Neuronal Circuits Underlying Persistent Representations Despite Time Varying Activity
Neuronal Circuits Underlying Persistent Representations Despite Time Varying Activity
复制标题
DOI：
10.1016/j.cub.2012.08.058
复制
发表时间：
2012-11-20
期刊：
影响因子：
9.2
通讯作者：
Chklovskii, Dmitri B.
中科院分区：
文献类型：
--
作者：
Druckmann, Shaul;Chklovskii, Dmitri B.
背景：尽管神经活动随时间变化，但我们的大脑能够非常稳定地表征刺激。例如，在工作记忆任务的延迟期间，当刺激在工作记忆中表示时，被认为支持记忆表示的前额叶皮层中的神经元表现出随时间变化的神经元活动。由于神经元活动编码的刺激，其随时间变化的动力学似乎是矛盾的，不符合稳定的网络刺激表示。事实上，这一发现提出了一个根本性的问题：稳定的表征只能用稳定的神经活动来编码吗？或者，它的推论是，活动的每一个变化都是刺激表征变化的标志吗？结果如下：在这里，我们解释了如何由单个神经元提供的不同的时变表示可以编织在一起，形成一个连贯的，时不变的，表示。出于两个无处不在的新皮层的功能冗余的神经代表和稀疏的皮层内连接，我们得出一个网络架构，解决了代表稳定性和不断变化的神经活动之间的明显矛盾。出乎意料的是，这种网络结构表现出许多在皮层感觉区测量的结构特性。特别地，我们可以解释少数神经元基序、突触权重分布以及神经元功能特性与连接概率之间的关系。我们表明，关于网络刺激表示的直觉，通常来自考虑单个神经元，可能是误导性的，皮层回路中分布式表征的时变活动并不一定意味着网络明确地编码时间-不同的属性。
Background: Our brains are capable of remarkably stable stimulus representations despite time-varying neural activity. For instance, during delay periods in working memory tasks, while stimuli are represented in working memory, neurons in the prefrontal cortex, thought to support the memory representation, exhibit time-varying neuronal activity. Since neuronal activity encodes the stimulus, its time-varying dynamics appears to be paradoxical and incompatible with stable network stimulus representations. Indeed, this finding raises a fundamental question: can stable representations only be encoded with stable neural activity, or, its corollary, is every change in activity a sign of change in stimulus representation?Results: Here we explain how different time-varying representations offered by individual neurons can be woven together to form a coherent, time-invariant, representation. Motivated by two ubiquitous features of the neocortex-redundancy of neural representation and sparse intracortical connections-we derive a network architecture that resolves the apparent contradiction between representation stability and changing neural activity. Unexpectedly, this network architecture exhibits many structural properties that have been measured in cortical sensory areas. In particular, we can account for few-neuron motifs, synapse weight distribution, and the relations between neuronal functional properties and connection probability.Conclusions: We show that the intuition regarding network stimulus representation, typically derived from considering single neurons, may be misleading and that time-varying activity of distributed representation in cortical circuits does not necessarily imply that the network explicitly encodes time-varying properties.