Pub Date : 2018-10-24DOI: 10.4324/9781315789347-38
{"title":"Role of the Frontal and Temporal Lobes in Scanning Visual Features","authors":"","doi":"10.4324/9781315789347-38","DOIUrl":"https://doi.org/10.4324/9781315789347-38","url":null,"abstract":"","PeriodicalId":82238,"journal":{"name":"Origins","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46394621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-24DOI: 10.4324/9781315789347-17
M. Xie, K. Pribram, Joseph S. King
Before examining neural interspike intervals to see how they might encode information, an essential question that has first to be answered is whether, under the unstimulated condition, the apparent randomness of the neural firing paltern renects deterministic chaos or a stochastic process. Here, we use short term predictability and the structure of the prediction residual to determine the dynamic characteristics of interspike intervals. As demonstrated in given computer simulations, unlike stochastic processes, deterministic chaos is highly predictable in the short term by linear and I or nonlinear prediction techniques. interspike intervals recorded from somatosensory cortex and hippocampus were, thus, analyzed by using the same techniques. The results show that the neuml spontaneous interspike intervals are poorly predictable in the short term, and the models that best fit the interspike intenals are linear (AR or ARMA) stationary processes. Therefore, the pattern of neural spontaneous firing can be characterized as stochastic ratber tban deterministically chaotic.
{"title":"Are Neural Spike Trains Deterministically Chaotic or Stochastic Processes?","authors":"M. Xie, K. Pribram, Joseph S. King","doi":"10.4324/9781315789347-17","DOIUrl":"https://doi.org/10.4324/9781315789347-17","url":null,"abstract":"Before examining neural interspike intervals to see how they might encode information, an essential question that has first to be answered is whether, under the unstimulated condition, the apparent randomness of the neural firing paltern renects deterministic chaos or a stochastic process. Here, we use short term predictability and the structure of the prediction residual to determine the dynamic characteristics of interspike intervals. As demonstrated in given computer simulations, unlike stochastic processes, deterministic chaos is highly predictable in the short term by linear and I or nonlinear prediction techniques. interspike intervals recorded from somatosensory cortex and hippocampus were, thus, analyzed by using the same techniques. The results show that the neuml spontaneous interspike intervals are poorly predictable in the short term, and the models that best fit the interspike intenals are linear (AR or ARMA) stationary processes. Therefore, the pattern of neural spontaneous firing can be characterized as stochastic ratber tban deterministically chaotic.","PeriodicalId":82238,"journal":{"name":"Origins","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44646172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-24DOI: 10.4324/9781315789347-29
{"title":"Visually-triggered Neuronal Oscillations in the Pigeon: An Autocorrelation Study of Tectal Activity","authors":"","doi":"10.4324/9781315789347-29","DOIUrl":"https://doi.org/10.4324/9781315789347-29","url":null,"abstract":"","PeriodicalId":82238,"journal":{"name":"Origins","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48097243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-24DOI: 10.4324/9781315789347-36
{"title":"Auditory \"Objects:\" The Role of Motor Activity in Auditory Perception and Speech Perception","authors":"","doi":"10.4324/9781315789347-36","DOIUrl":"https://doi.org/10.4324/9781315789347-36","url":null,"abstract":"","PeriodicalId":82238,"journal":{"name":"Origins","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48146686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-24DOI: 10.4324/9781315789347-24
N. Farhat, M. Eldefrawy, S. Lin
{"title":"A Bifurcation Model of Neuronal of Spike Train Patterns: A Nonlinear Dynamic Systems Approach","authors":"N. Farhat, M. Eldefrawy, S. Lin","doi":"10.4324/9781315789347-24","DOIUrl":"https://doi.org/10.4324/9781315789347-24","url":null,"abstract":"","PeriodicalId":82238,"journal":{"name":"Origins","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43897717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-24DOI: 10.4324/9781315789347-14
{"title":"Non-Equilibrium Thermodynamics and the Brain","authors":"","doi":"10.4324/9781315789347-14","DOIUrl":"https://doi.org/10.4324/9781315789347-14","url":null,"abstract":"","PeriodicalId":82238,"journal":{"name":"Origins","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47032486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-24DOI: 10.4324/9781315789347-21
{"title":"Towards Simplicity: Noise and Cooperation in the ''Perfect Integrator\"","authors":"","doi":"10.4324/9781315789347-21","DOIUrl":"https://doi.org/10.4324/9781315789347-21","url":null,"abstract":"","PeriodicalId":82238,"journal":{"name":"Origins","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48540553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-24DOI: 10.4324/9781315789347-16
Eaton Peabody
Potential strategies for temporal neural processing in the brain and their implications for the design of artificial neural networks are considered. Current connectionist thinking holds that neurons send signals to each other by changes in their average rate of discharge. This implies that there is one output signal per neuron at any given time (scalar coding), and that all neuronal specificity is achieved solely by patterns of synaptic connections. However, information can be carried by temporal codes, in temporal patterns of neural discharges and by relative times of arrival of individual spikes. Temporal coding permits multiplexing of information in the time domain, which potentially increases the flexibility of neural networks. A broadcast model of information transmission is contrasted with the current notion of highly specific connectivity. Evidence for temporal coding in somatoception, audition, electroception, gustation, olfaction and vision is reviewed, and possible neural architectures for temporal information processing are discussed. 1. The role of timing in the brain The human brain is by far the most capable, the most versatile, and the most complex informationprocessing system known to science. For those concerned with problems of artificial intelligence there has long been the dream that once its functional principles are well understood, the design and construction of adaptive devices more powerful than any yet seen could follow in a straightforward manner. Despite great advances, the neurosciences are still far from understanding the nature of the "neural code" underlying the detailed workings of the brain. i.e. exactly which information-processing operations are involved. If we choose to view the brain in informational terms, as an adaptive signalling system embedded within an external environment, then the issue of which aspects of neural activity constitute the "signals" in the system is absolutely critical to understanding its functioning. It is a question which must be answered before all others, because all functional assumptions, interpretations, and models depend upon the appropriate choice of what processes neurons use to convey information. The role of the time patterns of neural discharges in the transmission and processing of information in the nervous system has been debated since the pulsatile nature of nervous transmission was recognized less than a century ago. Because external stimuli can be physically well-characterized and controlled, the encoding of sensory information has always played a pivotal role in more general conceptions of neural coding. 2. Coding by average discharge rate With the advent of single cell recording techniques in neurophysiology, it was generally assumed that neural information is encoded solely in the average neural discharge rates of neurons (Adrian 1928). This notion of a average discharge rate code, sometimes called the Frequency Coding principle1, has persisted and forms the basis
{"title":"As If Time Really Mattered: Temporal Strategies for Neural Coding of Sensory Information","authors":"Eaton Peabody","doi":"10.4324/9781315789347-16","DOIUrl":"https://doi.org/10.4324/9781315789347-16","url":null,"abstract":"Potential strategies for temporal neural processing in the brain and their implications for the design of artificial neural networks are considered. Current connectionist thinking holds that neurons send signals to each other by changes in their average rate of discharge. This implies that there is one output signal per neuron at any given time (scalar coding), and that all neuronal specificity is achieved solely by patterns of synaptic connections. However, information can be carried by temporal codes, in temporal patterns of neural discharges and by relative times of arrival of individual spikes. Temporal coding permits multiplexing of information in the time domain, which potentially increases the flexibility of neural networks. A broadcast model of information transmission is contrasted with the current notion of highly specific connectivity. Evidence for temporal coding in somatoception, audition, electroception, gustation, olfaction and vision is reviewed, and possible neural architectures for temporal information processing are discussed. 1. The role of timing in the brain The human brain is by far the most capable, the most versatile, and the most complex informationprocessing system known to science. For those concerned with problems of artificial intelligence there has long been the dream that once its functional principles are well understood, the design and construction of adaptive devices more powerful than any yet seen could follow in a straightforward manner. Despite great advances, the neurosciences are still far from understanding the nature of the \"neural code\" underlying the detailed workings of the brain. i.e. exactly which information-processing operations are involved. If we choose to view the brain in informational terms, as an adaptive signalling system embedded within an external environment, then the issue of which aspects of neural activity constitute the \"signals\" in the system is absolutely critical to understanding its functioning. It is a question which must be answered before all others, because all functional assumptions, interpretations, and models depend upon the appropriate choice of what processes neurons use to convey information. The role of the time patterns of neural discharges in the transmission and processing of information in the nervous system has been debated since the pulsatile nature of nervous transmission was recognized less than a century ago. Because external stimuli can be physically well-characterized and controlled, the encoding of sensory information has always played a pivotal role in more general conceptions of neural coding. 2. Coding by average discharge rate With the advent of single cell recording techniques in neurophysiology, it was generally assumed that neural information is encoded solely in the average neural discharge rates of neurons (Adrian 1928). This notion of a average discharge rate code, sometimes called the Frequency Coding principle1, has persisted and forms the basis ","PeriodicalId":82238,"journal":{"name":"Origins","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47168532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-24DOI: 10.4324/9781315789347-13
D. Stassinopoulos, P. Bak, P. Alstrøm
{"title":"Self-Organization and Pavlov's Dogs: A Simple Model of the Brain","authors":"D. Stassinopoulos, P. Bak, P. Alstrøm","doi":"10.4324/9781315789347-13","DOIUrl":"https://doi.org/10.4324/9781315789347-13","url":null,"abstract":"","PeriodicalId":82238,"journal":{"name":"Origins","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47431190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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