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CoDi
CoDi is a cellular automaton (CA) model for spiking neural networks (SNNs). CoDi is an acronym for Collect and Distribute, referring to the signals and spikes in a neural network. CoDi uses a von Neumann neighborhood modified for a three-dimensional space; each cell looks at the states of its six orthogonal neighbors and its own state. In a growth phase a neural network is grown in the CA-space based on an underlying chromosome. There are four types of cells: neuron body, axon, dendrite and blank. The growth phase is followed by a signaling- or processing-phase. Signals are distributed from the neuron bodies via their axon tree and collected from connection dendrites. These two basic interactions cover every case, and they can be expressed simply, using a small number of rules. Cell interaction during signaling The neuron body cells collect neural signals from the surrounding dendritic cells and apply an internally defined function to the collected data. In the CoDi model ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
CoDi CA Signaling
CoDi is a cellular automaton (CA) model for spiking neural networks (SNNs). CoDi is an acronym for Collect and Distribute, referring to the signals and spikes in a neural network. CoDi uses a von Neumann neighborhood modified for a three-dimensional space; each cell looks at the states of its six orthogonal neighbors and its own state. In a growth phase a neural network is grown in the CA-space based on an underlying chromosome. There are four types of cells: neuron body, axon, dendrite and blank. The growth phase is followed by a signaling- or processing-phase. Signals are distributed from the neuron bodies via their axon tree and collected from connection dendrites. These two basic interactions cover every case, and they can be expressed simply, using a small number of rules. Cell interaction during signaling The neuron body cells collect neural signals from the surrounding dendritic cells and apply an internally defined function to the collected data. In the CoDi model ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
CoDi CA Signaling Rules
CoDi is a cellular automaton (CA) model for spiking neural networks (SNNs). CoDi is an acronym for Collect and Distribute, referring to the signals and spikes in a neural network. CoDi uses a von Neumann neighborhood modified for a three-dimensional space; each cell looks at the states of its six orthogonal neighbors and its own state. In a growth phase a neural network is grown in the CA-space based on an underlying chromosome. There are four types of cells: neuron body, axon, dendrite and blank. The growth phase is followed by a signaling- or processing-phase. Signals are distributed from the neuron bodies via their axon tree and collected from connection dendrites. These two basic interactions cover every case, and they can be expressed simply, using a small number of rules. Cell interaction during signaling The neuron body cells collect neural signals from the surrounding dendritic cells and apply an internally defined function to the collected data. In the CoDi model ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Codi Growth Chromosome
CoDi is a cellular automaton (CA) model for spiking neural networks (SNNs). CoDi is an acronym for Collect and Distribute, referring to the signals and spikes in a neural network. CoDi uses a von Neumann neighborhood modified for a three-dimensional space; each cell looks at the states of its six orthogonal neighbors and its own state. In a growth phase a neural network is grown in the CA-space based on an underlying chromosome. There are four types of cells: neuron body, axon, dendrite and blank. The growth phase is followed by a signaling- or processing-phase. Signals are distributed from the neuron bodies via their axon tree and collected from connection dendrites. These two basic interactions cover every case, and they can be expressed simply, using a small number of rules. Cell interaction during signaling The neuron body cells collect neural signals from the surrounding dendritic cells and apply an internally defined function to the collected data. In the CoDi model ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Codi Chromosome Start Growth
CoDi is a cellular automaton (CA) model for spiking neural networks (SNNs). CoDi is an acronym for Collect and Distribute, referring to the signals and spikes in a neural network. CoDi uses a von Neumann neighborhood modified for a three-dimensional space; each cell looks at the states of its six orthogonal neighbors and its own state. In a growth phase a neural network is grown in the CA-space based on an underlying chromosome. There are four types of cells: neuron body, axon, dendrite and blank. The growth phase is followed by a signaling- or processing-phase. Signals are distributed from the neuron bodies via their axon tree and collected from connection dendrites. These two basic interactions cover every case, and they can be expressed simply, using a small number of rules. Cell interaction during signaling The neuron body cells collect neural signals from the surrounding dendritic cells and apply an internally defined function to the collected data. In the CoDi model ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Codi 16bit Cell
CoDi is a cellular automaton (CA) model for spiking neural networks (SNNs). CoDi is an acronym for Collect and Distribute, referring to the signals and spikes in a neural network. CoDi uses a von Neumann neighborhood modified for a three-dimensional space; each cell looks at the states of its six orthogonal neighbors and its own state. In a growth phase a neural network is grown in the CA-space based on an underlying chromosome. There are four types of cells: neuron body, axon, dendrite and blank. The growth phase is followed by a signaling- or processing-phase. Signals are distributed from the neuron bodies via their axon tree and collected from connection dendrites. These two basic interactions cover every case, and they can be expressed simply, using a small number of rules. Cell interaction during signaling The neuron body cells collect neural signals from the surrounding dendritic cells and apply an internally defined function to the collected data. In the CoDi model ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cultured Neuronal Network
A cultured neuronal network is a cell culture of neurons that is used as a model to study the central nervous system, especially the brain. Often, cultured neuronal networks are connected to an input/output device such as a multi-electrode array (MEA), thus allowing two-way communication between the researcher and the network. This model has proved to be an invaluable tool to scientists studying the underlying principles behind neuronal learning, memory, plasticity, connectivity, and information processing. Cultured neurons are often connected via computer to a real or simulated robotic component, creating a hybrot or animat, respectively. Researchers can then thoroughly study learning and plasticity in a realistic context, where the neuronal networks are able to interact with their environment and receive at least some artificial sensory feedback. One example of this can be seen in the Multielectrode Array Art (MEART) system developed by the Potter Research Group at the Georg ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Spiking Neural Network
Spiking neural networks (SNNs) are artificial neural networks that more closely mimic natural neural networks. In addition to neuronal and synaptic state, SNNs incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not transmit information at each propagation cycle (as it happens with typical multi-layer perceptron networks), but rather transmit information only when a membrane potential – an intrinsic quality of the neuron related to its membrane electrical charge – reaches a specific value, called the threshold. When the membrane potential reaches the threshold, the neuron fires, and generates a signal that travels to other neurons which, in turn, increase or decrease their potentials in response to this signal. A neuron model that fires at the moment of threshold crossing is also called a spiking neuron model. The most prominent spiking neuron model is the leaky integrate-and-fire model. In the integrate-and-fir ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Neurogrid
Neurogrid is a piece of computer hardware that is designed specifically for simulation of biological brains. It uses analog computation to emulate ion channel activity, and digital communication to softwire structured connectivity patterns. Neurogrid simulates one million neurons and six billion synapses in real time. The neurons spike at a rate of ten times a second. In terms of number of simulated neurons, it rivals simulations done by the Blue Brain Project. However, by running the simulation of whole neurons, instead of simulation on molecular level, it needs only one millionth of Blue Brain's power. The entire board consumes less than two watts of electrical energy. Neurogrid was designed and built by the ''Brains in Silicon'' group at Stanford University. The group is led by Kwabena Boahen. The Neurogrid hardware was first up and running in late 2009. Since then it has been used to start performing simulation experiments. The Neurogrid board contains sixteen ''Neurocores'', ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Spiking Neural Network
Spiking neural networks (SNNs) are artificial neural networks that more closely mimic natural neural networks. In addition to neuronal and synaptic state, SNNs incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not transmit information at each propagation cycle (as it happens with typical multi-layer perceptron networks), but rather transmit information only when a membrane potential – an intrinsic quality of the neuron related to its membrane electrical charge – reaches a specific value, called the threshold. When the membrane potential reaches the threshold, the neuron fires, and generates a signal that travels to other neurons which, in turn, increase or decrease their potentials in response to this signal. A neuron model that fires at the moment of threshold crossing is also called a spiking neuron model. The most prominent spiking neuron model is the leaky integrate-and-fire model. In the integrate-and-fir ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cellular Automaton
A cellular automaton (pl. cellular automata, abbrev. CA) is a discrete model of computation studied in automata theory. Cellular automata are also called cellular spaces, tessellation automata, homogeneous structures, cellular structures, tessellation structures, and iterative arrays. Cellular automata have found application in various areas, including physics, theoretical biology and microstructure modeling. A cellular automaton consists of a regular grid of ''cells'', each in one of a finite number of '' states'', such as ''on'' and ''off'' (in contrast to a coupled map lattice). The grid can be in any finite number of dimensions. For each cell, a set of cells called its ''neighborhood'' is defined relative to the specified cell. An initial state (time ''t'' = 0) is selected by assigning a state for each cell. A new ''generation'' is created (advancing ''t'' by 1), according to some fixed ''rule'' (generally, a mathematical function) that determines the new state o ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Artificial Brain
An artificial brain (or artificial mind) is software and hardware with cognitive abilities similar to those of the animal or human brain. Research investigating "artificial brains" and brain emulation plays three important roles in science: #An ongoing attempt by neuroscientists to understand how the human brain works, known as cognitive neuroscience. #A thought experiment in the philosophy of artificial intelligence, demonstrating that it is possible, at least in theory, to create a machine that has all the capabilities of a human being. #A long-term project to create machines exhibiting behavior comparable to those of animals with complex central nervous system such as mammals and most particularly humans. The ultimate goal of creating a machine exhibiting human-like behavior or intelligence is sometimes called strong AI. An example of the first objective is the project reported by Aston University in Birmingham, England where researchers are using biological cells to create " ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Neurochip
A neurochip is an integrated circuit chip (such as a microprocessor) that is designed for interaction with neuronal cells. Formation It is made of silicon that is doped in such a way that it contains EOSFETs (electrolyte- oxide-semiconductor field-effect transistors) that can sense the electrical activity of the neurons (action potentials) in the above-standing physiological electrolyte solution. It also contains capacitors for the electrical stimulation of the neurons. The University of Calgary, Faculty of Medicine scientists led by Pakistani-born Canadian scientist Naweed Syed who proved it is possible to cultivate a network of brain cells that reconnect on a silicon chip—or the brain on a microchip—have developed new technology that monitors brain cell activity at a resolution never achieved before. Developed with the National Research Council Canada (NRC), the new silicon chips are also simpler to use, which will help future understanding of how brain cells work und ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
