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NEUROSCIENCE:
Temporal Coding in Neural Populations?

The waking brain continually performs a remarkable range of neural computations, processing its interactions with the environment as well as performing numerous internal operations. Consider the immediate example of your ability to quickly comprehend the spatial patterns on this page. Scanning successive words and decoding their meaning is a common miracle performed effortlessly by cerebral networks. If you pause to contemplate how neurons could perform these feats, your thoughts are also generated by a complex calculus of neural impulses. Realizing that your entire mental existence depends on neural operations, you can appreciate why neuroscientists would like to understand the underlying mechanisms.

To investigate how neurons process information, researchers typically relate the firing rate of single neurons (the rate at which neurons generate all-or-none action potentials) to a behavioral parameter, such as a sensory stimulus or motor response (1-3). Neural activity is quite variable, even with precise repetitions of the behavioral conditions, so mean firing rates have been obtained by averaging neural responses over many trials, thereby enhancing the rate-coding "signal." The success of this approach has led to the belief that firing rates of neurons are the necessary and sufficient mechanism underlying information processing, and furthermore, that firing rates not only code peripheral sensory and motor events, but also mediate central cognitive processes.

But in fact, large populations of neurons are involved simultaneously in any behavior (4), and the temporal structure of spike activity in neural assemblies provides additional dimensions for coding information. Increasing numbers of researchers are now recording from many neurons simultaneously (3, 4). Indeed, the techniques for multiunit recording have outstripped the development of algorithms for analyzing the dynamics of neural populations. Some investigators have found ways to derive better fits between multiunit activity and behavioral parameters (3), but most population-coding schemes still employ functions of firing rates. The possibility that information processing may involve the instantaneous relations between members of neural assemblies has been proposed (5, 6), although such ideas are in dire need of compelling experimental support. In this regard, the report on page 1950 of this issue by Riehle et al. (7) provides new tools and supporting evidence. Using a simple algorithm for detecting real-time interactions in neural populations, the authors have found that neural activity became transiently synchronized during a purely internal cognitive process--when a monkey expected the presentation of a signal, in the absence of any sensory or motor events.

These findings relate to a current controversy concerning neural coding: whether neurons process information only by rate coding or whether the brain also exploits "temporal coding," in which the relative timing of spikes can carry information (1, 7-10, 14). Temporal coding can employ the temporal structure of the firing pattern of single neurons (10) or, as considered here, the precise relation between firings in multiple neurons of a functional assembly (5, 9, 12, 14). If the relative timing of spikes in a neural population can represent information, as it surely does in the auditory system, the "bandwidth" for neural processing could be significantly expanded. As yet, there is no accepted standard for how this increased channel capacity would be used for high-definition neural computation. But a common feature of most temporal codes is the synchronization of spikes in neurons. The report by Riehle et al. shows that motor cortex neurons can become significantly more synchronized in relation to expected events, independently of any changes in firing rates.

The figure illustrates the different types of neural interactions. The two representative neurons (A and B) can fire in relation to peripheral events, like a sensory stimulus (S) or movement (M), and can also participate in internal cognitive events, symbolized by the central assembly (C). The proposed signature of the cells' participation in assembly activity is increased synchronization, produced by common input from the assembly. Synchronous activity provides an effective propagating mechanism within the assembly, and can also change the efficacy of synaptic connections between the synchronized cells (9). Riehle et al. detected the increased synchrony by comparing the number of observed coincident spikes (red bars) with the number expected by chance, based on a running estimate of rates over a longer interval (blue bar). This running comparison can identify exceptional synchronizations whenever they occur. Interestingly, these occurred at times when the monkey expected a possible cue, as well as when he made a motor response. Thus, synchronization and rate coding can occur separately or together (8, 9, 12, 14, 15).

Several other approaches can detect coherent activity in neural populations at millisecond resolution. Standard cross-correlations between simultaneously recorded neurons commonly reveal synchronous activity, and time-dependent cross-correlation measures (11) have revealed that synchronous firing can be rapidly modulated with behavior, even without changes in firing rates (12). Other studies have found evidence for precisely timed patterns occurring among neural groups in a behavioral situation (5). Simulations have shown that such synchrony can be preserved in chains of neurons with realistic synaptic connections (13). Another form of synchronized activity in neural populations is the widespread periodic oscillations seen in visual cortex neurons, which has been suggested to subserve a binding function (9), a suggestion potentially applicable to coordination of motor responses (14, 15). Another approach uses the "gravity" method to identify groups of neurons that tend to fire in synchrony: If n neurons are located in n-dimensional space, and their spikes are endowed with a transient "charge," those cells that fire synchronously tend to be attracted and form identifiable clusters (6).

Although all of these algorithms can detect the existence of precise temporal structure in neural activity, this does not yet establish their function as a temporal code. What is needed first is some demonstration that synchronization occurs reliably under particular behavioral conditions. The accumulating evidence is suggestive (5-9, 12, 14) but still leaves the exact function unproven. Establishing the functional mechanism may not be a matter of finding tighter correlations with behavioral events; for example, holographic mechanisms code distributed information in terms of phase relations rather than literal representations (2). Skeptics can still argue that the temporal events revealed by these methods are epiphenomena or products of the statistical models, and that anything temporal coding can do, population rate coding can do as well (16). Moreover, there are open questions about how temporal codes are established and how they interact with rate coding (8, 15). These issues can now be investigated with the tools at hand: Multiunit recordings can be analyzed with these algorithms, and the detected events can be related to behavior. Neural network simulations can also help to demonstrate how the putative coding mechanisms could actually work (2, 5, 13). This combination of approaches should crack the temporal code one way or the other.

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Summary of this Article E-Letters: Submit a response to this article   Download to Citation Manager Alert me when: new articles cite this article   Search for similar articles in:   Science Online   ISI Web of Science   PubMed Search Medline for articles by: Fetz, E. E. Search for citing articles in:   ISI Web of Science (33)   HighWire Press Journals   This article appears in the following Subject Collections: Neuroscience

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