Natural stimuli evoke dynamic sequences of states in sensory cortical ensembles

Abstract

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)...

Sensory Input Drives Multiple Intracellular Information Streams in Somatosensory Cortex

“…Our results agree with the idea that "spontaneous" collective activity can be modulated by sensory stimuli (Anderson et al, 2000;Kenet et al, 2003;Fiser et al, 2004;Loewenstein et al, 2005;MacLean et al, 2005;Jones et al, 2007;Gao et al, 2009;Luczak et al, 2009). In the barrel cortex, the onset of sufficiently strong stimuli can evoke transitions between down and up states (Hasenstaub et al, 2007;Reig and Sanchez-Vives, 2007).…”

Synthesizing cognition in neuromorphic electronic systems

“…Models of distributed state encodings were shown to be compatible with experimental observations of perceptual dominance (53), position encoding in grid cells (54), context and task rule encodings (31), and taste processing (55). Extensions to the SSM of this sort can enable the neuromorphic implementation of more versatile hidden Markov models (55), which can be used to solve a wide variety of machine-learning tasks (34).…”

Quantitative examination of stimulus-response relations in cortical networks in vitro

“…More physiological sensory responses induced by, e.g., foot tapping, whisker deflection, or air puffs unfold comparable dynamics in various brain regions of awake and anesthetized animals (Cody et al 1981;Fanselow and Nicolelis 1999;Rowland and Jaeger 2005). Across stimulation trials, however, the variability of timing and duration of the response, as well as the number and distribution of neurons involved, is typically high (Azouz and Gray 1999;Jones et al 2007). On short time scales, temporal nonstationarities by modulation of activity and excitability tend to prevail and modify response amplitude, latency, and spatial spread (Kisley and Gerstein 1999;Petersen et al 2003).…”