Article

Multidimensional encoding strategy of spiking neurons.

Institut für Theoretische Physik, Universität Bremen, Germany.
Neural Computation (impact factor: 1.88). 08/2000; 12(7):1519-29. pp.1519-29
Source: PubMed

ABSTRACT Neural responses in sensory systems are typically triggered by a multitude of stimulus features. Using information theory, we study the encoding accuracy of a population of stochastically spiking neurons characterized by different tuning widths for the different features. The optimal encoding strategy for representing one feature most accurately consists of narrow tuning in the dimension to be encoded, to increase the single-neuron Fisher information, and broad tuning in all other dimensions, to increase the number of active neurons. Extremely narrow tuning without sufficient receptive field overlap will severely worsen the coding. This implies the existence of an optimal tuning width for the feature to be encoded. Empirically, only a subset of all stimulus features will normally be accessible. In this case, relative encoding errors can be calculated that yield a criterion for the function of a neural population based on the measured tuning curves.

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Keywords

active neurons
 
broad tuning
 
different tuning widths
 
dimensions
 
encoding accuracy
 
information theory
 
measured tuning curves
 
narrow tuning
 
neural population
 
optimal encoding strategy
 
optimal tuning width
 
relative encoding errors
 
sensory systems
 
single-neuron Fisher information
 
stimulus features
 
stochastically spiking neurons
 
sufficient receptive field overlap
 

C. W. Eurich