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Review
. 2008 Sep;100(3):1160-8.
doi: 10.1152/jn.90592.2008. Epub 2008 Jul 9.

Behavioral states, network states, and sensory response variability

Alfredo Fontanini  1 , Donald B Katz
Affiliations
Review

Behavioral states, network states, and sensory response variability

Alfredo Fontanini et al. J Neurophysiol. 2008 Sep.

Abstract

We review data demonstrating that single-neuron sensory responses change with the states of the neural networks (indexed in terms of spectral properties of local field potentials) in which those neurons are embedded. We start with broad network changes--different levels of anesthesia and sleep--and then move to studies demonstrating that the sensory response plasticity associated with attention and experience can also be conceptualized as functions of network state changes. This leads naturally to the recent data that can be interpreted to suggest that even brief experience can change sensory responses via changes in network states and that trial-to-trial variability in sensory responses is a nonrandom function of network fluctuations, as well. We suggest that the CNS may have evolved specifically to deal with stimulus variability and that the coupling with network states may be central to sensory processing.

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Figures

FIG. 1.
FIG. 1.
Macroscopic states modulate neural responsiveness. A: responses to odors in the olfactory cortex of rats are larger during periods dominated by fast electroencephalographic (EEG) waves (right) than during periods of slow waves (left). From top to bottom: EEG recorded from neocortex; a single-trial, single-neuron olfactory cortical response histogram and associated spike train (SU); simultaneously recorded record of artificial inhalation (AI); and, finally, an across-trial peristimulus time histogram (PSTH). B: average firing rate (y-axis) across time (x-axis) of a murine olfactory bulb mitral cell in response to amyl acetate (red) and citral (blue) during anesthesia and wakefulness. Note that the excitatory response to amyl acetate turns inhibitory during wakefulness. Gray shadow: period of odor presentation. Colored bars: average response amplitude across the last half of the stimulus period. Black line in the prestimulus period: average spontaneous firing frequency. C: spread of activity following magnetic stimulation of the cortex in human subjects during nonrapid eye movement (NREM) sleep (left) and wakefulness (right). Traces on the left of each panel are global mean field powers (yellow line: significance level). On the right of each panel, brain diagrams showing the location of current sources, color coded according to the latency of activation (blue = 0 ms; red = 300 ms). The yellow cross marks the location of the stimulus. Panels modified with permission from Murakami et al. (2005) (A), Rinberg et al. (2006) (B), and Massimini et al. (2005) (C).
FIG. 2.
FIG. 2.
Relationship between behavioral and network states. A, top and middle panels: time course of average relative power in the gamma band of a visual cortex local field potential (LFP, y-axis) across trials (x-axis) in which a monkey showed fast and slow reactions to a sudden visual change taking place within the receptive field of neurons simultaneously recorded from the same electrode. The 0 time point in the x-axis shows when the stimulus change occurred. The top panel shows the relative LFP power when the changing stimulus was the focus of the monkey's attention, whereas the middle panel represents the power induced when it was meant to be ignored. In each, the solid line shows LFP power for trials with fast reaction times (i.e., when attention was allocated most effectively) and the dashed line shows power for trials with slow reaction times (those when attention was less effective); the gray-shaded area shows significant differences between those two trial types. Gamma power predicts faster reactions to attended stimuli and slower reactions to unattended stimuli. Bottom: Z-score of correlation coefficients across monkeys and recording sites for LFP power vs. reaction times, showing that higher gamma power predicts faster performance (i.e., smaller reaction times). B: correlation between μ oscillations and reaction times in rat engaged in a tasting session. Top trace: session-long recording trace of a single cortical LFP, showing a general change at 71 min into the session; below, two 2-s-long traces, one from early in the session and one from late, showing in detail the emergence of μ rhythms late in the session. Bottom: the 2 bars show the amount of μ rhythms in and after the 1st h; the points overlaid on the bars show the reaction times of the first purposeful orofacial movement in response to a taste stimulus. Note the correlation with μ rhythms. A modified with permission from Womelsdorf et al. (2006); B from Fontanini and Katz (2005).
FIG. 3.
FIG. 3.
Learning and multisensory integration modulate intra- and interareal connectivity. A: changes in spontaneous functional connectivity: representative cross-correlograms between pairs of neurons within amygdala (A1) and orbitofrontal cortex (A2) during different phases of the learning and reversal of a 2-odor go/no-go discrimination task. Cross-correlations can occur for some neurons on a fast (top row of each panel) timescale and for others can be slower (bottom row). Cross-correlation peaks are modulated upward or downward depending on the different phases of learning. B: multisensory integration between ecologically meaningful stimuli modulates the functional coupling between auditory cortex and superior temporal sulcus. Top panels: cross-spectogram between electrodes positioned in each of the 2 areas following the presentation of a stimulus representing the face of a monkey performing a call (left) or a monkey face plus the sound of the corresponding call (right). Bottom, left: cross-spectra for different frequencies for 300 ms after stimulus onset, showing that gamma power in these auditory regions is significantly higher with both face and voice than with voice or face alone (or for voice matched with a dynamic disc stimulus mimicking mouth opening). Right: the average normalized cross-spectra in the gamma band (55–95 Hz). A adapted from Schoenbaum et al. (2000); B adapted from Ghazanfar et al. (2008).
FIG. 4.
FIG. 4.
Trial-to-trial variability in neuronal responses. A, top: raster plot of a neuron in monkey's middle temporal area (MT) responding to multiple presentations of a moving Gabor stimulus. Each tick mark is an action potential. The middle panels show in detail the spikes occurring in the dotted boxes. The bottom panels are representations of the same trials as in the middle panels, but reordered according to a clustering algorithm. Note that trials are now grouped into subtypes according to similarity of responses. B: population responses to tastes in the rat gustatory cortex go through stimulus-specific sequences of coherent network states. Top: raster plots showing the responses of 4 gustatory cortical neurons to multiple presentations of citric acid (9 trials). y-axis: trials; x-axis: time. Bottom: 4 representative citric acid trials showing the taste- and trial-specific coherent activity of the ensemble of simultaneously recorded neurons (the neurons represented in the top are the first 4 starting from the bottom). The overlaid shadowing represents network states characterized in terms of specific patterns of network activity. A adapted from Fellous et al. (2004); B adapted from Jones et al. (2007).
FIG. 5.
FIG. 5.
Dependence of sensory responses on anatomical and functional connectivity. A: schematic representing evoked activity as depending only on anatomical connectivity: the stimulus activates the cells directly connected to the periphery that, in turn, activate those to which they are more strongly connected. In this case repeated presentations of sensory stimuli always evoke the same responses. B: cartoon summarizing the view emerging from this review: the state of the network varies in parallel with the behavioral state of the animal. Functional connectivity and background activity of the neurons composing the network determine the shape of the evoked response.
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