1/03/2006

Synchrony vs. Polychrony

What is the neural code? Some claim information is encoded in the firing rate of neurons (often simulated via rate-coded neural networks) while others point to variations in each neuron's firing rate, aka "inter-spike interval" (which is simulated via pulsed neural networks). Yet others maintain that it's some combination of the two.

One well-known possibility, popularized by authors like Steven Strogatz, is that information is encoded through synchrony of firing. Self-synchronization is a pervasive property of natural systems (from pacemaker cells to crickets to fireflies) and could be useful computationally. Synchronous firing has been implicated in visual selection, attention, and prediction. Others have gone a step further and concluded that synchrony accomplishes binding. Only a pulsed network could include information carried by synchrony, since phase information is lost in rate-coded networks.

Unfortunately, using synchrony as a computational mechanism has several problems, as pointed out by Oreilly, Busby & Soto (2001). For one, synchrony is a transient phenomenon and yet binding is persistent. Further, as the authors put it, "The problem is that if one is truly binding the features of multiple objects at the same time, but out of phase with each other, a downstream neuron will receive synchronous inputs from the features associated with both objects! How can it decode which object to respond to, when it will be strongly driven by the synchrony associated with both sets of features?" Finally, synchrony requires that neurons produce reliable firing rates both across time and across various contexts, yet this contradicts evidence from studies with alcohol, aging, and ERP.

Yet another possibility remains, however, as proposed by Izhikevich in a new paper in Neural Computation and supported by another in-press paper of theta-phase locking in the hippocampus. Izhikevich terms this "polychronization," or the generation of reproducible time-locked but not synchronous spiking patterns. Polychrony may inherit many of synchrony's problems, but it has distinct computational advantages and is more in line with neurophysiology (such as conduction delays) than synchrony. Finally, because it is the phase transition from anachronous to syn- or polychronous behavior (and back again) which allegedly accomplishes cognitive functions like attention, both rate-coded and pulsed network simulations carry sufficient information for accurate modeling.

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