论文标题
戴尔网络的组合代码和图形规则
The combinatorial code and the graph rules of Dale networks
论文作者
论文摘要
我们描述了满足戴尔定律的阈值线性网络中神经元的平衡和稳态的组合。戴尔网络的组合代码的特征是两个条件：（i）网络连接图上的条件，以及（ii）突触矩阵上的光谱条件。我们发现，在弱耦合方案中，组合代码仅取决于连接图，而不取决于突触强度的细节。此外，我们证明了弱耦合网络的组合代码是sublattice，我们为在弱耦合的兴奋性网络中编码sublattice提供了学习规则。在强大的耦合方案中，我们证明了通用dale网络的组合代码是相交组的，因此是凸代码，这在大脑的某些感觉系统中很常见。
We describe the combinatorics of equilibria and steady states of neurons in threshold-linear networks that satisfy Dale's law. The combinatorial code of a Dale network is characterized in terms of two conditions: (i) a condition on the network connectivity graph, and (ii) a spectral condition on the synaptic matrix. We find that in the weak coupling regime the combinatorial code depends only on the connectivity graph, and not on the particulars of the synaptic strengths. Moreover, we prove that the combinatorial code of a weakly coupled network is a sublattice, and we provide a learning rule for encoding a sublattice in a weakly coupled excitatory network. In the strong coupling regime we prove that the combinatorial code of a generic Dale network is intersection-complete and is therefore a convex code, as is common in some sensory systems in the brain.
