NOISE AND ENDOCRINE OF SENSORY INFORMATION

对信息进行适当编码是一项令人紧张的主要任务 系统。感觉输入最初被编码的神经机制和 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”。到目前为止在那里 对神经系统如何有效地改善还没有清楚的了解 信噪比。双稳态研究领域的最新进展 系统显示，在特定情况下， 系统的输入噪声会导致输出噪声的降低。在这 这种现象称为随机共振，输出的信号中含有噪声 双稳系统可以通过施加(添加剂或 乘法)微弱的外部周期性强迫。初步证据 表明在神经元模型中也出现了类似的现象。用这本小说 理论论证本项目将检验 支持噪声在神经元中起积极作用的假说 编码。该项目将探索几个量化签名 感觉神经元对周期反应的随机共振 刺激物。对这些措施的估计是在蜂窝和 系统水平(在神经轴的不同水平，从初级 皮质传入纤维)以阐明随机共振的作用 在感觉信息的神经元编码中。的理论研究 神经元的一般模型和离子模型被用来描述和 刻画可激发系统中随机共振的度量和 探索基本网络如何在随机范围内工作 共振可能会对嵌入在棘波序列中的信息进行“解码”。 这些理论和实验结果应该表明， 神经系统被主动控制和调制，以便编码 感官信息。总体而言，结果将提供更好的 体感神经编码的理解。

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.