Elements of information theory
Elements of information theory
The effect of correlated variability on the accuracy of a population code
Neural Computation
Spikes: exploring the neural code
Spikes: exploring the neural code
An Information-Theoretic Approach to Deciphering the Hippocampal Code
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Neural Computation
The Neurophysiology of Backward Visual Masking: Information Analysis
Journal of Cognitive Neuroscience
Information-geometric measure for neural spikes
Neural Computation
Synchronisation, binding, and the role of correlated firing in fast information transmission
Emergent neural computational architectures based on neuroscience
Synchronisation, Binding, and the Role of Correlated Firing in Fast Information Transmission
Emergent Neural Computational Architectures Based on Neuroscience - Towards Neuroscience-Inspired Computing
Tree-structured neural decoding
The Journal of Machine Learning Research
Dynamic switching of neural codes in networks with gap junctions
Neural Networks
Information theory and neural information processing
IEEE Transactions on Information Theory - Special issue on information theory in molecular biology and neuroscience
The Ising decoder: reading out the activity of large neural ensembles
Journal of Computational Neuroscience
Differential entropy of multivariate neural spike trains
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
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We demonstrate that the information contained in the spike occurrence times of a population of neurons can be broken up into a series of terms, each reflecting something about potential coding mechanisms. This is possible in the coding regime in which few spikes are emitted in the relevant time window. This approach allows us to study the additional information contributed by spike timing beyond that present in the spike counts and to examine the contributions to the whole information of different statistical properties of spike trains, such as firing rates and correlation functions. It thus forms the basis for a new quantitative procedure for analyzing simultaneous multiple neuron recordings and provides theoretical constraints on neural coding strategies. We find a transition between two coding regimes, depending on the size of the relevant observation timescale. For time windows shorter than the timescale of the stimulus-induced response fluctuations, there exists a spike count coding phase, in which the purely temporal information is of third order in time. For time windows much longer than the characteristic timescale, there can be additional timing information of first order, leading to a temporal coding phase in which timing information may affect the instantaneous information rate. In this new framework, we study the relative contributions of the dynamic firing rate and correlation variables to the full temporal information, the interaction of signal and noise correlations in temporal coding, synergy between spikes and between cells, and the effect of refractoriness. We illustrate the utility of the technique by analyzing a few cells from the rat barrel cortex.