![]() The amount of information about a stimulus feature that can be extracted from neural population activity depends on how this activity changes with a change in the stimulus feature. Is information spread evenly and largely independently across neurons, or in a way that introduces significant redundancy? In the first scenario, one would need to record from the whole neuronal population to get access to all available information, whereas in the second scenario only a fraction of neurons would be needed. Unfortunately, we still know little about how information is distributed across neuronal populations even within a single brain area. Therefore, knowing how the brain encodes information about the world is necessary if we are to understand the computations it performs. For example, the amount of information in visual cortex about the drift direction of a moving visual stimulus determines how well one could in principle discriminate different drift directions if the brain operates at maximum efficiency, and its format tells us how downstream motion-processing areas ought to “read out” this information. The format of this encoding reveals how downstream brain areas ought to access the encoded information for further processing. The amount of encoded information provides an upper bound on behavioral performance, and so exposes the efficiency and structure of the computations implemented by the brain. Our brains encode information about sensory features and other variables of interest in the activity of large neural populations. Overall, these findings suggest that the brain uses a widely distributed, but nonetheless redundant code that supports recovering most information from smaller subpopulations. We predict that tens of thousands of neurons are required to encode 95% of the information about visual stimulus direction, a number much smaller than the number of neurons in V1. Using recent theoretical advances, we compartmentalized noise correlations into information-limiting and nonlimiting components, and then extrapolated to predict how information grows when neural populations are even larger. We found that information scales sublinearly, due to the presence of correlated noise in these populations. Here we investigated how information about the direction of a moving visual stimulus is distributed across hundreds of simultaneously recorded neurons in mouse primary visual cortex (V1). Alternatively, the neural code might be highly redundant, meaning that total information saturates. Citations provide a way for the developers to justify their continued involvement in the development of the package.How is information distributed across large neuronal populations within a given brain area? One possibility is that information is distributed roughly evenly across neurons, so that total information scales linearly with the number of recorded neurons. Academic assessment (whether for promotion or even getting appointed to a job in the first place) prioritises publications over making useful tools for others. Journal of Neuroscience Methods, 162 (1-2):8-13 doi:10.1016/j.jneumeth.2006.11.017Ĭiting these papers gives the reviewer/reader of your study information about how the system works and it attributes some credit for its original creation. PsychoPy - Psychophysics software in Python. ![]() ![]() Frontiers in Neuroinformatics, 2 (10), 1-8. Generating stimuli for neuroscience using PsychoPy. PsychoPy2: experiments in behavior made easy. R., Höchenberger, R., Sogo, H., Kastman, E., Lindeløv, J. For most people the 2019 paper is probably the most relevant (the papers from 2009, 2007 did not mention Builder at all, for instance). If you use this software, please cite one of the publications that describe it.
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