Version #1: 4 pages, 5 figures, manuscript submitted to Biological Cybernetics. Version #2 (this version): added 3 pages of new text with additional analytical and numerical calculations, 2 more figures, 11 more references, added Discussion section
发表信息：
Eur. Phys. J. B 65, 577-584 (2008); Eur. Phys. J. B 69, 313 (2009)
The binding neuron model is inspired by numerical simulation of Hodgkin-Huxley-type point neuron, as well as by the leaky integrate-and-fire model. In the binding neuron, the trace of an input is remembered for a fixed period of time after which it disappears completely. This is in the contrast with the above two models, where the postsynaptic potentials decay exponentially and can be forgotten only after triggering. The finiteness of memory in the binding neuron allows one to construct fast recurrent networks for computer modeling. Recently, the finiteness is utilized for exact mathematical description of the output stochastic process if the binding neuron is driven with the Poissonian input stream. In this paper, the simplest networking is considered for binding neuron. Namely, it is expected that every output spike of single neuron is immediately fed into its input. For this construction, externally fed with Poissonian stream, the output stream is characterized in terms of interspike interval probability density distribution if the binding neuron has threshold 2. For higher thresholds, the distribution is calculated numerically. The distributions are compared with those found for binding neuron without feedback, and for leaky integrator. Sample distributions for leaky integrator with feedback are calculated numerically as well. It is oncluded that even the simplest networking can radically alter spikng statistics. Information condensation at the level of single neuron is discussed.
The Gabor-Einstein Wavelet: A Model for the Receptive Fields of V1 to MT Neurons
Quantum Lucid Research Laboratories（Quantum Lucid研究实验室）；Howard University Hospital（霍华德大学医院）
我们的视觉系统在检测移动物体方面非常有效。这一过程由连接初级视觉皮层（V1）和中颞区（MT）的神经元介导。有趣的是，自从Kuffler对视网膜神经节细胞进行开创性实验以来，数学模型对于促进我们对视觉神经元感受野的理解至关重要。然而，现有的模型并没有被设计来描述V1到MT运动处理流中高度专业化的神经元的最显著属性;并且它们也无法做到这一点。在这里，我们介绍了Gabor-Einstein小波，一个新的家庭的功能代表感受野的V1 MT神经元。我们发现，空间和时间在视觉皮层中的混合方式类似于它们在狭义相对论（STR）中的混合方式。因此，我们通过要求：（i）波载波的相对论不变性，以及（ii）最小可能的参数数量来约束Gabor-Einstein模型。从这两个约束，sinc函数作为载波的自然描述符出现。V1到MT神经元的低通到带通时间频率滤波特性的特定分布（Foster et al 1985; DeAngelis et al 1993 b; Hawken et al 1996）可以通过Gabor-Einstein基础清楚地解释。此外，它这样做的方式天生代表运动处理流的神经元层次。我们的分析和计算机模拟表明，沿运动处理流的时间频率滤波特性的分布是大脑联合编码空间和时间的方式的直接影响。我们通过证明STR和联合皮层时空编码的相似数学结构揭示了这一基本联系。这种联系将为大脑如何表达视觉信息提供新的生理…
Noninvasive precision modulation of high-level neural population activity via natural vision perturbations
McGovern Institute for Brain Research, Dept. of Brain and Cognitive Sciences, Massachusetts Institute of Technology（麦戈文脑科学研究所，脑科学与认知科学系，麻省理工学院）；Center for Brains, Minds, and Machines, MIT（脑、心智与机器中心，麻省理工学院）；MIT Quest for Intelligence（麻省理工学院智能探索计划）
