The Auditory P50 Gating in Mild Cognitive Impairment: A Case-Control Study

Objective: Auditory P50 gating changed might be a neurophysiological biomarker of the diagnosis of Mild Cognitive Impairment (MCI). We aimed to determine the impact of MCI in auditory P50 gating. Methods: All recruited participants completed structured questionnaires and finished auditory P50 gating measure. Results: A total of 20 MCI patients and 17 controls had been recruited. MCI patients had a significant higher reduction of P50 gating at Fz site, when compared to controls (1.21 ± .68 vs .66 ± .37, P = .00). Zero point five was the best cut off point to distinguish MCI and control of auditory P50 gating S2/S1 at Fz site. The P50 average amplitude at Pz site in MCI patients was significantly higher than controls (2.62 ± 1.20 vs 1.70 ± .74, P = .01). Conclusion: MCI patients might have impaired the ability of inhibiting the repeated stimulus.

Visual stimulus-specific habituation of innate defensive behaviour in mice

ABSTRACTInnate defensive responses such as freezing or escape are essential for animal survival. Mice show defensive behaviour to stimuli sweeping overhead, like a bird cruising the sky. Here, we tested this in young male mice and found that mice reduced their defensive freezing after sessions with a stimulus passing overhead repeatedly. This habituation is stimulus specific, as mice freeze again to a novel shape. Habituation occurs regardless of the visual field location of the repeated stimulus. The mice generalized over a range of sizes and shapes, but distinguished objects when they differed in both size and shape. Innate visual defensive responses are thus strongly influenced by previous experience as mice learn to ignore specific stimuli.

Stimulus-specific plasticity of macaque V1 spike rates and gamma

When a visual stimulus is repeated, average neuronal responses typically decrease, yet they might maintain or even increase their impact through increased synchronization. Previous work has found that many repetitions of a grating lead to increasing gamma-band synchronization. Here we show in awake macaque area V1 that both, repetition-related reductions in firing rate and increases in gamma are specific to the repeated stimulus. These effects showed some persistence on the timescale of minutes. Further, gamma increases were specific to the presented stimulus location. Importantly, repetition effects on gamma and on firing rates generalized to natural images. These findings suggest that gamma-band synchronization subserves the adaptive processing of repeated stimulus encounters, both for generating efficient stimulus responses and possibly for memory formation.

Identifying signal and noise structure in neural population activity with Gaussian process factor models

Neural datasets often contain measurements of neural activity across multiple trials of a repeated stimulus or behavior. An important problem in the analysis of such datasets is to characterize systematic aspects of neural activity that carry information about the repeated stimulus or behavior of interest, which can be considered “signal”, and to separate them from the trial-to-trial fluctuations in activity that are not time-locked to the stimulus, which for purposes of such analyses can be considered “noise”. Gaussian Process factor models provide a powerful tool for identifying shared structure in high-dimensional neural data. However, they have not yet been adapted to the problem of characterizing signal and noise in multi-trial datasets. Here we address this shortcoming by proposing “signal-noise” Poisson-spiking Gaussian Process Factor Analysis (SNP-GPFA), a flexible latent variable model that resolves signal and noise latent structure in neural population spiking activity. To learn the parameters of our model, we introduce a Fourier-domain black box variational inference method that quickly identifies smooth latent structure. The resulting model reliably uncovers latent signal and trial-to-trial noise-related fluctuations in large-scale recordings. We use this model to show that predominantly, noise fluctuations perturb neural activity within a subspace orthogonal to signal activity, suggesting that trial-by-trial noise does not interfere with signal representations. Finally, we extend the model to capture statistical dependencies across brain regions in multi-region data. We show that in mouse visual cortex, models with shared noise across brain regions out-perform models with independent per-region noise.

A Simple Model for Low Variability in Neural Spike Trains

Neural noise sets a limit to information transmission in sensory systems. In several areas, the spiking response (to a repeated stimulus) has shown a higher degree of regularity than predicted by a Poisson process. However, a simple model to explain this low variability is still lacking. Here we introduce a new model, with a correction to Poisson statistics, that can accurately predict the regularity of neural spike trains in response to a repeated stimulus. The model has only two parameters but can reproduce the observed variability in retinal recordings in various conditions. We show analytically why this approximation can work. In a model of the spike-emitting process where a refractory period is assumed, we derive that our simple correction can well approximate the spike train statistics over a broad range of firing rates. Our model can be easily plugged to stimulus processing models, like a linear-nonlinear model or its generalizations, to replace the Poisson spike train hypothesis that is commonly assumed. It estimates the amount of information transmitted much more accurately than Poisson models in retinal recordings. Thanks to its simplicity, this model has the potential to explain low variability in other areas.

A simple model for low variability in neural spike trains

Neural noise sets a limit to information transmission in sensory systems. In several areas, the spiking response (to a repeated stimulus) has shown a higher degree of regularity than predicted by a Poisson process. However, a simple model to explain this low variability is still lacking. Here we introduce a new model, with a correction to Poisson statistics, which can accurately predict the regularity of neural spike trains in response to a repeated stimulus. The model has only two parameters, but can reproduce the observed variability in retinal recordings in various conditions. We show analytically why this approximation can work. In a model of the spike emitting process where a refractory period is assumed, we derive that our simple correction can well approximate the spike train statistics over a broad range of firing rates. Our model can be easily plugged to stimulus processing models, like Linear-nonlinear model or its generalizations, to replace the Poisson spike train hypothesis that is commonly assumed. It estimates the amount of information transmitted much more accurately than Poisson models in retinal recordings. Thanks to its simplicity this model has the potential to explain low variability in other areas.

Visual memories are stored along a compressed timeline

In continuous recognition the recency effect manifests as a decrease in accuracy and a sublinear increase in response time (RT) with the lag of a repeated stimulus. The recency effect could result from the gradual weakening of mnemonic traces. Alternatively, the recency effect could result from a search through a compressed timeline of recent experience. These two hypotheses make very different predictions about the shape of response time distributions. Using highly-memorable pictures to mitigate changes in accuracy enabled a detailed examination of the effect of recency on retrieval dynamics. The recency at which pictures were repeated ranged over two orders of magnitude across three experiments. Analysis of the RT distributions showed that the time at which memories became accessible changed with the recency of the probe, as predicted by a serial search model suggesting that visual memories can be accessed by sequentially scanning along a compressed representation of the past.

Novelty is not enough: laser-evoked potentials are determined by stimulus saliency, not absolute novelty

Event-related potentials (ERPs) elicited by transient nociceptive stimuli in humans are largely sensitive to bottom-up novelty induced, for example, by changes in stimulus attributes (e.g., modality or spatial location) within a stream of repeated stimuli. Here we aimed 1) to test the contribution of a selective change of the intensity of a repeated stimulus in determining the magnitude of nociceptive ERPs, and 2) to dissect the effect of this change of intensity in terms of “novelty” and “saliency” (an increase of stimulus intensity is more salient than a decrease of stimulus intensity). Nociceptive ERPs were elicited by trains of three consecutive laser stimuli (S1-S2-S3) delivered to the hand dorsum at a constant 1-s interstimulus interval. Three, equally spaced intensities were used: low (L), medium (M), and high (H). While the intensities of S1 and S2 were always identical (L, M, or H), the intensity of S3 was either identical (e.g., HHH) or different (e.g., MMH) from the intensity of S1 and S2. Introducing a selective change in stimulus intensity elicited significantly larger N1 and N2 waves of the S3-ERP but only when the change consisted in an increase in stimulus intensity. This observation indicates that nociceptive ERPs do not simply reflect processes involved in the detection of novelty but, instead, are mainly determined by stimulus saliency.