Although temporal coding is a frequent topic of neurophysiology research, trial-to-trial variability in temporal codes is typically dismissed as noise and thought to play no role in sensory function. Here, we show that much of this supposed ''noise'' faithfully reflects stimulus-related processes carried out in coherent neural networks. Cortical neurons responded to sensory stimuli by progressing through sequences of states, identifiable only in examinations of simultaneously recorded ensembles. The specific times at which ensembles transitioned from state to state varied from trial to trial, but the state sequences were reliable and stimulusspecific. Thus, the characterization of ensemble responses in terms of state sequences captured facets of sensory processing that are missing from, and obscured in, other analyses. This work provides evidence that sensory neurons act as parts of a systems-level dynamic process, the nature of which can best be appreciated through observation of distributed ensembles.gustatory ͉ hidden Markov model T he time courses of sensory neural responses are rich with structure. Taking time into consideration increases the amount of information that can be extracted from neural codes (1-5) and changes the nature of that information (6-8). Such temporal complexity is the natural result of interactions among neural populations (9-11), a concept recently illustrated in studies of olfactory antennal lobe responses in insects (12)(13)(14).The behavior of mammalian sensory systems has proven more difficult to characterize, due in part to the relative complexity of these networks and of the behaviors and neural activity that they subtend. Feedback and convergence found in mammalian brains are extensive and diffuse (15), a fact that contributes to high trial-to-trial variability of mammalian cortical sensory responses (16). This variability is usually dismissed as noise, a decision formalized by the use of across-trial averages such as peristimulus time histograms (PSTHs) (8) and compilations of sequentially recorded neurons (13) to characterize temporal codes.If the variability in neural responses is not noise, however [if, for instance, it reflects network processes evolving at different speeds from trial to trial (17, 18)], then trial-averaging techniques will obscure features of the underlying neural processes. Recent evidence indirectly suggests that this possibility may be the case: repeating multineuronal temporal patterns that are not reflected in PSTHs follow application of sensory stimuli (19, 20) and precede initiation of motor behaviors (21-23), although the search algorithms used to identify such patterns are controversial (24, 25); furthermore, the speed of perceptual identification itself varies from trial to trial (26, 27) in a manner linked to the dynamics of network activity (27)(28)(29)(30).Here, we provide direct evidence that trial-to-trial variability is a reliable, information-rich part of ensemble sensory processing in awake rats, by using hidden Markov models [HMM (31)...

The ability of rats to use their whiskers for fine tactile discrimination rivals that of humans using their fingertips. Rats perform discriminations rapidly and accurately while palpating the environment with their whiskers. This suggests that whisker deflections produce a robust and reliable neural code. Whisker primary afferents respond with highly reproducible temporal spike patterns to transient stimuli. Here we show that, with the use of a linear kernel, any of these reproducible response trains recorded from an individual neuron can reliably predict complex whisker deflections. These predictions are significantly improved by integrating responses from neurons with opposite angular preferences.

. The ability of rats using their whiskers to perform fine tactile discrimination rivals that of humans using their fingertips. Rats must perform these discriminations rapidly and accurately while palpating the environment with their whiskers. This suggests that whisker-derived inputs produce a robust and reliable code, capable of capturing complex, high-frequency information. The first neural representation of whisker-derived stimulus information is in primary afferent neurons of the trigeminal ganglion. Here we demonstrate that there is a continuum of directiondependent response profiles in trigeminal neurons and provide the first quantitative analysis of the encoding of complex stimuli by these neurons. We show that all classes of trigeminal ganglion neurons respond with highly reproducible temporal spike patterns to transient stimuli. Such a robust coding mechanism may allow rapid perception of complex tactile features.

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