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)...

Single-trial analyses of ensemble activity in alert animals demonstrate that cortical circuits dynamics evolve through temporal sequences of metastable states. Metastability has been studied for its potential role in sensory coding, memory, and decision-making. Yet, very little is known about the network mechanisms responsible for its genesis. It is often assumed that the onset of state sequences is triggered by an external stimulus. Here we show that state sequences can be observed also in the absence of overt sensory stimulation. Analysis of multielectrode recordings from the gustatory cortex of alert rats revealed ongoing sequences of states, where single neurons spontaneously attain several firing rates across different states. This single-neuron multistability represents a challenge to existing spiking network models, where typically each neuron is at most bistable. We present a recurrent spiking network model that accounts for both the spontaneous generation of state sequences and the multistability in single-neuron firing rates. Each state results from the activation of neural clusters with potentiated intracluster connections, with the firing rate in each cluster depending on the number of active clusters. Simulations show that the model's ensemble activity hops among the different states, reproducing the ongoing dynamics observed in the data. When probed with external stimuli, the model predicts the quenching of single-neuron multistability into bistability and the reduction of trial-by-trial variability. Both predictions were confirmed in the data. Together, these results provide a theoretical framework that captures both ongoing and evoked network dynamics in a single mechanistic model.

SUMMARY Animals are not passive spectators of the sensory world in which they live. In natural conditions they often sense objects on the bases of expectations initiated by predictive cues. Expectation profoundly modulates neural activity by altering the background state of cortical networks and modulating sensory processing. The link between these two effects is not known. Here, we studied how cue-triggered expectation of stimulus availability influences processing of sensory stimuli in the gustatory cortex (GC). We found that expected tastants were coded more rapidly than unexpected stimuli. The faster onset of sensory coding related to anticipatory priming of GC by associative auditory cues. Simultaneous recordings and pharmacological manipulations of GC and basolateral amygdala revealed the role of top-down inputs in mediating the effects of anticipatory cues. Altogether these data provide a new model for how cue-triggered expectation changes the state of sensory cortices to achieve rapid processing of natural stimuli.

Emotional learning requires the coordinated action of neural populations in limbic and cortical networks. Here, we performed simultaneous extracellular recordings from gustatory cortical (GC) and basolateral amygdalar (BLA) neural ensembles as awake, behaving rats learned to dislike the taste of saccharin [via conditioned taste aversion (CTA)]. Learning-related changes in single-neuron sensory responses were observed in both regions, but the nature of the changes was region specific. In GC, most changes were restricted to relatively late aspects of the response (starting ϳ1.0 s after stimulus administration), supporting our hypothesis that in this paradigm palatability-related information resides exclusively in later cortical responses. In contrast, and consistent with data suggesting the amygdala's primary role in judging stimulus palatability, CTA altered all components of BLA taste responses, including the earliest. Finally, learning caused dramatic increases in the functional connectivity (measured in terms of cross-correlation peak heights) between pairs of simultaneously recorded BLA and GC neurons, increases that were evident only during taste processing. Our simultaneous assays of the activity of single neurons in multiple relevant brain regions across learning suggest that the transmission of taste information through amygdala-cortical circuits plays a vital role in CTA memory formation.

The activity of ensembles of simultaneously recorded neurons can be represented as a set of points in the space of firing rates. Even though the dimension of this space is equal to the ensemble size, neural activity can be effectively localized on smaller subspaces. The dimensionality of the neural space is an important determinant of the computational tasks supported by the neural activity. Here, we investigate the dimensionality of neural ensembles from the sensory cortex of alert rats during periods of ongoing (inter-trial) and stimulus-evoked activity. We find that dimensionality grows linearly with ensemble size, and grows significantly faster during ongoing activity compared to evoked activity. We explain these results using a spiking network model based on a clustered architecture. The model captures the difference in growth rate between ongoing and evoked activity and predicts a characteristic scaling with ensemble size that could be tested in high-density multi-electrode recordings. Moreover, we present a simple theory that predicts the existence of an upper bound on dimensionality. This upper bound is inversely proportional to the amount of pair-wise correlations and, compared to a homogeneous network without clusters, it is larger by a factor equal to the number of clusters. The empirical estimation of such bounds depends on the number and duration of trials and is well predicted by the theory. Together, these results provide a framework to analyze neural dimensionality in alert animals, its behavior under stimulus presentation, and its theoretical dependence on ensemble size, number of clusters, and correlations in spiking network models.

Sensory processing is modulated by attention, which is a function of network states. Here we show that changes in such states do more than a simple gating of stimuli: they actually re-arrange cortical coding space to emphasize emotional valences. We delivered taste stimuli to rats before and after a spontaneous state change ("disengagement") that is associated with a reduction in attention and a concurrent emergence of cortical mu rhythms. The percentage of cortical neurons that responded to tastes, and the average response across neurons, remained stable with disengagement, but the particulars of the responses changed drastically. The distinctiveness of sucrose and quinine-which represent the high and low ends of the palatability spectrum-increased, the distinctiveness of the two aversive tastes (quinine and citric acid) decreased, and the distinctiveness of sucrose and NaCl, which were almost identically palatable to start with, did not change. Overall, then, the changes appeared to be palatability-specific. Two additional findings were consistent with this conclusion: rats' palatability-related behavioral responses to the tastes changed in similar ways with disengagement and disengagement-related neural changes specifically appeared late in the response, when palatability-specific information emerges in cortical responses. These data suggest that neural state changes can change the content of neural codes.

1Sensory stimuli can be recognized more rapidly when they are expected. This 2 phenomenon depends on expectation affecting the cortical processing of sensory 3 information. However, virtually nothing is known on the mechanisms responsible for 4 the effects of expectation on sensory networks. Here, we report a novel computational 5 mechanism underlying the expectation-dependent acceleration of coding observed in 6 the gustatory cortex (GC) of alert rats. We use a recurrent spiking network model with a 7 clustered architecture capturing essential features of cortical activity, including the 8 metastable activity observed in GC before and after gustatory stimulation. Relying both 9 on network theory and computer simulations, we propose that expectation exerts its 10 function by modulating the intrinsically generated dynamics preceding taste delivery. 11Our model, whose predictions are confirmed in the experimental data, demonstrates 12 how the modulation of intrinsic metastable activity can shape sensory coding and 13 mediate cognitive processes such as the expectation of relevant events. Altogether, these 14 results provide a biologically plausible theory of expectation and ascribe a new 15 functional role to intrinsically generated, metastable activity. 16 17

The amygdala processes multiple, dissociable properties of sensory stimuli. Given its central location within a dense network of reciprocally connected regions, it is reasonable to expect that basolateral amygdala (BLA) neurons should produce a rich repertoire of dynamical responses to taste stimuli. Here, we examined single BLA neuron taste responses in awake rats and report the existence of two distinct subgroups of BLA taste neurons operating simultaneously during perceptual processing. One neuron type produced long, protracted responses with dynamics that were strikingly similar to those previously observed in gustatory cortex. These responses reflect cooperation between amygdala and cortex for the purposes of processing palatability. A second type of BLA taste neuron may be part of the system often described as being responsible for reward learning: these neurons produced very brief, short-latency responses to rewarding stimuli; when the rat participated in procuring the taste by pressing a lever in response to a tone, however, those phasic taste responses vanished, phasic responses to the tone appearing instead. Our data provide strong evidence that the neural handling of taste is actually a distributed set of processes and that BLA is a nexus of these multiple processes. These results offer new insights into how amygdala imbues naturalistic sensory stimuli with value.