People with sensorineural hearing loss have difficulty understanding speech, especially when background sounds are present. A reduction in the ability to resolve the frequency components of complex sounds is one factor contributing to this difficulty. Here, we show that a reduced ability to process the temporal fine structure of sounds plays an important role. Speech sounds were processed by filtering them into 16 adjacent frequency bands. The signal in each band was processed by using the Hilbert transform so as to preserve either the envelope (E, the relatively slow variations in amplitude over time) or the temporal fine structure (TFS, the rapid oscillations with rate close to the center frequency of the band). The band signals were then recombined and the stimuli were presented to subjects for identification. After training, normal-hearing subjects scored perfectly with unprocessed speech, and were approximately 90% correct with E and TFS speech. Both young and elderly subjects with moderate flat hearing loss performed almost as well as normal with unprocessed and E speech but performed very poorly with TFS speech, indicating a greatly reduced ability to use TFS. For the younger hearing-impaired group, TFS scores were highly correlated with the ability to take advantage of temporal dips in a background noise when identifying unprocessed speech. The results suggest that the ability to use TFS may be critical for "listening in the background dips." TFS stimuli may be useful in evaluating impaired hearing and in guiding the design of hearing aids and cochlear implants.

To manage populations, whether of threatened, exploited, or pest species, or even of parasites or pathogens in hosts, we must be able to predict their population dynamics, and to do this, we need to understand the underlying processes. An important recent focus is the way that deterministic density-dependent processes interact with random influences ("noise") to create novel patterns in the dynamics. Such noise typically has two sources. First, environmental noise is driven by changes in extrinsic processes (such as climate and resource availability). Second, demographic noise is the random way individuals progress through their life history and arises through the "discreteness of individuality": an organism can be male or female, dead or alive, but the "vital rate" (sex ratio, survival, fecundity) may be noninteger. In large populations, demographic stochasticity can be ignored because it is averaged across a large number of individuals. So, at any point in time or space, the population will typically be very close to the average, but in small populations, the deviation between the population average and the realized value at any particular time may be considerable. The "strength" of demographic noise is inversely related to the square root of population size, and a general question that is asked by Reuman et al. (1) in this issue of PNAS is "how does demographic noise of different strengths affect dynamics?"

This study concerns the properties of neurons carrying signals for colour vision in primates. We investigated the variability of responses of individual parvocellular lateral geniculate neurons of dichromatic and trichromatic marmosets to drifting sinusoidal luminance and chromatic gratings. Response variability was quantified by the cycle-to-cycle variation in Fourier components of the response. Averaged across the population, the variability at low contrasts was greater than predicted by a Poisson process, and at high contrasts the responses were approximately 40% more variable than responses at low contrasts. The contrast- dependent increase in variability was nevertheless below that expected from the increase in firing rate. Variability falls below the Poisson prediction at high contrast, and intrinsic variability of the spike train decreases as contrast increases. Thus, while deeply modulated responses in parvocellular cells have a larger absolute variability than weakly modulated ones, they have a more favourable signal: noise ratio than predicted by a Poisson process. Similar results were obtained from a small sample of magnocellular and koniocellular ("blue-on") neurons. For parvocellular neurons with pronounced colour opponency, chromatic responses were, on average, less variable (10-15%, p<0.01) than luminance responses of equal magnitude. Conversely, non-opponent parvocellular neurons showed the opposite tendency. This is consistent with a supra-additive noise source prior to combination of cone signals. In sum, though variability of parvocellular neurons is largely independent of the way in which they combine cone signals, the noise characteristics of retinal circuitry may augment specialization of parvocellular neurons to signal luminance or chromatic contrast.

Human visual speed perception is qualitatively consistent with a Bayesian observer that optimally combines noisy measurements with a prior preference for lower speeds. Quantitative validation of this model, however, is difficult because the precise noise characteristics and prior expectations are unknown. Here, we present an augmented observer model that accounts for the variability of subjective responses in a speed discrimination task. This allowed us to infer the shape of the prior probability as well as the internal noise characteristics directly from psychophysical data. For all subjects, we found that the fitted model provides an accurate description of the data across a wide range of stimulus parameters. The inferred prior distribution shows significantly heavier tails than a Gaussian, and the amplitude of the internal noise is approximately proportional to stimulus speed and depends inversely on stimulus contrast. The framework is general and should prove applicable to other experiments and perceptual modalities.

We show that times of spikes can be very precise. In the cerebral cortex, where each nerve cell is affected by thousands of others, it is the common belief that the exact time of a spike is random up to an averaged firing rate over tens of milliseconds. In a brain slice, precise time relations of several neurons have been observed. It remained unclear whether this phenomenon can also be observed in brains of behaving animals. Here we show, in behaving monkeys, that time intervals between spikes, measured in correspondence to a specific behavior, may be controlled to within the milliseconds range.

In vivo studies suggest that precise firing of neurons is important for correct sensory representation. Principal neocortical neurons fire imprecisely when repeatedly activated by fixed sensory stimuli or current depolarizations. Here we show that in contrast to pyramidal neurons, firing in neocortical GABAergic fast-spiking (FS) interneurons is quite precise. FS interneurons are self-innervated by powerful GABAergic autaptic connections reliably activated after each spike, suggesting that autapses strongly regulate FS-cell spike timing. Indeed, blockade of autaptic transmission degraded temporal precision in multiple ways. Under these conditions, realistic dynamic-clamp hyperpolarizing autapses restored precision of spike timing, even in the presence of synaptic noise. Furthermore, firing precision was increased in pyramidal neurons by artificial GABAergic autaptic conductances, suggesting that tightly coupled synaptic feedback inhibition regulates spike timing in principal cells. Thus, well-timed inhibition, whether autaptic or synaptic, facilitates precise spike timing and promotes synchronized cortical network oscillations relevant to several behaviors.

Although spontaneous activity occurs throughout the neocortex, its relation to the activity produced by external or sensory inputs remains unclear. To address this, we used calcium imaging of mouse thalamocortical slices to reconstruct, with single-cell resolution, the spatiotemporal dynamics of activity of layer 4 in the presence or absence of thalamic stimulation. We found spontaneous neuronal coactivations corresponded to intracellular UP states. Thalamic stimulation of sufficient frequency (>10 Hz) triggered cortical activity, and UP states, indistinguishable from those arising spontaneously. Moreover, neurons were activated in identical and precise spatiotemporal patterns in thalamically triggered and spontaneous events. The similarities between cortical activations indicate that intracortical connectivity plays the dominant role in the cortical response to thalamic inputs. Our data demonstrate that precise spatiotemporal activity patterns can be triggered by thalamic inputs and indicate that the thalamus serves to release intrinsic cortical dynamics.

Characterizing the responsiveness of thalamic neurons is crucial to understanding the flow of sensory information. Typically, thalamocortical neurons possess two distinct firing modes. At depolarized membrane potentials, thalamic cells fire single action potentials and faithfully relay synaptic inputs to the cortex. At hyperpolarized potentials, the activation of T-type calcium channels promotes burst firing, and the transfer is less accurate. Our results suggest that this duality no longer holds if synaptic background activity is taken into account. By injecting stochastic conductances into guinea-pig thalamocortical neurons in slices, we show that the transfer function of these neurons is strongly influenced by conductance noise. The combination of synaptic noise with intrinsic properties gives a global responsiveness that is more linear, mixing single-spike and burst responses at all membrane potentials. Because in thalamic neurons, background synaptic input originates mainly from cortex, these results support a determinant role of corticothalamic feedback during sensory information processing.

Sensory neural systems use spatiotemporal coding mechanisms to represent stimuli. These time-varying response patterns sometimes outlast the stimulus. Can the temporal structure of a stimulus interfere with, or even disrupt, the spatiotemporal structure of the neural representation? We investigated this potential confound in the locust olfactory system. When odors were presented in trains of nearly overlapping pulses, responses of first-order interneurons (projection neurons) changed reliably, and often markedly, with pulse position as responses to one pulse interfered with subsequent responses. However, using the responses of an ensemble of projection neurons, we could accurately classify the odorants as well as characterize the temporal properties of the stimulus. Further, we found that second-order follower neurons showed firing patterns consistent with the information in the projection-neuron ensemble. Thus, ensemble-based spatiotemporal coding could disambiguate complex and potentially confounding temporally structured sensory stimuli and thereby provide an invariant response to a stimulus presented in various ways.

Hypotheses of sensory coding range from the notion of nonlinear "feature detectors" to linear rate coding strategies. Here, we report that auditory neurons exhibit a novel trade-off in the relationship between sound selectivity and the information that can be communicated to a postsynaptic cell. Recordings from the cat inferior colliculus show that neurons with the lowest spike rates reliably signal the occurrence of stereotyped stimulus features, whereas those with high response rates exhibit lower selectivity. The highest information conveyed by individual action potentials comes from neurons with low spike rate and high selectivity. Surprisingly, spike information is inversely related to spike rates, following a trend similar to that of feature selectivity. Information per time interval, however, was proportional to measured spike rates. A neuronal model based on the spike threshold of the synaptic drive accurately accounts for this trade-off: higher thresholds enhance the spiking fidelity at the expense of limiting the total communicated information. Such a constraint on the specificity and throughput creates a continuum in the neural code with two extreme forms of information transfer that likely serve complementary roles in the representation of the auditory environment.