Precise spatial memory in local random networks
Precise spatial memory in local random networks
复制标题
局部随机网络中的精确空间记忆
DOI：
10.1103/physreve.102.022405
复制
发表时间：
2020
影响因子：
2.4
通讯作者：
Nemenman, Ilya
中科院分区：
文献类型：
--
作者：
Natale, Joseph L.;Hentschel, H. George;Nemenman, Ilya
自我维持的、升高的神经元活动持续在S或更长时间的时间尺度上，被认为对工作记忆的各个方面至关重要，包括大脑对真实空间的表征。连续吸引子神经网络是最著名的持续活动建模框架之一，它已经能够对这种空间记忆的关键方面进行建模。这些模型往往需要高度结构化或规则的突触结构。相比之下，我们研究了一个几何嵌入模型的数值模拟，该模型具有局部但其他随机的连通性轮廓；对我们系统的平均发射速率施加全局规则，产生有效跨越二维流形的局部、精细间隔的离散吸引子。我们演示了吸引状态集如何可靠地编码系统接收外部输入的空间位置的表示，从而在没有突触微调或规则结构的情况下通过吸引子动力学实现空间记忆。然后，我们用数值方法测量了网络的存储容量，发现可检索位置的统计数据也相当于平面的完整平铺，这是迄今为止只有(大约)翻译不变突触才能实现的，这可能对在二维中模拟视觉空间工作记忆等生物现象感兴趣。
Self-sustained, elevated neuronal activity persisting on timescales of 10 s or longer is thought to be vital for aspects of working memory, including brain representations of real space. Continuous-attractor neural networks, one of the most well-known modeling frameworks for persistent activity, have been able to model crucial aspects of such spatial memory. These models tend to require highly structured or regular synaptic architectures. In contrast, we study numerical simulations of a geometrically embedded model with a local, but otherwise random, connectivity profile; imposing a global regulation of our system's mean firing rate produces localized, finely spaced discrete attractors that effectively span a two-dimensional manifold. We demonstrate how the set of attracting states can reliably encode a representation of the spatial locations at which the system receives external input, thereby accomplishing spatial memory via attractor dynamics without synaptic fine-tuning or regular structure. We then measure the network's storage capacity numerically and find that the statistics of retrievable positions are also equivalent to a full tiling of the plane, something hitherto achievable only with (approximately) translationally invariant synapses, and which may be of interest in modeling such biological phenomena as visuospatial working memory in two dimensions.