Network requirements for parametric working memory

申请者提供的描述)：该建议旨在使 成为一名成熟的、有成就的研究人员的候选人 理论和计算神经科学，通过个人指导， 参加课程和研讨会，特别是通过 培训以完成高质量的研究项目。候选人有 在理论物理方面做出了出色的研究，但需要一段时间 发展成为一名独立的、富有成效的科学家 神经科学。他选择了一所拥有成熟研究生课程的机构 在神经科学方面，并拥有高度赞誉的能力，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和 这有助于阐明认知过程中的一个关键过程。该项目的重点是 参数工作记忆的机制，借此一个量(如频率) 具有连续范围的值可以在点火时被编码和记忆 神经元率。候选人将实施计算模型网络， 包含具有生物物理特性的神经元，这些神经元将复制特定的 参数工作记忆任务中的数据集。他将测试哪些档案 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有关。 他假设长时间的可塑性是实现突触所必需的 工作记忆所需的连通性，所以会测试哪种长 项塑性稳定了网络。他假设短期内 可塑性是产生记忆状态的时间变化所必需的，如图所示 在实验中。他将测试哪些形式的短期可塑性会导致 观察到的行为，以及它们是否有助于助记的稳定性 各州。

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.