A Spiking Neuron and Population Model Based on the Growth Transform Dynamical System
A Spiking Neuron and Population Model Based on the Growth Transform Dynamical System
复制标题
基于生长变换动力系统的神经元放电与种群模型
DOI：
10.3389/fnins.2020.00425
复制
发表时间：
2020-05-12
影响因子：
4.3
通讯作者：
Chakrabartty, Shantanu
中科院分区：
文献类型：
--
作者：
Gangopadhyay, Ahana;Mehta, Darshit;Chakrabartty, Shantanu
在神经形态工程中，神经群体通常以自下而上的方式建模，其中单个神经元模型通过突触连接以形成大规模尖峰网络。或者，自上而下的方法处理的过程中的尖峰生成和神经表示的兴奋的背景下，最大限度地减少一些措施的网络能量。然而，这些方法通常根据尖峰活动的一些统计测量来定义能量泛函（例如，放电率），这不允许独立控制和优化神经动力学参数。在本文中，我们介绍了一个新的尖峰神经元和人口模型，神经元的动态和尖峰响应可以直接从网络目标或能量泛函的连续值的神经变量，如膜电位。该模型的主要优点是它允许对三个神经动力学特性进行独立控制：（a）对编码精确网络能量泛函的最小值的稳态种群动力学进行控制;（B）对网络中单个神经元产生的动作电位的形状进行控制而不影响网络最小值;以及（c）在不影响网络最小值或动作电位形状的情况下控制尖峰统计和瞬时群体动态。在所提出的模型的核心是不同的变体的生长变换动力系统，产生稳定的和可解释的人口动态，无论网络的大小和类型的神经元连接（抑制或兴奋）。在本文中，我们提出了几个例子，其中所提出的模型已被配置为产生不同类型的单神经元动力学以及种群动力学。在一个这样的示例中，网络被示出为适应，使得其在收敛到最优解时使用减少数量的尖峰来编码稳态解。在本文中，我们使用这个网络来构建一个尖峰联想记忆，使用更少的尖峰相比，传统的架构，同时保持高召回精度在高内存负载。
In neuromorphic engineering, neural populations are generally modeled in a bottom-up manner, where individual neuron models are connected through synapses to form large-scale spiking networks. Alternatively, a top-down approach treats the process of spike generation and neural representation of excitation in the context of minimizing some measure of network energy. However, these approaches usually define the energy functional in terms of some statistical measure of spiking activity (ex. firing rates), which does not allow independent control and optimization of neurodynamical parameters. In this paper, we introduce a new spiking neuron and population model where the dynamical and spiking responses of neurons can be derived directly from a network objective or energy functional of continuous-valued neural variables like the membrane potential. The key advantage of the model is that it allows for independent control over three neuro-dynamical properties: (a) control over the steady-state population dynamics that encodes the minimum of an exact network energy functional; (b) control over the shape of the action potentials generated by individual neurons in the network without affecting the network minimum; and (c) control over spiking statistics and transient population dynamics without affecting the network minimum or the shape of action potentials. At the core of the proposed model are different variants of Growth Transform dynamical systems that produce stable and interpretable population dynamics, irrespective of the network size and the type of neuronal connectivity (inhibitory or excitatory). In this paper, we present several examples where the proposed model has been configured to produce different types of single-neuron dynamics as well as population dynamics. In one such example, the network is shown to adapt such that it encodes the steady-state solution using a reduced number of spikes upon convergence to the optimal solution. In this paper, we use this network to construct a spiking associative memory that uses fewer spikes compared to conventional architectures, while maintaining high recall accuracy at high memory loads.