Robust information propagation through noisy neural circuits

Abstract

Sensory neurons give highly variable responses to stimulation, which can limit the amount of stimulus information available to downstream circuits. Much work has investigated the factors that affect the amount of information encoded in these population responses, leading to insights about the role of covariability among neurons, tuning curve shape, etc. However, the informativeness of neural responses is not the only relevant feature of population codes; of potentially equal importance is how robustly that information propagates to downstream structures. For instance, to quantify the retina's performance, one must consider not only the informativeness of the optic nerve responses, but also the amount of information that survives the spike-generating nonlinearity and noise corruption in the next stage of processing, the lateral geniculate nucleus. Our study identifies the set of covariance structures for the upstream cells that optimize the ability of information to propagate through noisy, nonlinear circuits. Within this optimal family are covariances with "differential correlations", which are known to reduce the information encoded in neural population activities. Thus, covariance structures that maximize information in neural population codes, and those that maximize the ability of this information to propagate, can be very different.

References in corpus (4)

• Network information and connected correlations
• The sign rule and beyond: Boundary effects, flexibility, and noise correlations in neural population codes
• Input nonlinearities can shape beyond-pairwise correlations and improve information transmission by neural populations
• Role of correlations in population coding

Cited by in corpus (6)

• The physics of brain network structure, function, and control
• Linear Classification of Neural Manifolds with Correlated Variability
• State Dependence of Stimulus-Induced Variability Tuning in Macaque MT
• Neuromodulation Influences Synchronization and Intrinsic Read-out
• How the brain might work: statistics flowing in redundant population codes
• Applications of information geometry to spiking neural network behavior