The effects of spontaneous activity, background noise, and the stimulus ensemble on information transfer in neurons.
Information theory is playing an increasingly important role in the analysis of neural data as it can precisely quantify the reliability of stimulus-response functions. Estimating the mutual information between a neural spike train and a time varying stimulus is, however, not trivial in practice and requires assumptions about the specific computations being performed by the neuron under study. Consequently, estimates of the mutual information depend on these assumptions and their validity must be ascertained in the particular physiological context in which experiments are carried out. Here we compare results obtained using different information measures that make different assumptions about the neural code (i.e. the way information is being encoded and decoded) and the stimulus ensemble (i.e. the set of stimuli that the animal can encounter in nature). Our comparisons are carried out in the context of spontaneously active neurons. However, some of our results are also applicable to neurons that are not spontaneously active. We first show conditions under which a single stimulus provides a good sample of the entire stimulus ensemble. Furthermore, we use a recently introduced information measure that is based on the spontaneous activity of the neuron rather than on the stimulus ensemble. This measure is compared to the Shannon information and it is shown that the two differ only by a constant. This constant is shown to represent the information that the neuron's spontaneous activity transmits about the fact that no stimulus is present in the animal's environment. As a consequence, the mutual information measure based on spontaneous activity is easily applied to stimuli that mimic those seen in nature, as it does not require a priori knowledge of the stimulus ensemble. Finally, we consider the effect of noise in the animal's environment on information transmission about sensory stimuli. Our results show that, as expected, such 'background' noise will increase the trial-to-trial variability of the neural response to repeated presentations of a stimulus. However, the same background noise can also increase the variability of the spike train and hence can lead to increased information transfer in the presence of background noise. Our study emphasizes how different assumptions can lead to different predictions for the information transmission of a neuron. Assumptions about the computations being performed by the system under study as well as the stimulus ensemble and background noise should therefore be carefully considered when applying information theory.
Chacron
Maurice J
MJ
Department of Physics, University of Ottawa, 451 Smyth Road, Ottawa, ON K1H-8M5, Canada. mchacron@physics.uottawa.ca
Longtin
André
A
Maler
Leonard
L
D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D019277:Entropy; D007257:Information Theory; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D009622:Noise; D010812:Physical Stimulation; D009435:Synaptic Transmission; D013997:Time Factors