Discrete and continuous compressed information representation using phase transition phenomena

许多发现表明，猕猴下颞叶(IT)皮质中相邻神经元的偏好往往是相似的。然而，最近，Tamura等人。发现相邻神经元对非相似刺激的反应解决了这一争议，我们提出了一种新的IT皮层信息表征的观点，该观点考虑了稀疏和局部神经元兴奋。由于激励是稀疏的，所以关于视觉对象的信息似乎以分布式方式编码。神经元的局部兴奋与柱状结构的经典概念一致。我们的模型由输入层和具有来自输入层的传入连接的神经场组成。通过Hebbian学习来学习传入连接，为了检验该模型的能力，我们采用单位球面上的两个平行环作为输入空间。输入信号有两个参数。…更多的是θ；它识别输入信号从振铃的哪个点出现。第二个参数是ξ；它指示输入信号属于哪个环。这意味着输入空间包含离散信息e和连续信息B。我们定义D为两个环之间的距离。当距离D较小时，仅使用局部激励位置对环上的位置θ进行连续编码。另一方面，当距离D较大时，局部激励的重心继续表示环上的位置θ，并且局部激励内的激发模式以输入信号所来自的环的分布方式表示ξ。为了探索这种相变现象，我们利用统计力学方法，特别是复制方法，研究了具有墨西哥帽子和自旋玻璃型相互作用的神经网络模型。本文利用复制方法研究了墨西哥帽型相互作用中的无序对递归神经网络合作和不合作行为的影响。虽然神经元之间的相互作用依赖于距离，但我们的模型可以解析求解。分叉分析确定了顺磁、铁磁、自旋玻璃和定域相之间的边界。在局部化阶段，该网络呈现凹凸状活动，常被用作空间工作记忆或视皮层柱状活动的模型。仿真结果表明，无序相互作用可以稳定传统的侧抑制型神经网络中常见的凸起位置漂移。我们还研究了神经元之间相互作用发生变化的部分退火法框架。较少

A number of findings suggest that the preferences of neighboring neurons in the inferior temporal (IT) cortex of macaque monkeys tend to be similar. However, recently However, Tamura, et al. found that neighboring neurons react to non-similar stimuli lb resolve this controversy, we proposed a new view of information representation in the IT cortex that takes a new view of information representation in the IT cortex that takes sparse and local neuronal excitation into account. Since the excitation is sparse, information regarding visual objects seems to be encoded in a distributed manner. The local excitation of neurons coincides with the classical notion of a column structure. Our model consists of an input layer and a nerve field having afferent connections from the input layer. The afferent connections are learned through Hebbian learning.To check abilities of the present model we employed two parallel rings in the unit sphere as an input space. The input signals have two parameters. … More One is θ; which identifies from what point of the ring the input signal emerged. The second parameter is ξ; which indicates which ring the input signal belongs to. This means that the input space contains the discrete information e and the continuous information B. We define D as distance between the two rings. When the distance D is small, only the position θ on the rings is continuously encoded using the position of local excitation. On the other hand, when the distance D is large, the center of gravity of the local excitation continues to represent the position θ on the rings, and the firing pattern within the local excitation represents ξ in a distributed manner which ring the input signal comes from. It means that there is phase transition regarding the information representationIn order to explore this phase transition phenomenon, we investigated neural network models with mexican-hat and spin glass type interaction using the statistical-mechanical methods, especially the replica method. Here we study the influence of disorder in the mexican-hat type interactions on the cooperative and uncooperative behavior of recurrent neural networks by using the replica method. Although the interaction between neurons has a dependency on distance, our model can be solved analytically. Bifurcation analysis identifies the boundaries between paramagnetic, ferromagnetic, spin-glass, and localized phases. In the localized phase, the network shows a bump like activity, which is often used as a model of spatial working memory or columnar activity in the visual cortex. Simulation results show that disordered interactions can stabilize the drift the of bump position, which is commonly observed in conventional lateral inhibition type neural networks. We also investigated the partial annealing framework in which the interaction between neurons changes. Less