Neural Noise Reduction Research Articles

INVOLVEMENT OF LATERAL SEPTUM INTO THE CENTAL NERVOUS SYSTEM REACTION ON ACUTE ALCOHOL INTOXICATION

The objective of this study - to determine how lateral septum (LS) is involved into thebrain reaction on acute alcohol intoxication (AAI) based on the changes in its electricalactivity and reaction of local nitrergic system.Material and methods. Wistar rats (n=8 for electrophysiology and n=6 for histochemistryof nitrergic system) were used for the study. Changes in the spectral power of the localfield potential, neural noise dynamics, as well as changes in the number of NOS-positivecells in LS for the model of AAI by intraperitoneal injections of ethanol solution (2 g/kg)were studied.Results. An increase in power for delta, theta, beta-2, gamma frequency bands, andultrafast oscillations as well as suppression of beta-1 rhythms was observed as aresponse of LS to AAI. Furthermore, we have detected an increase in neural noise forall frequency range. Inspection of specific frequency bands showed a decrease in neuralnoise for delta, theta, and beta bands as compared with spontaneous levels. In additionto that, ethanol-induced reduction in neural noise was measured for gamma frequencyrange and ultrafast oscillations. AAI resulted in activation of a population of medium/small-sized LS neurons the number of which increased from 3.15 ± 0.31 cells per sliceto 7.06±0.67 cells per slice (P = 0.0122).Conclusion. Our findings on the ethanol-induced changes in oscillatory activity andneural noise dynamics in LS coupled with activation of the nitrergic system allowed us toincorporate LS in the neural circuit engaged in the brain reaction to AAI.

Precursor processes of human self-initiated action

A gradual buildup of electrical potential over motor areas precedes self-initiated movements. Recently, such “readiness potentials” (RPs) were attributed to stochastic fluctuations in neural activity. We developed a new experimental paradigm that operationalized self-initiated actions as endogenous ‘skip’ responses while waiting for target stimuli in a perceptual decision task. We compared these to a block of trials where participants could not choose when to skip, but were instead instructed to skip. Frequency and timing of motor action were therefore balanced across blocks, so that conditions differed only in how the timing of skip decisions was generated. We reasoned that across-trial variability of EEG could carry as much information about the source of skip decisions as the mean RP. EEG variability decreased more markedly prior to self-initiated compared to externally-triggered skip actions. This convergence suggests a consistent preparatory process prior to self-initiated action. A leaky stochastic accumulator model could reproduce this convergence given the additional assumption of a systematic decrease in input noise prior to self-initiated actions. Our results may provide a novel neurophysiological perspective on the topical debate regarding whether self-initiated actions arise from a deterministic neurocognitive process, or from neural stochasticity. We suggest that the key precursor of self-initiated action may manifest as a reduction in neural noise.

Learning in tactile channels.

Vibrotactile intensity-discrimination thresholds for sinusoidal stimuli applied to the thenar eminence of the hand declined as a function of practice. However, improvement was confined to the tactile information-processing channel in which learning had occurred. Specifically, improvements in performance with training within the Pacinian-corpuscle (PC) channel with a 250-Hz stimulus failed to transfer to performance within the rapidly adapting (RA) nerve fiber channel and the slowly adapting Type I (SA I) nerve fiber channel with a 20-Hz stimulus; similarly, improvements in performance with training within the RA/SA I channels failed to transfer to the PC channel. The hypothesis that tactile intensity-discrimination learning involves changes in sensory processes rather than the acquisition of the general skills required to perform the intensity-discrimination task is supported by the finding that improvements in performance with practice did not transfer to the untrained contralateral hand and by the finding of no transfer of training across channels. Within the PC channel, improvements in intensity discrimination with training transferred from the training intensity level (20-dB SL) to an untrained intensity level (10-dB SL), showing that learning within the PC channel under a specific stimulus condition can generalize to another stimulus condition provided both stimuli are processed by the PC channel. The finding that intensity-discrimination training, which resulted in a substantial reduction in the intensity difference limen, had no effect on the slope of the sensation-magnitude function suggests that tactile intensity-discrimination learning results primarily from a progressive reduction in neural noise over the course of training.

1/f Neural Noise Reduction and Spike Feature Extraction Using a Subset of Informative Samples

This article describes a study on neural noise and neural signal feature extraction, targeting real-time spike sorting with miniaturized microchip implementation. Neuronal signature, noise shaping, and adaptive bandpass filtering are reported as the techniques to enhance the signal-to-noise ratio (SNR). A subset of informative samples of the waveforms is extracted as features for classification. Quantitative and comparative experiments with both synthesized and animal data are included to evaluate different feature extraction approaches. In addition, a preliminary hardware implementation has been realized using an integrated circuit.

Noise reduction for speech recognition

A neural network for noise reduction for speech recognition in a noisy environment uses an algorithm for automatic network generation which automatically selects a suitable signal representation. Nodes may be added to the input layer of the neural network successively, with a new node being trained by calculating and minimizing a mapping error. A squared mapping error may be formed and the mapping error may be assigned a weight dependent on the importance of the vectors. In addition, a neural network that performs neural noise reduction by reducing, in a training phase, a mapping error between noise-free vectors at an output of the neural network and noise-reduced vectors at the output of the neural network using an iterative process, has the mapping error further reduced by additional information which is selected from a suitable signal representation at the input of the neural network.