasticity

Dynamical Systems and Silicon Based Hybrid Spiking Neurons