Parametric modeling of the temporal dynamics of neuronal responses using connectionist architectures.
Parametric modeling of the temporal dynamics of neuronal responses using connectionist architectures.
复制标题
使用联结主义架构对神经元反应的时间动态进行参数化建模。
DOI：
10.1152/jn.1993.69.3.980
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发表时间：
1993
影响因子：
2.5
通讯作者：
Margoliash,D
中科院分区：
文献类型：
--
作者：
Bankes,SC;Margoliash,D
1. 我们描述了动态（时变）神经生理学数据参数化建模的探索性方法。该模型使用时间窗口中的刺激数据来预测该窗口结束时的神经元放电率。最成功的模型是由输入、隐藏和输出“节点”组成的前馈三层网络，这些网络通过权重连接，这些权重在训练阶段通过反向传播算法进行调整。这些模型中的记忆由层之间传播激活的不同长度的延迟线表示。还测试了无记忆的联结模型（每层 1 个顺序节点）以及零记忆非线性非联结模型。 2. 用乌拉坦麻醉斑马雀 (Taeniopygia guttata) 听觉丘脑卵圆核的神经元活动记录来测试模型。这里报告的所有细胞都显示出阶段性/紧张性反应。对一个神经元（细胞 1）的广泛建模定义了一种“规范”架构，该架构在对该细胞的建模中最为成功。规范模型具有零内存输入层、具有 29 个 bin（代表 185.6 ms）的隐藏层，以及单个输出节点，其值作为顺序 bin 位置的函数表示神经元的输出作为时间的函数。规范模型实现了单元 1 整个数据集的收敛，包括对单音爆发和斑胸草雀鸣叫的响应。规范模型的“频率”权重与单元 1 的兴奋和抑制频率相匹配，如单元的频率调谐曲线所确定的。规范模型的“记忆”权重在前 25 毫秒内由兴奋主导，随后是抑制。 3. 当仅使用对突发音调的动态响应进行训练时，规范模型还准确地预测了对歌曲的响应（平均 R2 = 0.823）。因此，对于该神经元来说，对单音突发的响应足以预测对六首不同歌曲的大部分响应，每首歌曲以三种不同的幅度呈现，尽管蒙特卡洛程序表明残余方差不仅仅是由噪声引起的（P < 0.001）。 4. 通过改变突发音调（训练）数据，进一步探索了模型预测歌曲反应的能力。对于单元 1，对歌曲的反应与对突发音调的时间反应的相位/音调细节密切相关。神经元的特征频率、频率调谐曲线和速率/强度函数的变化影响较小。这些实验很难或不可能从电生理学角度进行。（摘要截断为 400 字）
1. We describe an exploratory approach to the parametric modeling of dynamical (time-varying) neurophysiological data. The models use stimulus data from a window of time to predict the neuronal firing rate at the end of that window. The most successful models were feedforward three-layered networks of input, hidden, and output “nodes” connected by weights that were adjusted during a training phase by the backpropagation algorithm. The memory in these models was represented by delay lines of varying length propagating activation between the layers. Connectionist models with no memory (1 sequential node per layer) as well as zero-memory nonlinear nonconnectionist models were also tested. 2. Models were tested with recordings of neuronal activity from the auditory thalamic nucleus ovoidalis of urethane-anesthetized zebra finches (Taeniopygia guttata). All cells reported here showed phasic/tonic responses. Extensive modeling of one neuron (cell 1) defined a “canonical” architecture, which was most successful in modeling this cell. The canonical model had a zero-memory input layer, a hidden layer with 29 bins representing 185.6 ms, and a single output node whose value as a function of sequential bin position represented the output of the neuron as a function of time. The canonical model achieved convergence on the entire data set for cell 1, including responses to single tone bursts and zebra finch songs. The “requency” weights of the canonical model matched well excitatory and inhibitory frequencies for cell 1 as determined by the cell's frequency tuning curve. The “memory” weights of the canonical model were dominated by excitation over the first 25 ms followed by inhibition. 3. When trained with only the dynamical responses to tone bursts, the canonical model also accurately predicted the responses to song (average R2 = 0.823). Thus for this neuron the responses to single tone bursts were sufficient to predict most of the responses to six different songs, each presented at three different amplitudes, although a Monte Carlo procedure indicated the residual variance was not just due to noise (P < 0.001). 4. The model's ability to predict the responses to song was further explored by altering the tone burst (training) data. For cell 1, the responses to songs were most strongly related to the phasic/tonic details of the temporal responses to tone bursts. Changes in the characteristic frequency, frequency tuning curves, and rate/intensity function of the neuron had less effect. These experiments would be difficult or impossible to conduct electrophysiologically.(ABSTRACT TRUNCATED AT 400 WORDS)