Biomaterial Database

A history of spike trains as Point Processes in neural coding. 2010

José Pedro Segundo

Department of Neurobiology, University of California-Los Angeles, CA 90095-1763, USA. segundo@ucla.edu

The history of viewing spike trains as point processes along time is that of the inference that spike trains participate in neural codings. A characteristic thought process, the "Conceptual Framework", present always and with sequential components, upholds the logic of this view. Early research in the 1920's introduced the view: experiments, though largely primitive, were designed and interpreted adequately and thus demonstrated convincingly this role. Modern research, starting around 1950, has contributed impressively. Essential was the mathematical Theory of Point Processes that permitted rigorous studies; computer implementation, though no substitute for clear thinking, was enormously helpful. Contributions, besides confirming the key inference, include wide-range train participation in neural codings, with many courses of action and formal implications involving neuronal performances individually and in networks, discharge forms, dynamic behaviors, critical Information and Communication issues, etc.

UI	MeSH Term	Description	Entries
D008959	Models, Neurological	Theoretical representations that simulate the behavior or activity of the neurological system, processes or phenomena; includes the use of mathematical equations, computers, and other electronic equipment.	Neurologic Models,Model, Neurological,Neurologic Model,Neurological Model,Neurological Models,Model, Neurologic,Models, Neurologic
D009435	Synaptic Transmission	The communication from a NEURON to a target (neuron, muscle, or secretory cell) across a SYNAPSE. In chemical synaptic transmission, the presynaptic neuron releases a NEUROTRANSMITTER that diffuses across the synaptic cleft and binds to specific synaptic receptors, activating them. The activated receptors modulate specific ion channels and/or second-messenger systems in the postsynaptic cell. In electrical synaptic transmission, electrical signals are communicated as an ionic current flow across ELECTRICAL SYNAPSES.	Neural Transmission,Neurotransmission,Transmission, Neural,Transmission, Synaptic
D009474	Neurons	The basic cellular units of nervous tissue. Each neuron consists of a body, an axon, and dendrites. Their purpose is to receive, conduct, and transmit impulses in the NERVOUS SYSTEM.	Nerve Cells,Cell, Nerve,Cells, Nerve,Nerve Cell,Neuron
D002415	Cats	The domestic cat, Felis catus, of the carnivore family FELIDAE, comprising over 30 different breeds. The domestic cat is descended primarily from the wild cat of Africa and extreme southwestern Asia. Though probably present in towns in Palestine as long ago as 7000 years, actual domestication occurred in Egypt about 4000 years ago. (From Walker's Mammals of the World, 6th ed, p801)	Felis catus,Felis domesticus,Domestic Cats,Felis domestica,Felis sylvestris catus,Cat,Cat, Domestic,Cats, Domestic,Domestic Cat
D003056	Cochlear Nerve	The cochlear part of the 8th cranial nerve (VESTIBULOCOCHLEAR NERVE). The cochlear nerve fibers originate from neurons of the SPIRAL GANGLION and project peripherally to cochlear hair cells and centrally to the cochlear nuclei (COCHLEAR NUCLEUS) of the BRAIN STEM. They mediate the sense of hearing.	Acoustic Nerve,Auditory Nerve,Acoustic Nerves,Auditory Nerves,Cochlear Nerves,Nerve, Acoustic,Nerve, Auditory,Nerve, Cochlear,Nerves, Acoustic,Nerves, Auditory,Nerves, Cochlear
D003198	Computer Simulation	Computer-based representation of physical systems and phenomena such as chemical processes.	Computational Modeling,Computational Modelling,Computer Models,In silico Modeling,In silico Models,In silico Simulation,Models, Computer,Computerized Models,Computer Model,Computer Simulations,Computerized Model,In silico Model,Model, Computer,Model, Computerized,Model, In silico,Modeling, Computational,Modeling, In silico,Modelling, Computational,Simulation, Computer,Simulation, In silico,Simulations, Computer
D000200	Action Potentials	Abrupt changes in the membrane potential that sweep along the CELL MEMBRANE of excitable cells in response to excitation stimuli.	Spike Potentials,Nerve Impulses,Action Potential,Impulse, Nerve,Impulses, Nerve,Nerve Impulse,Potential, Action,Potential, Spike,Potentials, Action,Potentials, Spike,Spike Potential
D000818	Animals	Unicellular or multicellular, heterotrophic organisms, that have sensation and the power of voluntary movement. Under the older five kingdom paradigm, Animalia was one of the kingdoms. Under the modern three domain model, Animalia represents one of the many groups in the domain EUKARYOTA.	Animal,Metazoa,Animalia
D012815	Signal Processing, Computer-Assisted	Computer-assisted processing of electric, ultrasonic, or electronic signals to interpret function and activity.	Digital Signal Processing,Signal Interpretation, Computer-Assisted,Signal Processing, Digital,Computer-Assisted Signal Interpretation,Computer-Assisted Signal Interpretations,Computer-Assisted Signal Processing,Interpretation, Computer-Assisted Signal,Interpretations, Computer-Assisted Signal,Signal Interpretation, Computer Assisted,Signal Interpretations, Computer-Assisted,Signal Processing, Computer Assisted
D016571	Neural Networks, Computer	A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.	Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron