INFORMATION PROCESSING

信息处理

• 批准号: 3409987
• 负责人: IDAN SEGEV
• 金额: $ 9.41万
• 依托单位: HEBREW UNIVERSITY OF JERUSALEM
• 依托单位国家: 美国
• 财政年份: 1988
• 资助国家: 美国
• 起止时间: 1988-04-01 至 1991-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3409987
• 关键词: Malacostraca; artificial intelligence; axon; biophysics; computer simulation; dendrites; electrophysiology; ganglions; mathematical model; model design /development; neural information processing; neuroanatomy; neurochemistry; neurons; neurophysiology; online computer; saltwater environment; synapses

A new simultaneous experimental and theoretical interdisciplinary approach to study information processing in neuronal networks is suggested. It combines a detailed electro-morphological study at the cellular level with mathematical analysis using a new tool for the Simulation of Neuronal Networks (SONN) which we plan to develop. The idea is to consider neurons as a composition of subunits (e.g., axonal branch point), each performs specific information processing functions (Parnas and Segev, 1979). These functional subunits are first characterized experimentally, then analyzed using detailed partial differential equations (P.D.E) models (e.g., Parnas et al., 1986 a, b, c). Investigating these experimentally based models will eventually allow to abstract the subunits as independent processors or State Machines (S.M), which transform input to output and changes their internal states. SONN will include both P.D.E and the S.M models, the latter will serve to build a whole neuronal network and to analyze its information processing aspects in comparison to the experimental network. We believe that this approach will help to establish a theoretical framework for connecting biophysical mechanisms to the function of the neural-network and will aid to refine and redefine models in Artificial Intelligence. The heart ganglion of the lobster is the experimental network we plan to investigate. The initial modeling will be done with SPICE, a general-purpose electrical network analysis program, which we have adapted for the use of neurobiologists (Segev et al., 1985). However, its limitations and the realization that an appropriate modeling tool is crucial for our purposes, prompted us to propose to develop SONN for performing "on line" theoretical experiments. The emphasis is on developing graphical and mathematical tools to visualize the morphology and the electrical behavior of the modeled system using color codes. We believe this approach will lead to a better understanding of the principles that underlie the function of neuronal networks.

一个新的实验和理论同时交叉学科 研究神经网络中信息处理的方法是 建议。 它结合了详细的电形态学研究， 使用新工具进行数学分析的细胞水平 神经元网络模拟（SONN），我们计划 开发. 这个想法是把神经元看作是 子单元（例如， 轴突分支点），每个都执行特定的 信息处理功能（Parnas和Segev，1979）。 这些 功能亚基首先通过实验表征，然后 使用详细偏微分方程（P.D.E）分析 模型（例如，Parnas等人，1986 a、B、c）。 调查这些 基于实验的模型将最终允许抽象的 子单元作为独立的处理器或状态机（S.M）， 将输入转换为输出并改变其内部状态。 SONN将包括PD E和S.M两种型号，后者将 用于构建一个完整的神经网络，并分析其 信息处理方面与实验相比， 网络 我们认为，这种做法将有助于建立一个 将生物物理机制与 神经网络的功能，将有助于完善和 重新定义人工智能中的模型。 龙虾的心脏神经节是我们 计划进行调查。 初始建模将使用SPICE完成， 一个通用的电气网络分析程序，我们 已经适应了神经生物学家的使用（Segev等人，1985年）。 然而，它的局限性和实现适当的 建模工具对我们的目的至关重要，促使我们提出 开发SONN用于执行“在线”理论 实验 重点是开发图形和 数学工具来可视化形态学和电 使用颜色代码的建模系统的行为。 我们认为 这种方法将有助于更好地理解这些原则 神经网络功能的基础。