将皮层网络简化为Potts模型，可以估计存储容量
Reducing a cortical network to a Potts model yields storage capacity estimates
摘要：
一个自联想网络的波茨单位，通过张量连接耦合，已被提出和分析作为一个有效的模型，广泛的皮层网络与不同的短期和长期的突触连接，但它还没有澄清在什么意义上，它可以被视为一个有效的模型。我们在这里画出两者之间的对应关系，这表明需要在简化模型中引入局部反馈项，即，在波茨网络。一个有效的模型允许相变的研究。作为一个例子，我们研究的Potts网络的存储容量与这个额外的长期，本地反馈$w$，这有助于推动网络的活动对存储的模式之一。使用副本工具执行的存储容量计算仅限于完全连接的网络，可以定义Hamilton。为了将结果扩展到中间部分连接的情况下，我们还推导出自洽的信号噪声分析的波茨网络，最后我们讨论了人类的语义记忆的影响。
英文摘要：
An autoassociative network of Potts units, coupled via tensor connections, has been proposed and analysed as an effective model of an extensive cortical network with distinct short- and long-range synaptic connections, but it has not been clarified in what sense it can be regarded as an effective model. We draw here the correspondence between the two, which indicates the need to introduce a local feedback term in the reduced model, i.e., in the Potts network. An effective model allows the study of phase transitions. As an example, we study the storage capacity of the Potts network with this additional term, the local feedback $w$, which contributes to drive the activity of the network towards one of the stored patterns. The storage capacity calculation, performed using replica tools, is limited to fully connected networks, for which a Hamiltonian can be defined. To extend the results to the case of intermediate partial connectivity, we also derive the self-consistent signal-to-noise analysis for the Potts network; and finally we discuss implications for semantic memory in humans.
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