Abstract
The amount of information that differentially correlated spikes in a neural ensemble carry is not the same; the information of different types of spikes is associated with different features of the stimulus. By calculating a neural ensemble’s information in response to a mixed stimulus comprising slow and fast signals, we show that the entropy of synchronous and asynchronous spikes are different, and their probability distributions are distinctively separable. We further show that these spikes carry a different amount of information. We propose a time-varying entropy (TVE) measure to track the dynamics of a neural code in an ensemble of neurons at each time bin. By applying the TVE to a multiplexed code, we show that synchronous and asynchronous spikes carry information in different time scales. Finally, a decoder based on the Kalman filtering approach is developed to reconstruct the stimulus from the spikes. We demonstrate that slow and fast features of the stimulus can be entirely reconstructed when this decoder is applied to asynchronous and synchronous spikes, respectively. The significance of this work is that the TVE can identify different types of information (for example, corresponding to synchronous and asynchronous spikes) that might simultaneously exist in a neural code.
Highlights
The collective responses of primary sensory neurons constitute fully or partly mixed inputs to cortical neurons; multiple features of the stimulus are to be reliably coded by cortical neurons.The brain uses different coding strategies to represent information underlying those features.Information can be encoded by either the rate of spikes in a relatively long time window—rate code—or by their precise timing—temporal code [1,2,3,4,5,6,7,8,9]
Information is mostly carried by groups of neurons that fire nearly simultaneously [9,10] whereas, in the rate coding, the precise timing of spikes is compromised and information across neurons is mostly carried by the rate of asynchronous spikes [12,13,14,15]
For each word between the probability distributions of these spikes [27,28], i.e., I (A; S), where S and A are random pattern, we considered bins, whereof synchronous is the lengthand of the word pattern, to construct histograms variables drawn from the2distributions asynchronous spikes, respectively
Summary
The collective responses of primary sensory neurons constitute fully or partly mixed inputs to cortical neurons; multiple features of the stimulus are to be reliably coded by cortical neurons. It is challenging to uncover the distinct roles of differentially correlated spikes—i.e., asynchronous spikes (rate code) and synchronous spikes (temporal code)—in a multiplexed code To address this challenge, it is crucial to measure the information underlying different types of spikes [16]. Inspired by [19], we consider spikes as code words with different lengths and time resolutions, and calculate the entropy across homogeneous neurons in a neural ensemble. In this way, we estimate the entropy of spikes at each time bin and show how it varies in different time resolutions, which correspond to different features of the stimulus. Our results indicate that information underlying synchronous and asynchronous spikes is different and associated with distinct features of the stimulus
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