Department of Electrical Engineering, University of Southern California, Hedco Neuroscience Building, Los Angeles, CA 90089-2520, USA.
Network. 2010;21(1-2):35-90. doi: 10.3109/0954898X.2010.488714.
A central goal of systems neuroscience is to characterize the transformation of sensory input to spiking output in single neurons. This problem is complicated by the large dimensionality of the inputs. To cope with this problem, previous methods have estimated simplified versions of a generic linear-nonlinear (LN) model and required, in most cases, stimuli with constrained statistics. Here we develop the extended Projection Pursuit Regression (ePPR) algorithm that allows the estimation of all of the parameters, in space and time, of a generic LN model using arbitrary stimuli. We first prove that ePPR models can uniformly approximate, to an arbitrary degree of precision, any continuous function. To test this generality empirically, we use ePPR to recover the parameters of models of cortical cells that cannot be represented exactly with an ePPR model. Next we evaluate ePPR with physiological data from primary visual cortex, and show that it can characterize both simple and complex cells, from their responses to both natural and random stimuli. For both simulated and physiological data, we show that ePPR compares favorably to spike-triggered and information-theoretic techniques. To the best of our knowledge, this article contains the first demonstration of a method that allows the estimation of an LN model of visual cells, containing multiple spatio-temporal filters, from their responses to natural stimuli.
系统神经科学的一个核心目标是描述单个神经元中感觉输入到尖峰输出的转换。这个问题由于输入的维度很大而变得复杂。为了解决这个问题，以前的方法估计了通用线性非线性（LN）模型的简化版本，并且在大多数情况下，需要具有受约束统计数据的刺激。在这里，我们开发了扩展的投影寻踪回归（ePPR）算法，该算法允许使用任意刺激来估计通用 LN 模型的所有空间和时间参数。我们首先证明 ePPR 模型可以以任意精度均匀逼近任何连续函数。为了从经验上检验这种通用性，我们使用 ePPR 来恢复皮层细胞模型的参数，这些模型不能用 ePPR 模型精确表示。接下来，我们使用来自初级视觉皮层的生理数据评估 ePPR，并表明它可以从对自然和随机刺激的反应来描述简单和复杂的细胞。对于模拟和生理数据，我们表明 ePPR 与尖峰触发和信息论技术相比具有优势。据我们所知，本文首次证明了一种方法，该方法可以从自然刺激的反应中估计包含多个时空滤波器的视觉细胞的 LN 模型。
