Network requirements for parametric working memory

申请人提供的描述）：该提案旨在使 候选人成为一个成熟的，有成就的研究人员在该领域 理论和计算神经科学，通过个人指导， 参加培训班和研讨会，特别是通过强化 完成一个高质量的研究项目。这位候选人有 在理论物理学方面产生了出色的研究，但需要一段时间， 发展成为一个独立的，多产的科学家在该领域 神经科学他选择了一个有既定研究生课程的机构 在神经科学，并与教师的高度赞誉，以获得最好的 培训质量。 该研究项目将解决工作记忆的细胞机制， 这有助于阐明认知的一个关键过程。该项目的重点是 参数工作记忆的机制，其中一个量（例如频率） 具有连续范围的值可以被编码并存储在发射中， 神经元的比率。候选人将实现一个计算模型网络， 含有具有生物物理特性的神经元， 参数工作记忆任务中的数据集。他将测试哪些配置文件的 突触连接可以产生必要的稳定网络状态， 参数工作记忆，假设突触的强度 与神经元的连接与神经元的兴奋性相关。 他假设，长期可塑性是实现突触的必要条件。 连接工作记忆所必需的，所以将测试哪种长 术语可塑性使网络稳定。他假设短期内 可塑性是必要的，以产生时间变化的记忆状态，如所看到的 在实验中。他将测试哪种形式的短期可塑性导致 观察到的行为，以及它们是否有助于记忆的稳定性 states.

DESCRIPTION provided by applicant): The proposal is designed to enable the candidate to become a mature, accomplished researcher in the field of theoretical and computational neuroscience, through individual mentoring, participation in courses and seminars, and particularly through intensive training to complete a high-quality research project. The candidate has produced excellent research in theoretical physics, but requires a period of development to become an independent, productive scientist in the field of neuroscience. He has chosen an institution with an established graduate program in neuroscience, and with faculty of high acclaim in order to gain the best quality of training. The research project will address the cellular mechanisms of working memory and so help elucidate a key process in cognition. The project focuses on the mechanisms of parametric working memory, whereby a quantity (such as frequency) with a continuous range of values can be encoded and memorized in the firing rates of neurons. The candidate will implement a computational model network, containing neurons with biophysical properties, which will reproduce specific sets of data in a parametric working memory task. He will test what profiles of synaptic connectivity can produce the necessary stable network states for parametric working memory, hypothesizing that the strength of synaptic connections to a neuron correlates with the excitability of the neuron. He hypothesizes that long-term plasticity is necessary to achieve the synaptic connectivity necessary for working memory, so will test which kinds of long term plasticity stabilize the network. He hypothesizes that short-term plasticity is necessary to produce time variation in the memory states, as seen in experiment. He will test which forms of short-term plasticity lead to the observed behavior, and whether they contribute to the stability of the mnemonic states.