Data/hora: 13/06/2024 02:43:50

Provedor de dados: | Nature Precedings
País: | United Kingdom
Título: | A Mathematical Model of a Neuron with Synapses based on Physiology
Autores: | Xiaolin Zhang.
Data: | 2008-03-21
Ano: | 2008
Palavras-chave: | Neuroscience.
Resumo: | The neuron, when considered as a signal processing device, itsinputs are the frequency of pulses received at the synapses, and its output is the frequency of action potentials generated- in essence, a neuron is a pulse frequency signal processing device. In comparison, electrical devices use either digital or analog signals for communication or processing, and the mathematics behind these subjects is well understood. However, in regards to pulse frequency processing devices, there has not yet been a clear and persuasive mathematical model to describe the functions of neurons. It goes without saying that such a model is very important, not only for understanding neuron and neural system behavior, but also for undeveloped potential applications in industry. This paper proposes a method for obtaining the mathematical relationship between the input and output signals of a neuron based on physiological facts. The proposed method focuses on the currents across the postsynaptic membrane of each synapse, and the key is to recognize that the net charge across the whole membrane of a neuron over each action potential cycle must equal to zero. By analyzing the relationship between the input of a synapse and the currents across the postsynaptic membranes, a dynamic pulse frequency model of the neuron can be obtained. Here, we show that the transfer function of a neuron depends on the function of thepostsynaptic current of each synapse in resting state, which can be found by detecting the postsynaptic current when a pulse is received at the synapse. The transfer function of a typical neuron generally includes addition and subtraction of feedthrough terms and/or first order lag functions. To focus on the most basic characteristics of a neuron, accommodation, adaptation, learning, etc. are not discussed in this paper.
Tipo: | Manuscript

Identificador:

http://precedings.nature.com/documents/1703/version/1

oai:nature.com:10101/npre.2008.1703.1

http://hdl.handle.net/10101/npre.2008.1703.1

Fonte: | Nature Precedings
Fonte: | Creative Commons Attribution 3.0 License