Mechanism Of Neural Information Processing Research Articles

Research on the Robustness Mechanism of Maeda–Makino Hardware Neuron Based on Symbolic Dynamics

Noise permeates every level of the nervous systems, from single neurons to the whole system. However, the noise seems not to take so much trouble to neural information processing in contrast to the electronic system. The robustness of neural information processing inspired us to study the method of neuromorphic hardware systems to resist electromagnetic interference. The dynamics of the neuron model can reflect the inherent mechanism of neural information processing. In this work, we take the Maeda-Makino (MM) hardware neuron as an example to study the robustness mechanism of biological neuron information encoding based on symbolic dynamics. MM hardware neuron is a neuromorphic neuron circuit with biological plausibility. The simulation results show that MM hardware neuron can encode the order of current stimulus periods, even with parameter disturbance and noisy input signals which simulate the effects of electromagnetic interference on neuronal cell membrane permeability, sodium ion channels, potassium ion channels, and action potentials. Choosing a robust hardware neuron is the basis for building a neuromorphic hardware system that can resist electromagnetic interference. This letter not only verifies the robustness of using symbol sequence encoding but also provides an optional hardware neuron solution for neuromorphic hardware designed for anti-electromagnetic interference.

Healthy aging delays the neural processing of face features relevant for behavior by 40 ms.

Fast and accurate face processing is critical for everyday social interactions, but it declines and becomes delayed with age, as measured by both neural and behavioral responses. Here, we addressed the critical challenge of understanding how aging changes neural information processing mechanisms to delay behavior. Young (20–36 years) and older (60–86 years) adults performed the basic social interaction task of detecting a face versus noise while we recorded their electroencephalogram (EEG). In each participant, using a new information theoretic framework we reconstructed the features supporting face detection behavior, and also where, when and how EEG activity represents them. We found that occipital‐temporal pathway activity dynamically represents the eyes of the face images for behavior ~170 ms poststimulus, with a 40 ms delay in older adults that underlies their 200 ms behavioral deficit of slower reaction times. Our results therefore demonstrate how aging can change neural information processing mechanisms that underlie behavioral slow down.

Mechanism of neural information processing of sound localization by interaural level difference

More attention should be paid to the neural system, which is a quantitative system. There are few reports about the quantitative research on neural system. It will hinder the quantitative studies on animal binaural sound localization. The existing physiological experiments have found that there is a monotonic increase/decrease relationship between the input sound levels and the output spike frequencies of auditory neurons, so the variations of input sound level can be simplified into the change of output spike frequency of auditory neurons. In this paper, based on the theory of circle map and symbolic dynamics, a quantitative model of auditory neural circuitry is presented. In this neural circuit model, the neurons of ipsilateral input circuit propagate the action potentials as excitatory inputs, the neurons of heterolateral input one propagate the action potentials as inhibitory inputs. We also use a chemical neuronal model, which shows that the neurotransmitters released from pre-synapse vesicles have characteristics of quantitative release. The strength of the coupling between two neurons is represented by a coupling coefficient. The relationship between the input/output spike sequences of neural circuitry is simulated by using an Hodgkin-Huxley equation. In the range of simulating parameters, there is a monotonic increase/decrease phenomenon between input and output spike frequency. For the neuron with a single input and output structure, it is symbolized according to the method of symbolic dynamics; for the neurons with multi-input and single output structure, the output spike time will be used to detect the input spike frequency variations which are caused by the changes of interaural level difference (ILD), and the binaural level differences from those spike sequences are analyzed to locate the source of the sounds. With the increase of output spikes, the length of symbolic sequence increases, the symbolic sequence is sensitive to the variation of input signal. The simulation results show that the quantitative model proposed in this paper is able to detect the ILD signals by neural spike sequences.

Single-Trial Decoding of Bistable Perception Based on Sparse Nonnegative Tensor Decomposition

The study of the neuronal correlates of the spontaneous alternation in perception elicited by bistable visual stimuli is promising for understanding the mechanism of neural information processing and the neural basis of visual perception and perceptual decision-making. In this paper, we develop a sparse nonnegative tensor factorization-(NTF)-based method to extract features from the local field potential (LFP), collected from the middle temporal (MT) visual cortex in a macaque monkey, for decoding its bistable structure-from-motion (SFM) perception. We apply the feature extraction approach to the multichannel time-frequency representation of the intracortical LFP data. The advantages of the sparse NTF-based feature extraction approach lies in its capability to yield components common across the space, time, and frequency domains yet discriminative across different conditions without prior knowledge of the discriminating frequency bands and temporal windows for a specific subject. We employ the support vector machines (SVMs) classifier based on the features of the NTF components for single-trial decoding the reported perception. Our results suggest that although other bands also have certain discriminability, the gamma band feature carries the most discriminative information for bistable perception, and that imposing the sparseness constraints on the nonnegative tensor factorization improves extraction of this feature.

Emotional Intelligence predicts individual differences in social exchange reasoning

When assessed with performance measures, Emotional Intelligence (EI) correlates positively with the quality of social relationships. However, the bases of such correlations are not understood in terms of cognitive and neural information processing mechanisms. We investigated whether a performance measure of EI is related to reasoning about social situations (specifically social exchange reasoning) using versions of the Wason Card Selection Task. In an fMRI study (N=16), higher EI predicted hemodynamic responses during social reasoning in the left frontal polar and left anterior temporal brain regions, even when controlling for responses on a very closely matched task (precautionary reasoning). In a larger behavioral study (N=48), higher EI predicted faster social exchange reasoning, after controlling for precautionary reasoning. The results are the first to directly suggest that EI is mediated in part by mechanisms supporting social reasoning and validate a new approach to investigating EI in terms of more basic information processing mechanisms.

Neurons in Golgi-stain-like images revealed by GFP-adenovirus infection in vivo.

Neurons in the adult brain have a very complex morphology with many processes, including tremendously long axons. Since dendrites and axons play key roles in the input and output of neural information, respectively, the visualization of complete images of these processes is necessary to reveal the mechanism of neural information processing. Here we made a recombinant adenovirus vector which encodes green fluorescent protein (GFP) tagged with a palmitoylation site, a membrane-targeting signal, produced specific antibodies to GFP, and used them as probes for staining the nervous system. In the neocortex, after injection of the recombinant virus and immunoperoxidase staining with the antibodies, many different types of cells were labeled in a Golgi stain-like fashion. Although the number of labeled cells varied depending on the amount of virus injected, the recombinant virus was considered to be infectious to cortical neurons of all cell types without selectivity. In contrast, the viral infection in the cerebellar cortex and superior cervical ganglion showed some selectivity toward the cell type. It is expected that this recombinant virus will be a useful tool for the morphological analysis of neuronal connections, especially the analysis of microcircuitry in the cerebral cortex.

Determination of the favored pattern in neural spike train with monte carlo method and template method

The modern neuroelectrical physiology indicates that there are certain patterns in neural spike train which appear more frequently than others. These favored patterns(FP) may be related to the mechanism of neural information processing in central nervous system. In this paper, the quantized Monte Carlo method and, template method used in favored pattern recognition are described. The Monte Carlo method determines favored patterns or the possible favored patterns, while the template method is used for choosing the FP from the candidates and counting the number of the FP in the spike train. This work will be pursued for extracting the FP in some complicated spike train.

Chemical transfer of learned behavior.

The controversial ‘memory transfer’ experiments developed during the last few years into a new bioassay approach for the study of the chemical correlates of learning and the molecular coding of neural information. As with all biological assays, the reliability of the method is limited because of the multiplicity of the factors determining success or failure. The experience accumulated in the last five years in many laboratories explains most of the past failures and suggests conditions for the best assay designs. With the help of this method, we were able to identify a pentadecapeptide, called scotophobin, formed in rat brain during acquisition of dark avoidance. It is hoped that a sufficient number of these coded molecules will be identified in the near future to give us a broader base for studying the molecular mechanism of neural information processing.