Why Neurons Have Thousands of Synapses, a Theory of Sequence Memory in Neocortex.
Why Neurons Have Thousands of Synapses, a Theory of Sequence Memory in Neocortex.
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DOI：
10.3389/fncir.2016.00023
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发表时间：
2016
影响因子：
3.5
通讯作者：
Ahmad S
中科院分区：
文献类型：
--
作者：
Hawkins J;Ahmad S
锥体神经元代表新皮层中的大多数兴奋性神经元。每一个锥体神经元都从数千个分离到树突分支上的兴奋性突触接收输入。树突本身被分离成顶端、基底和近端整合区，这些整合区具有不同的性质。锥体神经元如何整合来自数千个突触的输入，不同的树突在这种整合中扮演什么角色，以及这在皮层组织中实现了什么样的网络行为，这是一个谜。以前有人提出，树突的非线性特性使皮层神经元能够识别多个独立的模式。在本文中，我们以多种方式扩展了这一想法。首先，我们表明，一个神经元与数千个突触分离的活跃树突可以识别数百个独立的模式的细胞活动，即使在大量的噪音和模式的变化。然后，我们提出了一个神经元模型，在近端树突上检测到的模式导致动作电位，定义了神经元的经典感受野，在基底和顶端树突上检测到的模式通过轻微去极化神经元而不产生动作电位来作为预测。通过这种机制，神经元可以在数百个独立的环境中预测其激活。然后，我们提出了一个基于神经元的网络模型，该模型具有学习基于时间的序列的这些属性。该网络依赖于快速局部抑制来优先激活轻微去极化的神经元。通过仿真，我们表明，只要网络使用稀疏的分布式蜂窝激活代码，该网络就可以在很大的参数范围内良好地扩展并稳健运行。我们将新网络模型的属性与其他几种神经网络模型进行对比，以说明每个模型的相对功能。我们的结论是，锥体神经元与数以千计的突触，活跃的树突，和多个整合区创建一个强大的和强大的序列记忆。鉴于兴奋性神经元在整个新皮层中的普遍性和相似性，以及序列记忆在推理和行为中的重要性，我们认为这种形式的序列记忆可能是新皮层组织的普遍属性。
Pyramidal neurons represent the majority of excitatory neurons in the neocortex. Each pyramidal neuron receives input from thousands of excitatory synapses that are segregated onto dendritic branches. The dendrites themselves are segregated into apical, basal, and proximal integration zones, which have different properties. It is a mystery how pyramidal neurons integrate the input from thousands of synapses, what role the different dendrites play in this integration, and what kind of network behavior this enables in cortical tissue. It has been previously proposed that non-linear properties of dendrites enable cortical neurons to recognize multiple independent patterns. In this paper we extend this idea in multiple ways. First we show that a neuron with several thousand synapses segregated on active dendrites can recognize hundreds of independent patterns of cellular activity even in the presence of large amounts of noise and pattern variation. We then propose a neuron model where patterns detected on proximal dendrites lead to action potentials, defining the classic receptive field of the neuron, and patterns detected on basal and apical dendrites act as predictions by slightly depolarizing the neuron without generating an action potential. By this mechanism, a neuron can predict its activation in hundreds of independent contexts. We then present a network model based on neurons with these properties that learns time-based sequences. The network relies on fast local inhibition to preferentially activate neurons that are slightly depolarized. Through simulation we show that the network scales well and operates robustly over a wide range of parameters as long as the network uses a sparse distributed code of cellular activations. We contrast the properties of the new network model with several other neural network models to illustrate the relative capabilities of each. We conclude that pyramidal neurons with thousands of synapses, active dendrites, and multiple integration zones create a robust and powerful sequence memory. Given the prevalence and similarity of excitatory neurons throughout the neocortex and the importance of sequence memory in inference and behavior, we propose that this form of sequence memory may be a universal property of neocortical tissue.