scholarly journals An Information Theoretic Approach to the Contributions of the Firing Rates and the Correlations Between the Firing of Neurons

2003 ◽  
Vol 89 (5) ◽  
pp. 2810-2822 ◽  
Author(s):  
Edmund T. Rolls ◽  
Leonardo Franco ◽  
Nicholas C. Aggelopoulos ◽  
Steven Reece

To analyze the extent to which populations of neurons encode information in the numbers of spikes each neuron emits or in the relative time of firing of the different neurons that might reflect synchronization, we developed and analyzed the performance of an information theoretic approach. The formula quantifies the corrections to the instantaneous information rate that result from correlations in spike emission between pairs of neurons. We showed how these cross-cell terms can be separated from the correlations that occur between the spikes emitted by each neuron, the auto-cell terms in the information rate expansion. We also described a method to test whether the estimate of the amount of information contributed by stimulus-dependent synchronization is significant. With simulated data, we show that the approach can separate information arising from the number of spikes emitted by each neuron from the redundancy that can arise if neurons have common inputs and from the synergy that can arise if cells have stimulus-dependent synchronization. The usefulness of the approach is also demonstrated by showing how it helps to interpret the encoding shown by neurons in the primate inferior temporal visual cortex. When applied to a sample dataset of simultaneously recorded inferior temporal cortex neurons, the algorithm showed that most of the information is available in the number of spikes emitted by each cell; that there is typically just a small degree (approximately 12%) of redundancy between simultaneously recorded inferior temporal cortex (IT) neurons; and that there is very little gain of information that arises from stimulus-dependent synchronization effects in these neurons.

2003 ◽  
Vol 23 (4) ◽  
pp. 490-498 ◽  
Author(s):  
Federico E. Turkheimer ◽  
Rainer Hinz ◽  
Vincent J. Cunningham

This article deals with the problem of model selection for the mathematical description of tracer kinetics in nuclear medicine. It stems from the consideration of some specific data sets where different models have similar performances. In these situations, it is shown that considerate averaging of a parameter's estimates over the entire model set is better than obtaining the estimates from one model only. Furthermore, it is also shown that the procedure of averaging over a small number of “good” models reduces the “generalization error,” the error introduced when the model selected over a particular data set is applied to different conditions, such as subject populations with altered physiologic parameters, modified acquisition protocols, and different signal-to-noise ratios. The method of averaging over the entire model set uses Akaike coefficients as measures of an individual model's likelihood. To facilitate the understanding of these statistical tools, the authors provide an introduction to model selection criteria and a short technical treatment of Akaike's information–theoretic approach. The new method is illustrated and epitomized by a case example on the modeling of [11C]flumazenil kinetics in the brain, containing both real and simulated data.


Author(s):  
R. V. Prasad ◽  
R. Muralishankar ◽  
S. Vijay ◽  
H. N. Shankar ◽  
Przemyslaw Pawelczak ◽  
...  

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