NONLINEAR MODEL OF HIPPOCAMPUS

海马体非线性模型

• 批准号: 6120734
• 负责人: THEODORE W BERGER
• 金额: $ 13.56万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-09-30 至 1999-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6120734
• 关键词: bioengineering /biomedical engineering; biomedical resource; model design /development; nervous system; psychology; technology /technique

During this past year we have made major progress on developing and applying several of the essential modeling methodologies for input/output, or nonparametric, models of point-process input neural systems such as the hippocampus. First, we have pursued development and application of adaptive estimation procedures for decomposition studies of the hippocampus. Using this approach, we have been able to successfully estimate linear and nonlinear properties of negative feedback elements (analogous to inhibitory interneurons) given input/output data for only the feedthrough element in the presence and absence of inhibition, i.e., without direct observations from the feedback pathway. Second, we have developed a new means for estimating input/output properties of neural elements using artificial (feedforward) neural networks, which we have demonstrated is particularly powerful for estimating higher order nonlinearities from electrophysiological data. We are particularly excited about the potential for expanding this approach for estimating higher order cross-kernels for multiple-input conditions. Third, for the first time we have applied our nonlinear systems approach at the level of intracellularly recorded synaptic potentials. We have recently modeled the closed- and open-loop AMPA and NMDA components of glutamatergic synaptic input to hippocampal neurons, have derived a representation of the dynamics of the positive feedback to the NMDA receptor-channel, and can accurately predict control EPSPs to a wide range of input patterns. In addition, the model can account for differences between closed- and open-loop NMDA receptor-mediated synaptic responses. These are two of the most widely studied receptor subtypes, as the dynamics of their interaction determine the direction and magnitude of activity-dependent synaptic plasticity in hippocampus, neocortex, and other brain regions. Finally, we have made considerable progress in developing highly efficient circuit designs for analog VLSI implementation of kernel models of hippocampal neurons, a direction that should provide the means for developing massively parallel architectures for simulating global hippocampal system properties.

在过去的一年里，我们在发展 并应用几种基本的建模方法， 点过程输入神经网络的输入/输出或非参数模型 海马体等系统。 第一，我们追求发展 以及分解的自适应估计程序的应用 海马体的研究。 通过这种方法，我们能够 成功地估计了负的线性和非线性性质 反馈元件（类似于抑制性中间神经元） 在存在时仅用于馈通元件的输入/输出数据，以及 没有抑制，即，如果没有直接的观察， 反馈途径 第二，我们开发了一种新的方法， 使用人工神经元估计神经元的输入/输出特性 （前馈）神经网络，我们已经证明， 对于估计高阶非线性， 电生理数据。 我们特别兴奋的是， 扩展这种方法估计高阶的潜力 多输入条件下的交叉内核。 第三，对于第一 时间，我们已经应用我们的非线性系统的方法在水平上， 细胞内记录的突触电位。 我们最近 模拟了闭环和开环AMPA和NMDA组件， 海马神经元的突触输入，已经衍生出一个 对NMDA的正反馈的动态表示 受体通道，并能准确地预测控制EPSPs到广泛的 输入模式的范围。 此外，该模型可以解释 闭环和开环NMDA受体介导的差异 突触反应 这是两种研究最广泛的受体 亚型，因为它们相互作用的动力学决定了 和活动依赖性突触可塑性的大小， 海马体、新皮层和其他大脑区域。 我们终于有 在开发高效电路方面取得了很大进展 海马核模型的模拟VLSI实现设计 神经元，一个方向，应该提供发展的手段， 用于模拟全球海马的大规模并行架构 系统属性。