Discrete and continuous compressed information representation using phase transition phenomena

许多研究结果表明，猕猴下颞叶（IT）皮层的邻近神经元的偏好往往是相似的。然而，最近，Tamura等人发现邻近神经元对非相似刺激作出反应，为了解决这一争议，我们提出了IT皮层中信息表示的新观点，该观点采用了IT皮层中信息表示的新观点，该观点将稀疏和局部神经元兴奋考虑在内。由于激励是稀疏的，关于视觉对象的信息似乎是以分布式方式编码的。神经元的局部兴奋与柱结构的经典概念相一致。我们的模型由一个输入层和一个神经场组成，神经场具有来自输入层的传入连接。为了检验本模型的能力，我们在单位球面上使用两个平行环作为输入空间。输入信号具有两个参数。 ...更多信息 一个是θ;它标识输入信号从环的哪个点出现。第二个参数是R2;它表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，只有环上的位置θ使用局部激发的位置连续编码。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，并且局部激励内的放电模式以分布的方式表示输入信号来自哪个环。为了探索这种相变现象，我们采用物理力学方法，特别是复制方法，研究了具有墨西哥帽型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交互作用中的无序性对递归神经网络的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，自旋玻璃和本地化阶段之间的边界。在局部化阶段，网络显示出隆起状活动，这通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，无序的相互作用可以稳定碰撞位置的漂移，这是常见的在传统的侧抑制型神经网络。我们还研究了部分退火框架中的神经元之间的相互作用的变化。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