Statistically efficient estimation using population coding
Neural Computation
Probabilistic interpretation of population codes
Neural Computation
Neuronal tuning: to sharpen or broaden
Neural Computation
Narrow versus wide turning curves: what's best for a population code?
Neural Computation
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Efficient Coding of Time-Relative Structure Using Spikes
Neural Computation
Dynamic Analyses of Information Encoding in Neural Ensembles
Neural Computation
Population Coding with Correlation and an Unfaithful Model
Neural Computation
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Neural Computation
Infinite-horizon policy-gradient estimation
Journal of Artificial Intelligence Research
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Naturally occurring sensory stimuli are dynamic. In this letter, we consider how spiking neural populations might transmit information about continuous dynamic stimulus variables. The combination of simple encoders and temporal stimulus correlations leads to a code in which information is not readily available to downstream neurons. Here, we explore a complex encoder that is paired with a simple decoder that allows representation and manipulation of the dynamic information in neural systems. The encoder we present takes the form of a biologically plausible recurrent spiking neural network where the output population recodes its inputs to produce spikes that are independently decodeable. We show that this network can be learned in a supervised manner by a simple local learning rule.