Selectively grouping neurons in recurrent networks of lateral inhibition
Selectively grouping neurons in recurrent networks of lateral inhibition
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DOI：
10.1162/089976602760408008
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发表时间：
2002-11-01
影响因子：
2.9
通讯作者：
Seung, HS
中科院分区：
文献类型：
--
作者：
Xie, XH;Hahnloser, RHR;Seung, HS
赢家通吃的网络被认为是大脑许多基本计算能力的基础。然而，关于如何将这些网络中的潜在赢家的分组扩展到单个神经元或统一排列的神经元组，人们知之甚少。我们证明了任意组神经元之间的竞争可以通过在线性阈值网络中组织侧抑制来实现。给定一组潜在重叠的组(除了一些退化的情况外)，侧抑制导致网络动态，使得在稳定稳定状态下可以被一些输入共同激活的任何允许的神经元集合包含在其中一个组中。有关输入的信息将在此操作中保留。在允许的范围内，神经元的活动水平与其刺激强度相对应，并被某个常量放大。处于稳定稳定状态的任何输入都不能共同激活不属于一个组的神经元组。我们针对随机组分析了这种网络的存储容量--在不创建太多虚假组的情况下，网络可以存储为允许集的随机组的数量。在这个框架中，我们计算了群的最优稀疏性(最大化群熵)。我们发现对于密集的输入，最优稀疏性是非生理性的小的。然而，当输入和组同样稀疏时，我们得到了更合理的最优稀疏性。我们相信我们的结果是迈向模拟-数字混合网络吸引子理论的第一步。
Winner-take-all networks have been proposed to underlie many of the brain's fundamental computational abilities. However, not much is known about how to extend the grouping of potential winners in these networks beyond single neuron or uniformly arranged groups of neurons. We show that competition between arbitrary groups of neurons can be realized by organizing lateral inhibition in linear threshold networks. Given a collection of potentially overlapping groups (with the exception of some degenerate cases), the lateral inhibition results in network dynamics such that any permitted set of neurons that can be coactivated by some input at a stable steady state is contained in one of the groups. The information about the input is preserved in this operation. The activity level of a neuron in a permitted set corresponds to its stimulus strength, amplified by some constant. Sets of neurons that are not part of a group cannot be coactivated by any input at a stable steady state. We analyze the storage capacity of such a network for random groups-the number of random groups the network can store as permitted sets without creating too many spurious ones. In this framework, we calculate the optimal sparsity of the groups (maximizing group entropy). We find that for dense inputs, the optimal sparsity is unphysiologically small. However, when the inputs and the groups are equally sparse, we derive a more plausible optimal sparsity. We believe our results are the first steps toward attractor theories in hybrid analog-digital networks.