NOISE AND ENDOCRINE OF SENSORY INFORMATION

噪声和感官信息内分泌

• 批准号: 2249419
• 负责人: DANTE R CHIALVO
• 金额: $ 9.64万
• 依托单位: SANTA FE INSTITUTE
• 依托单位国家: 美国
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-07-01 至 1995-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2249419
• 关键词: Annelida; axon; cats; computational neuroscience; electrostimulus; intracellular membranes; membrane potentials; neural information processing; neural transmission; sensory signal detection; stimulus /response

Appropriate encoding of information is a major task in the nervous system. Neural mechanisms by which sensory inputs are first encoded and later decoded in the central nervous system have to take into account the presence of considerable uncorrelated background "noise". To date there is no clear understanding on how the nervous system efficiently improves the signal to noise ratio. A recent development in the field of bistable systems shows that under specific circumstances an increase in the system's input noise can lead to a decrease in the output noise. In this phenomena known as stochastic resonance, the output signal of a noisy bistable system can be modulated in time by applying (additive or multiplicative) a weak external periodic forcing. Preliminary evidence shows that similar phenomena occurs in neuron models. Using this novel theoretical argument this project will examine experimental evidence in support of the hypothesis that noise plays an active role in neuronal encoding. The project will explore several quantitative signatures of stochastic resonance on the response of sensory neurons to periodic stimuli. Estimates of these measures are taken at the cellular and at the system level (in different levels of the neuraxis, from primary afferent fibers to cortex) to clarify the role of stochastic resonance in neuronal encoding of sensory information. Theoretical studies of neurons' generic and ionic models are directed to formulate and characterize measures of stochastic resonance in excitable systems and to explore how rudimentary networks working at the range of stochastic resonance might "decode" the information embedded in the spike train. These theoretical and experimental results should show that noise in the nervous system is actively controlled and modulated in order to encode sensory information. Overall, the results will provide a better understanding of somatosensory neural encoding.

适当的信息编码是神经系统的一项主要任务， 系统 感觉输入首先被编码的神经机制 后来在中枢神经系统中解码时必须考虑到 存在相当大的不相关背景“噪声”。 迄今 对神经系统如何有效地改善 信噪比。 最近，在生物医学领域的一项新进展是， 系统显示，在特定情况下， 系统的输入噪声可以导致输出噪声的降低。 在这 被称为随机共振的现象，噪声的输出信号， 可通过施加（添加剂或 乘法）一个弱的外部周期强迫。 初步证据 类似的现象也发生在神经元模型中。 使用该新颖 理论论证本项目将检验实验证据， 支持噪声在神经元中起积极作用的假设 编码. 该项目将探讨几个量化的签名， 随机共振对感觉神经元周期性反应的影响 刺激。 这些措施的估计是在蜂窝和 系统水平（在不同水平的神经轴，从主要的 皮层的传入纤维），以阐明随机共振的作用 神经元对感觉信息的编码。 的理论研究 神经元的通用和离子模型是针对制定和 描述可激发系统中随机共振的特征， 探索基本的网络如何在随机范围内工作， 共振可以“解码”嵌入尖峰序列中的信息。 这些理论和实验结果应该表明， 神经系统被主动控制和调节，以便编码 感官信息 总体而言，结果将提供更好的 对躯体感觉神经编码的理解