Neuroscience (also known as neurobiology) is a science that studies the structure, function, development, pharmacology and pathology of the nervous system. In recent years, C. Cotardo has introduced coding theory into...Neuroscience (also known as neurobiology) is a science that studies the structure, function, development, pharmacology and pathology of the nervous system. In recent years, C. Cotardo has introduced coding theory into neuroscience, proposing the concept of combinatorial neural codes. And it was further studied in depth using algebraic methods by C. Curto. In this paper, we construct a class of combinatorial neural codes with special properties based on classical combinatorial structures such as orthogonal Latin rectangle, disjoint Steiner systems, groupable designs and transversal designs. These neural codes have significant weight distribution properties and large minimum distances, and are thus valuable for potential applications in information representation and neuroscience. This study provides new ideas for the construction method and property analysis of combinatorial neural codes, and enriches the study of algebraic coding theory.展开更多
Neural representations arise from high-dimensional population activity,but current neuromodulation methods lack the precision to write information into the central nervous system at this complexity.In this perspective...Neural representations arise from high-dimensional population activity,but current neuromodulation methods lack the precision to write information into the central nervous system at this complexity.In this perspective,we propose high-dimensional stimulation as an approach to better approximate natural neural codes for brain-machine interfaces.Key advancements in resolution,coverage,and safety are essential,with flexible microelectrode arrays offering a promising path toward precise synthetic neural codes.展开更多
By mcans of stable attractors of discret Hopfield neural network (DHNN) , anew class of nonlinear error control codes is sugsested and some relativetheorems are presented. A kind of single error control codes is also ...By mcans of stable attractors of discret Hopfield neural network (DHNN) , anew class of nonlinear error control codes is sugsested and some relativetheorems are presented. A kind of single error control codes is also given forillustrating this new approach.展开更多
Presents the fuzzy neural network optimized by hybrid coded genetic algorithm of decimal encoding and binary encoding, the searching ability and stability of genetic algorithms enhanced by using binary encoding during...Presents the fuzzy neural network optimized by hybrid coded genetic algorithm of decimal encoding and binary encoding, the searching ability and stability of genetic algorithms enhanced by using binary encoding during the crossover operation and decimal encoding during the mutation operation, and the way of accepting new individuals by probability adopted, by which a new individual is accepted and its parent is discarded when its fitness is higher than that of its parent, and a new individual is accepted by probability when its fitness is lower than that of its parent. And concludes with calculations made with an example that these improvements enhance the speed of genetic algorithms to optimize the fuzzy neural network controller.展开更多
A method of assigning binary indexes to codevectors in vector quantization (VQ)system, which is called pseudo-Gray coding, is presented in this paper by constructing a kind of Hopfield neural network. Pseudo-Gray codi...A method of assigning binary indexes to codevectors in vector quantization (VQ)system, which is called pseudo-Gray coding, is presented in this paper by constructing a kind of Hopfield neural network. Pseudo-Gray coding belongs to joint source/channel coding, which could provide a redundancy-free error protection scheme for VQ of analog signals when the binary indexes of signal codevectors are used as channel symbols on a discrete memoryless channel. Since pseudo-Gray coding is of combinatorial optimization problems which are NP-complete problems,globally optimal solutions are generally impossible. Thus, a kind of Hopfield neural network is used by constructing suitable energy function to get sub-optimal solutions. This kind of Hop field neural network is easily modified to solve simplified version of pseudo-Gray coding for single bit-error channel model. Simulating experimental results show that the method introduced here could offer good performances.展开更多
In recent decades, brain science has been enriched from both empirical and computational approaches. Interesting emerging neural features include power-law distribution, chaotic behavior, self-organized criticality, v...In recent decades, brain science has been enriched from both empirical and computational approaches. Interesting emerging neural features include power-law distribution, chaotic behavior, self-organized criticality, variance approach, neuronal avalanches, difference-based and sparse coding, optimized information transfer, maximized dynamic range for information processing, and reproducibility of evoked spatio-temporal motifs in spontaneous activities, and so on. These intriguing findings can be largely categorized into two classes: complexity and regularity. This article would like to highlight that the above-mentioned properties although look diverse and unrelated, but actually may be rooted in a common foundation—excitatory and inhibitory balance (EIB) and ongoing activities (OA). To be clear, description and observation of neural features are phenomena or epiphenomena, while EIB-OA is the underlying mechanism. The EIB is maintained in a dynamic manner and may possess regional specificity, and importantly, EIB is organized along the boundary of phase transition which has been called criticality, bifurcation or edge of chaos. OA is composed of spontaneous organized activity, physiological noise, non-physiological noise and the interacting effect between OA and evoked activities. Based on EIB-OA, the brain may accommodate the property of chaos and regularity. We propose “virtual brain space” to bridge brain dynamics and mental space, and “code driving complexity hypothesis” to integrate regularity and complexity. The functional implication of oscillation and energy consumption of the brain are discussed.展开更多
As a main structure of the limbic system,the hippocampus plays a critical role in pain perception and chronicity.The ventral hippocampal CA1(vCA1)is closely associated with negative emotions such as anxiety,stress,and...As a main structure of the limbic system,the hippocampus plays a critical role in pain perception and chronicity.The ventral hippocampal CA1(vCA1)is closely associated with negative emotions such as anxiety,stress,and fear,yet how vCA1 neurons encode nociceptive information remains unclear.Using in vivo electrophysiological recording,we characterized vCA1 pyramidal neuron subpopulations that exhibited inhibitory or excitatory responses to plantar stimuli and were implicated in encoding stimuli modalities in naïve rats.Functional heterogeneity of the vCA1 pyramidal neurons was further identified in neuropathic pain conditions:the proportion and magnitude of the inhibitory response neurons paralleled mechanical allodynia and contributed to the confounded encoding of innocuous and noxious stimuli,whereas the excitatory response neurons were still instrumental in the discrimination of stimulus properties.Increased theta power and theta-spike coupling in vCA1 correlated with nociceptive behaviors.Optogenetic inhibition of vCA1 pyramidal neurons induced mechanical allodynia in naïve rats,whereas chemogenetic reversal of the overall suppressed vCA1 activity had analgesic effects in rats with neuropathic pain.These results provide direct evidence for the representations of nociceptive information in vCA1.展开更多
Neural interaction is realized by information exchange. It seemed that the information amount does not keep constant and may be reduced during the travel between neural nodes. In addition, recent research of neural co...Neural interaction is realized by information exchange. It seemed that the information amount does not keep constant and may be reduced during the travel between neural nodes. In addition, recent research of neural coding has suggested that neural information could be represented by parsimonious spiking pattern, named sparse coding. Based on the above observation, neuro-messenger theory (NMT) is proposed to explicate the communicative process between the source and the target neural nodes. Neuro-messenger is a sparse code which does not have to carry every detail of the dynamics in source node. Other formats of neural coding (e.g., temporal and rate coding) could be the precursors of neuro-messengers, and the repeated spatiotemporal patterns buried in the ongoing brain activities may be the circulated neuro-messengers<span> from diverse origins. Referred to chaos/complexity theory, information can be recovered at target node where neuro-messenger serves as a facilitator to locate the trajectory at proper </span><span style="font-family:Verdana;"></span><span style="font-family:;" "=""><span>attractor, and hence the associated psychological entity. In contrast to conventional concepts of encoding and decoding, the processes of encoding in source node, issuing neuro-messengers,</span> and recovering information at target node are summarized as “three-facet coding scheme”. The design of neuro-messenger enables the brain to utilize energy in an efficient and economical way. NMT may have substantial implication in several major psychiatric disorders. Some psychiatric conditions could be mediated by abnormal neuro-messengers that coerce the regional neuro-dynamics to delve into maladaptive attractors and hence the characteristic symptoms.</span>展开更多
A novel cochlear implant coding strategy based on the neural excitability has been developed and implemented using Matlab/Simulink. Unlike present day coding strategies, the Excitability Controlled Coding (ECC) strate...A novel cochlear implant coding strategy based on the neural excitability has been developed and implemented using Matlab/Simulink. Unlike present day coding strategies, the Excitability Controlled Coding (ECC) strategy uses a model of the excitability state of the target neural population to determine its stimulus selection, with the aim of more efficient stimulation as well as reduced channel interaction. Central to the ECC algorithm is an excitability state model, which takes into account the supposed refractory behaviour of the stimulated neural populations. The excitability state, used to weight the input signal for selecting the stimuli, is estimated and updated after the presentation of each stimulus, and used iteratively in selecting the next stimulus. Additionally, ECC regulates the frequency of stimulation on a given channel as a function of the corresponding input stimulus intensity. Details of the model, implementation and results of benchtop plus subjective tests are presented and discussed. Compared to the Advanced Combination Encoder (ACE) strategy, ECC produces a better spectral representation of an input signal, and can potentially reduce channel interactions. Pilot test results from 4 CI recipients suggest that ECC may have some advantage over ACE for complex situations such as speech in noise, possibly due to ECC’s ability to present more of the input spectral contents compared to ACE, which is restricted to a fixed number of maxima. The ECC strategy represents a neuro-physiological approach that could potentially improve the perception of more complex sound patterns with cochlear implants.展开更多
This paper proposed a back propagation neural network model for predictive block-matching. Predictive block-matching is a way to significantly decrease the computational complexity of motion estimation, but the tradit...This paper proposed a back propagation neural network model for predictive block-matching. Predictive block-matching is a way to significantly decrease the computational complexity of motion estimation, but the traditional prediction model was proposed 26 years ago. It is straight forward but not accurate enough. The proposed back propagation neural network has 5 inputs, 5 neutrons and 1 output. Because of its simplicity, it requires very little calculation power which is negligible compared with existing computation complexity. The test results show 10% - 30% higher prediction accuracy and PSNR improvement up to 0.3 dB. The above advantages make it a feasible replacement of the current model.展开更多
文摘Neuroscience (also known as neurobiology) is a science that studies the structure, function, development, pharmacology and pathology of the nervous system. In recent years, C. Cotardo has introduced coding theory into neuroscience, proposing the concept of combinatorial neural codes. And it was further studied in depth using algebraic methods by C. Curto. In this paper, we construct a class of combinatorial neural codes with special properties based on classical combinatorial structures such as orthogonal Latin rectangle, disjoint Steiner systems, groupable designs and transversal designs. These neural codes have significant weight distribution properties and large minimum distances, and are thus valuable for potential applications in information representation and neuroscience. This study provides new ideas for the construction method and property analysis of combinatorial neural codes, and enriches the study of algebraic coding theory.
基金funded by R01EY036094(L.L)R01NS102917(C.X)+1 种基金U01NS115588(C.X)U01NS131086(C.X&L.L.)。
文摘Neural representations arise from high-dimensional population activity,but current neuromodulation methods lack the precision to write information into the central nervous system at this complexity.In this perspective,we propose high-dimensional stimulation as an approach to better approximate natural neural codes for brain-machine interfaces.Key advancements in resolution,coverage,and safety are essential,with flexible microelectrode arrays offering a promising path toward precise synthetic neural codes.
文摘By mcans of stable attractors of discret Hopfield neural network (DHNN) , anew class of nonlinear error control codes is sugsested and some relativetheorems are presented. A kind of single error control codes is also given forillustrating this new approach.
文摘Presents the fuzzy neural network optimized by hybrid coded genetic algorithm of decimal encoding and binary encoding, the searching ability and stability of genetic algorithms enhanced by using binary encoding during the crossover operation and decimal encoding during the mutation operation, and the way of accepting new individuals by probability adopted, by which a new individual is accepted and its parent is discarded when its fitness is higher than that of its parent, and a new individual is accepted by probability when its fitness is lower than that of its parent. And concludes with calculations made with an example that these improvements enhance the speed of genetic algorithms to optimize the fuzzy neural network controller.
文摘A method of assigning binary indexes to codevectors in vector quantization (VQ)system, which is called pseudo-Gray coding, is presented in this paper by constructing a kind of Hopfield neural network. Pseudo-Gray coding belongs to joint source/channel coding, which could provide a redundancy-free error protection scheme for VQ of analog signals when the binary indexes of signal codevectors are used as channel symbols on a discrete memoryless channel. Since pseudo-Gray coding is of combinatorial optimization problems which are NP-complete problems,globally optimal solutions are generally impossible. Thus, a kind of Hopfield neural network is used by constructing suitable energy function to get sub-optimal solutions. This kind of Hop field neural network is easily modified to solve simplified version of pseudo-Gray coding for single bit-error channel model. Simulating experimental results show that the method introduced here could offer good performances.
文摘In recent decades, brain science has been enriched from both empirical and computational approaches. Interesting emerging neural features include power-law distribution, chaotic behavior, self-organized criticality, variance approach, neuronal avalanches, difference-based and sparse coding, optimized information transfer, maximized dynamic range for information processing, and reproducibility of evoked spatio-temporal motifs in spontaneous activities, and so on. These intriguing findings can be largely categorized into two classes: complexity and regularity. This article would like to highlight that the above-mentioned properties although look diverse and unrelated, but actually may be rooted in a common foundation—excitatory and inhibitory balance (EIB) and ongoing activities (OA). To be clear, description and observation of neural features are phenomena or epiphenomena, while EIB-OA is the underlying mechanism. The EIB is maintained in a dynamic manner and may possess regional specificity, and importantly, EIB is organized along the boundary of phase transition which has been called criticality, bifurcation or edge of chaos. OA is composed of spontaneous organized activity, physiological noise, non-physiological noise and the interacting effect between OA and evoked activities. Based on EIB-OA, the brain may accommodate the property of chaos and regularity. We propose “virtual brain space” to bridge brain dynamics and mental space, and “code driving complexity hypothesis” to integrate regularity and complexity. The functional implication of oscillation and energy consumption of the brain are discussed.
基金supported by the National Natural Science Foundation of China(81974166,32271053,and 31872774).
文摘As a main structure of the limbic system,the hippocampus plays a critical role in pain perception and chronicity.The ventral hippocampal CA1(vCA1)is closely associated with negative emotions such as anxiety,stress,and fear,yet how vCA1 neurons encode nociceptive information remains unclear.Using in vivo electrophysiological recording,we characterized vCA1 pyramidal neuron subpopulations that exhibited inhibitory or excitatory responses to plantar stimuli and were implicated in encoding stimuli modalities in naïve rats.Functional heterogeneity of the vCA1 pyramidal neurons was further identified in neuropathic pain conditions:the proportion and magnitude of the inhibitory response neurons paralleled mechanical allodynia and contributed to the confounded encoding of innocuous and noxious stimuli,whereas the excitatory response neurons were still instrumental in the discrimination of stimulus properties.Increased theta power and theta-spike coupling in vCA1 correlated with nociceptive behaviors.Optogenetic inhibition of vCA1 pyramidal neurons induced mechanical allodynia in naïve rats,whereas chemogenetic reversal of the overall suppressed vCA1 activity had analgesic effects in rats with neuropathic pain.These results provide direct evidence for the representations of nociceptive information in vCA1.
文摘Neural interaction is realized by information exchange. It seemed that the information amount does not keep constant and may be reduced during the travel between neural nodes. In addition, recent research of neural coding has suggested that neural information could be represented by parsimonious spiking pattern, named sparse coding. Based on the above observation, neuro-messenger theory (NMT) is proposed to explicate the communicative process between the source and the target neural nodes. Neuro-messenger is a sparse code which does not have to carry every detail of the dynamics in source node. Other formats of neural coding (e.g., temporal and rate coding) could be the precursors of neuro-messengers, and the repeated spatiotemporal patterns buried in the ongoing brain activities may be the circulated neuro-messengers<span> from diverse origins. Referred to chaos/complexity theory, information can be recovered at target node where neuro-messenger serves as a facilitator to locate the trajectory at proper </span><span style="font-family:Verdana;"></span><span style="font-family:;" "=""><span>attractor, and hence the associated psychological entity. In contrast to conventional concepts of encoding and decoding, the processes of encoding in source node, issuing neuro-messengers,</span> and recovering information at target node are summarized as “three-facet coding scheme”. The design of neuro-messenger enables the brain to utilize energy in an efficient and economical way. NMT may have substantial implication in several major psychiatric disorders. Some psychiatric conditions could be mediated by abnormal neuro-messengers that coerce the regional neuro-dynamics to delve into maladaptive attractors and hence the characteristic symptoms.</span>
文摘A novel cochlear implant coding strategy based on the neural excitability has been developed and implemented using Matlab/Simulink. Unlike present day coding strategies, the Excitability Controlled Coding (ECC) strategy uses a model of the excitability state of the target neural population to determine its stimulus selection, with the aim of more efficient stimulation as well as reduced channel interaction. Central to the ECC algorithm is an excitability state model, which takes into account the supposed refractory behaviour of the stimulated neural populations. The excitability state, used to weight the input signal for selecting the stimuli, is estimated and updated after the presentation of each stimulus, and used iteratively in selecting the next stimulus. Additionally, ECC regulates the frequency of stimulation on a given channel as a function of the corresponding input stimulus intensity. Details of the model, implementation and results of benchtop plus subjective tests are presented and discussed. Compared to the Advanced Combination Encoder (ACE) strategy, ECC produces a better spectral representation of an input signal, and can potentially reduce channel interactions. Pilot test results from 4 CI recipients suggest that ECC may have some advantage over ACE for complex situations such as speech in noise, possibly due to ECC’s ability to present more of the input spectral contents compared to ACE, which is restricted to a fixed number of maxima. The ECC strategy represents a neuro-physiological approach that could potentially improve the perception of more complex sound patterns with cochlear implants.
文摘This paper proposed a back propagation neural network model for predictive block-matching. Predictive block-matching is a way to significantly decrease the computational complexity of motion estimation, but the traditional prediction model was proposed 26 years ago. It is straight forward but not accurate enough. The proposed back propagation neural network has 5 inputs, 5 neutrons and 1 output. Because of its simplicity, it requires very little calculation power which is negligible compared with existing computation complexity. The test results show 10% - 30% higher prediction accuracy and PSNR improvement up to 0.3 dB. The above advantages make it a feasible replacement of the current model.
