Learning to make external sensory stimulus predictions using internal correlations in populations of neurons
Learning to make external sensory stimulus predictions using internal correlations in populations of neurons
复制标题
DOI：
10.1073/pnas.1710779115
复制
发表时间：
2018-01-30
影响因子：
11.1
通讯作者：
Palmer, Stephanie E.
中科院分区：
文献类型：
--
作者：
Sederberg, Audrey J.;MacLean, Jason N.;Palmer, Stephanie E.
为了弥补感官处理延迟，视觉系统必须做出预测，以确保及时和适当的行为。最近的研究发现，从视网膜开始，在视觉处理的早期，神经群体中的刺激具有预测性信息。然而，为了利用这些信息，下游细胞必须能够从视网膜神经节细胞的尖峰活动中读出预测信息。在这里，我们调查是否下游细胞可以学习有效的编码的预测信息在其输入中的相关性的输入本身，在没有其他的指导性信号。我们模拟由蝾螈视网膜中记录的尖峰活动驱动的学习。我们将下游细胞建模为接收一小组加权输入的二进制神经元，并量化二进制神经元中的活动与未来输入之间的预测信息。在训练期间，输入权重根据尖峰时间相关的学习规则而变化。我们的特点下学习的读数尖峰时序依赖的突触更新规则，发现虽然学习动态的固定点是不相关的绝对最佳读数，他们传达了几乎所有的最佳读数所传达的信息。此外，我们发现，学习感知器传输的位置和速度信息的移动酒吧刺激几乎有效的最佳感知器。我们的结论是，在没有其他输入的情况下，从下游神经元的角度来看，预测信息原则上是可读的。这表明前馈预测在感觉编码中发挥着重要作用。
To compensate for sensory processing delays, the visual system must make predictions to ensure timely and appropriate behaviors. Recent work has found predictive information about the stimulus in neural populations early in vision processing, starting in the retina. However, to utilize this information, cells downstream must be able to read out the predictive information from the spiking activity of retinal ganglion cells. Here we investigate whether a downstream cell could learn efficient encoding of predictive information in its inputs from the correlations in the inputs themselves, in the absence of other instructive signals. We simulate learning driven by spiking activity recorded in salamander retina. We model a downstream cell as a binary neuron receiving a small group of weighted inputs and quantify the predictive information between activity in the binary neuron and future input. Input weights change according to spike timing-dependent learning rules during a training period. We characterize the readouts learned under spike timing-dependent synaptic update rules, finding that although the fixed points of learning dynamics are not associated with absolute optimal readouts they convey nearly all of the information conveyed by the optimal readout. Moreover, we find that learned perceptrons transmit position and velocity information of a moving-bar stimulus nearly as efficiently as optimal perceptrons. We conclude that predictive information is, in principle, readable from the perspective of downstream neurons in the absence of other inputs. This suggests an important role for feedforward prediction in sensory encoding.