Faller W E, Luttges M W
Aerospace Engineering Sciences, University of Colorado, Boulder 80309-0429.
Biomed Sci Instrum. 1990;26:201-8.
Techniques are described that allow the use of multiple neuron spike data in a computational neural network architecture. The network architecture was devised to match the number of actual neurons from which data were obtained. The network was successfully trained to accurately predict the multiple neuron spike trains. Simultaneous spike histories of 44 neurons were modeled by a network architecture consisting of 44 input units, 88 hidden units with recurrent connections and 44 output units. The activation function of each unit was determined by data unique to a single neuron. These data were coupled with an analog gradient that preserved both the exact spiking times and the relative spiking tendency of each neuron. The input activation values were compared to network output target values calculated to occur 5 msec forward in the composite spiking records of all neurons. Following 2000 training cycles with the gradient data, the average error of each unit in the network was 0.0016. Discrete output values for each network unit were correlated with those of all other units. These correlations were comparable to those done using the actual neuron data. Both correlations reveal a functional connectivity pattern among the units and neurons. These connectivity patterns indicate that the networks may synthesize patterns of activity needed for biological function; in this case, flight patterns carried out in the mesothoracic ganglion of the dragonfly. This model represents, to the best of our knowledge, the first computer based network simulation using actual experimental neural data obtained from a large number of spontaneously active cells in a small intact ganglion.
本文描述了一些技术，这些技术允许在计算神经网络架构中使用多个神经元的尖峰数据。该网络架构的设计旨在匹配获取数据的实际神经元数量。该网络经过成功训练，能够准确预测多个神经元的尖峰序列。44个神经元的同步尖峰历史由一个网络架构进行建模，该架构由44个输入单元、88个具有循环连接的隐藏单元和44个输出单元组成。每个单元的激活函数由单个神经元特有的数据确定。这些数据与一个模拟梯度相结合，该梯度保留了每个神经元的确切尖峰时间和相对尖峰趋势。将输入激活值与在所有神经元的复合尖峰记录中提前5毫秒计算出的网络输出目标值进行比较。在使用梯度数据进行2000个训练周期后，网络中每个单元的平均误差为0.0016。每个网络单元的离散输出值与所有其他单元的输出值相关。这些相关性与使用实际神经元数据所做的相关性相当。两种相关性都揭示了单元和神经元之间的功能连接模式。这些连接模式表明，网络可能会合成生物功能所需的活动模式；在这种情况下，是在蜻蜓中胸神经节中执行的飞行模式。据我们所知，该模型代表了第一个基于计算机的网络模拟，它使用了从小的完整神经节中大量自发活动细胞获得的实际实验神经数据。
