scholarly journals Deciphering the Brain's Codes

1991 ◽  
Vol 3 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Masakazu Konishi
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Lower Order ◽  
Stimulus Pattern ◽  
Neural Circuits ◽  
Sensory Systems ◽  
Hierarchical Design ◽  
Motor Systems ◽  
Parallel Channels ◽  
The Brain

The two sensory systems discussed in this review use similar algorithms for the synthesis of the neuronal selectivity for the stimulus that releases a particular behavior, although the neural circuits, the brain sites involved, and even the species are different. This stimulus selectivity emerges gradually in a neural network organized according to parallel and hierarchical design principles. The parallel channels contain lower order stations with special circuits for the creation of neuronal selectivities for different features of the stimulus. Convergence of the parallel pathways brings these selectivities together at a higher order station for the eventual synthesis of the selectivity for the whole stimulus pattern. The neurons that are selective for the stimulus are at the top of the hierarchy, and they form the interface between the sensory and motor systems or between sensory systems of different modalities. The similarities of these two systems at the level of algorithms suggest the existence of rules of signal processing that transcend different sensory systems and species of animals.

Signal Processing and Classification for Electroencephalography Based Motor Imagery Brain Computer Interface

2021 ◽  
Vol 11 (12) ◽  
pp. 2918-2927
Author(s):  
A. Shankar ◽  
S. Muttan ◽  
D. Vaithiyanathan
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Motor Imagery ◽  
Classification Accuracy ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Discrete Wavelet ◽  
Feature Sets ◽  
Evaluation Of Performance ◽  
The Brain

Brain Computer Interface (BCI) is a fast growing area of research to enable communication between our brains and computers. EEG based motor imagery BCI involves the user imagining movement, the subsequent recording and signal processing on the electroencephalogram signals from the brain, and the translation of those signals into specific commands. Ultimately, motor imagery BCI has the potential to be applied to helping those with special abilities recover motor control. This paper presents an evaluation of performance for EEG based motor imagery BCI with a classification accuracy of 80.2%, making use of features extracted using the Fast Fourier Transform and the Discrete Wavelet Transform, and classification is done using an Artificial Neural Network. It goes on to conclude how the performance is affected by the particular feature sets and neural network parameters.

scholarly journals Contribution of linear and nonlinear mechanisms to predictive motion estimation

2021 ◽  
Author(s):  
Belle Liu ◽  
Arthur Hong ◽  
Fred Rieke ◽  
Michael B. Manookin
Keyword(s):  
Neural Circuits ◽  
Sensory Systems ◽  
Neural Systems ◽  
Sparse Signal ◽  
Response Suppression ◽  
Delayed Response ◽  
Signal Integration ◽  
Linear And Nonlinear ◽  
Successful Behavior ◽  
The Brain

Successful behavior relies on the ability to use information obtained from past experience to predict what is likely to occur in the future. A salient example of predictive encoding comes from the vertebrate retina, where neural circuits encode information that can be used to estimate the trajectory of a moving object. Predictive computations should be a general property of sensory systems, but the features needed to identify these computations across neural systems are not well understood. Here, we identify several properties of predictive computations in the primate retina that likely generalize across sensory systems. These features include calculating the derivative of incoming signals, sparse signal integration, and delayed response suppression. These findings provide a deeper understanding of how the brain carries out predictive computations and identify features that can be used to recognize these computations throughout the brain.

scholarly journals Let Sing Again. Of Sounds, Songs, and Language in Humans and Birds

2020 ◽  
Author(s):  
Umberto di Porzio ◽  
Luisa Speranza
Keyword(s):  
Neural Network ◽  
Motor Skills ◽  
Neural Circuits ◽  
Human Life ◽  
Vocal Learning ◽  
Social Bonding ◽  
Neural Pathways ◽  
Music And Language ◽  
Network Music ◽  
The Brain

In million years, under the pressure of natural selection, hominins acquired vocal learning, music, language, and intense cooperation, thanks to the efficacy of music in enhancing sociality. Thus, early in human evolution music became part of human life, a relevant activity, which required sophisticated perceptual and motor skills. It contributed to developing cultures and history, social bonding, and from the beginning of life strengthens the mother-baby relation while within the mother’s womb. Music existed in all known human cultures, although it varies in rhythmic and melodic complexity. It is art made of sounds capable of arousing emotions, evokes memories, engages multiple cognitive functions, and promotes attention, concentration, stimulates the imagination, creativity, and harmony of movement. Music and language share the same complex neural network. Music changes the chemistry of the brain activating the reward and prosocial systems, altruism, and allowing its use in therapy. This review explores "what" is music and illustrates the neural circuits that allow the production of music and language and those that transduce the sounds perceived by the ear, localize and archive them, allowing to recall them. Interestingly, songbirds share many commonalities with human music:, common neural pathways that shape vocal learning, and how they make sounds.

scholarly journals Predictive visual motion extrapolation emerges spontaneously and without supervision from a layered neural network with spike-timing-dependent plasticity

2020 ◽  
Author(s):  
Anthony N. Burkitt ◽  
Hinze Hogendoorn
Keyword(s):  
Neural Network ◽  
Neural Circuits ◽  
Spike Timing ◽  
Moving Object ◽  
Present Moment ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Neural Populations ◽  
Neural Transmission ◽  
The Brain

AbstractThe fact that the transmission and processing of visual information in the brain takes time presents a problem for the accurate real-time localisation of a moving object. One way this problem might be solved is extrapolation: using an object’s past trajectory to predict its location in the present moment. Here, we investigate how a simulated in silico layered neural network might implement such extrapolation mechanisms, and how the necessary neural circuits might develop. We allowed an unsupervised hierarchical network of velocity-tuned neurons to learn its connectivity through spike-timing dependent plasticity. We show that the temporal contingencies between the different neural populations that are activated by an object as it moves causes the receptive fields of higher-level neurons to shift in the direction opposite to their preferred direction of motion. The result is that neural populations spontaneously start to represent moving objects as being further along their trajectory than where they were physically detected. Due to the inherent delays of neural transmission, this effectively compensates for (part of) those delays by bringing the represented position of a moving object closer to its instantaneous position in the world. Finally, we show that this model accurately predicts the pattern of perceptual mislocalisation that arises when human observers are required to localise a moving object relative to a flashed static object (the flash-lag effect).Significance StatementOur ability to track and respond to rapidly changing visual stimuli, such as a fast moving tennis ball, indicates that the brain is capable of extrapolating the trajectory of a moving object in order to predict its current position, despite the delays that result from neural transmission. Here we show how the neural circuits underlying this ability can be learned through spike-timing dependent synaptic plasticity, and that these circuits emerge spontaneously and without supervision. This demonstrates how the neural transmission delays can, in part, be compensated to implement the extrapolation mechanisms required to predict where a moving object is at the present moment.

LOCALLY IMPLEMENTABLE MATRIX FLOWS AND THEIR APPLICATION TO NEURAL NETWORK LEARNING

1994 ◽  
Vol 04 (01) ◽  
pp. 23-51 ◽  
Author(s):  
JEROEN DEHAENE ◽  
JOOS VANDEWALLE
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Network Structure ◽  
Adaptive Signal Processing ◽  
External Signal ◽  
Neural Network Learning ◽  
Network Learning ◽  
Adaptive Signal

A number of matrix flows, based on isospectral and isodirectional flows, is studied and modified for the purpose of local implementability on a network structure. The flows converge to matrices with a predefined spectrum and eigenvectors which are determined by an external signal. The flows can be useful for adaptive signal processing applications and are applied to neural network learning.

scholarly journals Generalization of Patterns by Identification with Polynomial Neural Network

2010 ◽  
Vol 61 (2) ◽  
pp. 120-124 ◽  
Author(s):  
Ladislav Zjavka
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Functional Dependence ◽  
Polynomial Neural Network ◽  
New Type ◽  
Input Variables ◽  
The Brain ◽  
Functional Output ◽  
Simplified Form

Generalization of Patterns by Identification with Polynomial Neural Network Artificial neural networks (ANN) in general classify patterns according to their relationship, they are responding to related patterns with a similar output. Polynomial neural networks (PNN) are capable of organizing themselves in response to some features (relations) of the data. Polynomial neural network for dependence of variables identification (D-PNN) describes a functional dependence of input variables (not entire patterns). It approximates a hyper-surface of this function with multi-parametric particular polynomials forming its functional output as a generalization of input patterns. This new type of neural network is based on GMDH polynomial neural network and was designed by author. D-PNN operates in a way closer to the brain learning as the ANN does. The ANN is in principle a simplified form of the PNN, where the combinations of input variables are missing.

scholarly journals Tracheal occlusion-evoked respiratory load compensation and inhibitory neurotransmitter expression in rats

2014 ◽  
Vol 116 (8) ◽  
pp. 1006-1016 ◽  
Author(s):  
Hsiu-Wen Tsai ◽  
Paul W. Davenport
Keyword(s):  
Neural Network ◽  
Brain Stem ◽  
Breathing Pattern ◽  
Respiratory Rhythm ◽  
Inhibitory Neurotransmitter ◽  
Load Compensation ◽  
Efferent Projections ◽  
Motor Reflex ◽  
Sensory Pathway ◽  
The Brain

Respiratory load compensation is a sensory-motor reflex generated in the brain stem respiratory neural network. The nucleus of the solitary tract (NTS) is thought to be the primary structure to process the respiratory load-related afferent activity and contribute to the modification of the breathing pattern by sending efferent projections to other structures in the brain stem respiratory neural network. The sensory pathway and motor responses of respiratory load compensation have been studied extensively; however, the mechanism of neurogenesis of load compensation is still unknown. A variety of studies has shown that inhibitory interconnections among the brain stem respiratory groups play critical roles for the genesis of respiratory rhythm and pattern. The purpose of this study was to examine whether inhibitory glycinergic neurons in the NTS were activated by external and transient tracheal occlusions (ETTO) in anesthetized animals. The results showed that ETTO produced load compensation responses with increased inspiratory, expiratory, and total breath time, as well as elevated activation of inhibitory glycinergic neurons in the caudal NTS (cNTS) and intermediate NTS (iNTS). Vagotomized animals receiving transient respiratory loads did not exhibit these load compensation responses. In addition, vagotomy significantly reduced the activation of inhibitory glycinergic neurons in the cNTS and iNTS. The results suggest that these activated inhibitory glycinergic neurons in the NTS might be essential for the neurogenesis of load compensation responses in anesthetized animals.

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