Predictive coding of dynamical variables in balanced spiking networks.
Predictive coding of dynamical variables in balanced spiking networks.
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DOI：
10.1371/journal.pcbi.1003258
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发表时间：
2013
影响因子：
4.3
通讯作者：
Denève S
中科院分区：
文献类型：
--
作者：
Boerlin M;Machens CK;Denève S
关于大脑皮层的两个观察结果长期困扰着神经科学家。首先，神经反应是高度可变的。第二，每个神经元接受的兴奋和抑制水平在任何时候都是紧密平衡的。在这里，我们证明了这两个属性是神经网络在尖峰信号中有效表示信息的必要结果。我们用代表动态变量的尖峰网络来说明这一观点。我们的方法基于两个假设：我们假设动态变量的信息可以线性地从神经尖峰序列中读出，我们假设神经元只会发射尖峰，如果这改善了动态变量的表示。基于这些假设，我们推导出一个网络的泄漏积分和消防神经元，能够实现任意线性动力系统。我们表明，神经元的膜电压相当于一个共同的人口水平的信号的预测误差。除此之外，我们的方法使我们能够构建一个积分器网络的尖峰神经元，对许多扰动是强大的。最重要的是，我们网络中的神经变异性不能等同于噪声。尽管表现出与广泛使用的群体代码模型相同的单个单元属性（例如调谐曲线，泊松分布尖峰序列），但平衡网络的可靠性要高出几个数量级。我们的方法表明，在考虑大脑如何计算时，尖峰确实很重要，并且皮质表示的可靠性可能被严重低估。关于大脑皮层的两个观察结果长期以来一直困扰着神经科学家。首先，神经反应是高度可变的。第二，每个神经元接受的兴奋和抑制水平在任何时候都是紧密平衡的。在这里，我们证明了这两个属性是神经网络可靠地表示信息和少量尖峰的必要后果。为了达到这样的效率，单个神经元的尖峰必须传达关于共同群体水平信号的预测误差，自动导致平衡的兴奋和抑制以及高度可变的神经反应。我们说明我们的方法，重点是线性动力系统的实现。除此之外，这使我们能够构建一个尖峰神经元网络，它可以整合输入信号，但对许多扰动具有鲁棒性。最重要的是，我们的方法表明，神经变异性不能等同于噪声。尽管表现出与其他广泛使用的网络模型相同的单个单元属性，但我们的平衡网络的可靠性要高出几个数量级。我们的研究结果表明，皮质表征的精确性被严重低估了。
Two observations about the cortex have puzzled neuroscientists for a long time. First, neural responses are highly variable. Second, the level of excitation and inhibition received by each neuron is tightly balanced at all times. Here, we demonstrate that both properties are necessary consequences of neural networks that represent information efficiently in their spikes. We illustrate this insight with spiking networks that represent dynamical variables. Our approach is based on two assumptions: We assume that information about dynamical variables can be read out linearly from neural spike trains, and we assume that neurons only fire a spike if that improves the representation of the dynamical variables. Based on these assumptions, we derive a network of leaky integrate-and-fire neurons that is able to implement arbitrary linear dynamical systems. We show that the membrane voltage of the neurons is equivalent to a prediction error about a common population-level signal. Among other things, our approach allows us to construct an integrator network of spiking neurons that is robust against many perturbations. Most importantly, neural variability in our networks cannot be equated to noise. Despite exhibiting the same single unit properties as widely used population code models (e.g. tuning curves, Poisson distributed spike trains), balanced networks are orders of magnitudes more reliable. Our approach suggests that spikes do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly underestimated. Two observations about the cortex have puzzled and fascinated neuroscientists for a long time. First, neural responses are highly variable. Second, the level of excitation and inhibition received by each neuron is tightly balanced at all times. Here, we demonstrate that both properties are necessary consequences of neural networks representing information reliably and with a small number of spikes. To achieve such efficiency, spikes of individual neurons must communicate prediction errors about a common population-level signal, automatically resulting in balanced excitation and inhibition and highly variable neural responses. We illustrate our approach by focusing on the implementation of linear dynamical systems. Among other things, this allows us to construct a network of spiking neurons that can integrate input signals, yet is robust against many perturbations. Most importantly, our approach shows that neural variability cannot be equated to noise. Despite exhibiting the same single unit properties as other widely used network models, our balanced networks are orders of magnitudes more reliable. Our results suggest that the precision of cortical representations has been strongly underestimated.
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影响因子：
25
作者：
Lim, Sukbin;Goldman, Mark S.
通讯作者：
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影响因子：
4.3
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作者：
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