Abstract
Neuronal responses to external stimuli vary from trial to trial partly because they depend on continuous spontaneous variations of the state of neural circuits, reflected in variations of ongoing activity prior to stimulus presentation. Understanding how post-stimulus responses relate to the pre-stimulus spontaneous activity is thus important to understand how state dependence affects information processing and neural coding, and how state variations can be discounted to better decode single-trial neural responses. Here we exploited high-resolution CMOS electrode arrays to record simultaneously from thousands of electrodes in in-vitro cultures stimulated at specific sites. We used information-theoretic analyses to study how ongoing activity affects the information that neuronal responses carry about the location of the stimuli. We found that responses exhibited state dependence on the time between the last spontaneous burst and the stimulus presentation and that the dependence could be described with a linear model. Importantly, we found that a small number of selected neurons carry most of the stimulus information and contribute to the state-dependent information gain. This suggests that a major value of large-scale recording is that it individuates the small subset of neurons that carry most information and that benefit the most from knowledge of its state dependence.
Highlights
Can be modelled and discounted in order to improve the performance of algorithms to extract information from neural activity[4]
The features of the neural population responses that we considered were the centre activity trajectory (CAT) in each time bin, which takes into account one prominent aspect of the spatial distribution of the propagating neural activity, and the projections of the neural activity in each time bin along one of the 10 first spatial Principal Components (PC) of the neural activity, a variable that takes into account the spatial structure of the population activity
When considering other population response features such as PCs and CAT, the stimulus information in neural responses and the information gain due to state knowledge (Fig. 6c and d) showed the same qualitative pattern across response features that we found for untreated cultures
Summary
Can be modelled and discounted in order to improve the performance of algorithms to extract information from neural activity[4]. State-dependent information processing has been studied at the level of small neural populations It may involve a wide range of spatiotemporal scales of neural activity that are difficult to simultaneously access experimentally in-vivo. We take advantage of these large-scale in-vitro recordings to investigate the state dependent processing of stimulus information in cultured hippocampal neural networks. We delivered low-frequency trains of electrical stimuli (at 0.2 Hz) to the network from multiple, randomly selected on-chip electrode sites Both ongoing and electrically evoked spiking network activities were acquired from 4096 closely spaced microelectrodes of CMOS-MEAs16. To assess state dependence of neural information processing, we investigated whether knowledge of state variables, defined from measures of ongoing pre-stimulus spiking activity, increased the information that could be extracted from neural responses. By leveraging our access to thousands of simultaneously recording electrodes, we investigated whether the information about a stimulus set can efficiently be decoded when considering the responses of a relatively small group of neurons[21]
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