Synergy in a Neural Code

Authors:
Naama Brenner;Steven P. Strong;Roland P. Koberle;William P. Bialek;Rob R. De Ruyter Van Steveninck
Affiliations:
NEC Research Institute, Princeton, NJ 08540, U.S.A.;Institute for Advanced Study, Princeton, NJ 08544, and NEC Research Institute, Princeton, NJ 08540, U.S.A.;Instituto di Física de São Carlos, Universidade de São Paulo, 13560–970 São Carlos, SP Brasil, and NEC Research Institute, Princeton, NJ 08540, U.S.A.;NEC Research Institute, Princeton, NJ 08540, U.S.A.;NEC Research Institute, Princeton, NJ 08540, U.S.A.
Venue:
Neural Computation
Year:
2000

Citing 2
Cited 28

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]

Attractor reliability reveals deterministic structure in neuronal spike trains

Neural Computation
Information-geometric measure for neural spikes

Neural Computation
Computation in a single Neuron: Hodgkin and Huxley revisited

Neural Computation
What causes a Neuron to spike?

Neural Computation
The evidence for neural information processing with precise spike-times: A survey

Natural Computing: an international journal
Estimating the temporal interval entropy of neuronal discharge

Neural Computation
Enhancement of information transmission efficiency by synaptic failures

Neural Computation
The Costs of Ignoring High-Order Correlations in Populations of Model Neurons

Neural Computation
Estimating Entropy Rates with Bayesian Confidence Intervals

Neural Computation
Quantifying Stimulus Discriminability: A Comparison of Information Theory and Ideal Observer Analysis

Neural Computation
Analyzing Neural Responses to Natural Signals: Maximally Informative Dimensions

Neural Computation
A Unified Approach to the Study of Temporal, Correlational, and Rate Coding

Neural Computation
Estimating Information Rates with Confidence Intervals in Neural Spike Trains

Neural Computation
Representations of Space and Time in the Maximization of Information Flow in the Perception-Action Loop

Neural Computation
Mutual information expansion for studying the role of correlations in population codes: How important are autocorrelations?

Neural Computation
2009 Special Issue: Two forms of feedback inhibition determine the dynamical state of a small hippocampal network

Neural Networks
Information theory and neural information processing

IEEE Transactions on Information Theory - Special issue on information theory in molecular biology and neuroscience
Synergistic coding by cortical neural ensembles

IEEE Transactions on Information Theory - Special issue on information theory in molecular biology and neuroscience
How synaptic release probability shapes neuronal transmission: Information-theoretic analysis in a cerebellar granule cell

Neural Computation
Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers

The Journal of Machine Learning Research
Recording from two neurons: Second-order stimulus reconstruction from spike trains and population coding

Neural Computation
Quantifying neurotransmission reliability through metrics-based information analysis

Neural Computation
Active spike transmission in the neuron model with a winding threshold manifold

Neurocomputing
Characterizing responses of translation-invariant neurons to natural stimuli: Maximally informative invariant dimensions

Neural Computation
Interspike interval based filtering of directional selective retinal ganglion cells spike trains

Computational Intelligence and Neuroscience
Modulation of spike and burst rate in a minimal neuronal circuit with feed-forward inhibition

Neural Networks
Quantifying neurotransmission reliability through metrics-based information analysis

Neural Computation
Synergy, redundancy, and multivariate information measures: an experimentalist's perspective

Journal of Computational Neuroscience

Quantified Score

Hi-index	0.00

Visualization

Abstract

We show that the information carried by compound events in neural spike trains—patterns of spikes across time or across a population of cells— can be measured, independent of assumptions about what these patterns might represent. By comparing the information carried by a compound pattern with the information carried independently by its parts, we directly measure the synergy among these parts. We illustrate the use of these methods by applying them to experiments on the motion-sensitive neuron H1 of the fly’s visual system, where we confirm that two spikes close together in time carry far more than twice the information carried by a single spike. We analyze the sources of this synergy and provide evidence that pairs of spikes close together in time may be especially important patterns in the code of H1.