1b), such that the presence of correlated noise alone does not predict its impact. In general, depending on their structure, noise correlations can either improve or limit the amount of information (Fig. If, however, the trial-to-trial variations in spiking are shared across neurons-what are referred to as “noise correlations”-the situation is different. This corresponds to the first scenario in which information is spread evenly across neurons. If the noise is independent across neurons, it can be averaged out by pooling across neurons 10, and total information would on average increase by the same amount with every neuron added to this pool (Fig. Due to the variability in neural responses to repetitions of the same stimulus, each neuron’s response provides limited information about the stimulus feature 5, 6, 7, 8, 9. For information that can be extracted by a linear decoder, which is the information we focus on in this work, it depends on the neurons’ tuning curves, as well as how their activity varies across repetitions of the same stimulus (i.e., “noise”) 1, 2, 3, 4. 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 sensory information is distributed across neuronal populations even within a single brain area. Therefore, knowing how the brain encodes sensory 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 in the activity of large neural populations. These findings suggest that the brain uses a widely distributed, but nonetheless redundant code that supports recovering most sensory information from smaller subpopulations. We predict that tens of thousands of neurons encode 95% of the information about visual stimulus direction, much less than the number of neurons in primary visual cortex. We compartmentalized noise correlations into information-limiting and nonlimiting components, then extrapolate to predict how information grows with even larger neural populations. We show that information scales sublinearly due to correlated noise in these populations. Here we investigate how sensory information about the direction of a moving visual stimulus is distributed across hundreds of simultaneously recorded neurons in mouse primary visual cortex. Alternatively, the neural code might be highly redundant, meaning that total information saturates. How is information distributed across large neuronal populations within a given brain area? Information may be distributed roughly evenly across neuronal populations, so that total information scales linearly with the number of recorded neurons.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |