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A computational model of pattern separation efficiency in the dentate gyrus with implications in schizophrenia.

Faghihi F, Moustafa AA - Front Syst Neurosci (2015)

Bottom Line: Known neurons number of the EC and the DG is used to construct a neural network by electrophysiological features of granule cells of the DG.Separated inputs as activated neurons in the EC with different firing probabilities are presented into the DG.This work presents a mechanistic explanation for experimental observations in the hippocampus, in combination with theoretical measures.

View Article: PubMed Central - PubMed

Affiliation: Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study, George Mason University Fairfax, VA, USA.

ABSTRACT
Information processing in the hippocampus begins by transferring spiking activity of the entorhinal cortex (EC) into the dentate gyrus (DG). Activity pattern in the EC is separated by the DG such that it plays an important role in hippocampal functions including memory. The structural and physiological parameters of these neural networks enable the hippocampus to be efficient in encoding a large number of inputs that animals receive and process in their life time. The neural encoding capacity of the DG depends on its single neurons encoding and pattern separation efficiency. In this study, encoding by the DG is modeled such that single neurons and pattern separation efficiency are measured using simulations of different parameter values. For this purpose, a probabilistic model of single neurons efficiency is presented to study the role of structural and physiological parameters. Known neurons number of the EC and the DG is used to construct a neural network by electrophysiological features of granule cells of the DG. Separated inputs as activated neurons in the EC with different firing probabilities are presented into the DG. For different connectivity rates between the EC and DG, pattern separation efficiency of the DG is measured. The results show that in the absence of feedback inhibition on the DG neurons, the DG demonstrates low separation efficiency and high firing frequency. Feedback inhibition can increase separation efficiency while resulting in very low single neuron's encoding efficiency in the DG and very low firing frequency of neurons in the DG (sparse spiking). This work presents a mechanistic explanation for experimental observations in the hippocampus, in combination with theoretical measures. Moreover, the model predicts a critical role for impaired inhibitory neurons in schizophrenia where deficiency in pattern separation of the DG has been observed.

No MeSH data available.


Related in: MedlinePlus

Mutual information for low and high connectivity rates. The average mutual information for a low connectivity rate equal to 0.2 is decreased by increasing firing threshold while it is increased for high connectivity rate equal to 0.8.
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Figure 4: Mutual information for low and high connectivity rates. The average mutual information for a low connectivity rate equal to 0.2 is decreased by increasing firing threshold while it is increased for high connectivity rate equal to 0.8.

Mentions: Figure 3 shows the MI for different firing thresholds of the single neuron. The results illustrate a non-linear dependency of MI on connectivity rate and firing probability of inputs. An increase in firing threshold leads to a shift of high MI to right. Figure 4 shows MI for different firing threshold at low and high connectivity rate, 0.2 and 0.8, respectively. The results show that for a low connectivity rate (equal to 0.2) increase in θ leads to a decrease in average MI while it increases MI for high connectivity rate.


A computational model of pattern separation efficiency in the dentate gyrus with implications in schizophrenia.

Faghihi F, Moustafa AA - Front Syst Neurosci (2015)

Mutual information for low and high connectivity rates. The average mutual information for a low connectivity rate equal to 0.2 is decreased by increasing firing threshold while it is increased for high connectivity rate equal to 0.8.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4373261&req=5

Figure 4: Mutual information for low and high connectivity rates. The average mutual information for a low connectivity rate equal to 0.2 is decreased by increasing firing threshold while it is increased for high connectivity rate equal to 0.8.
Mentions: Figure 3 shows the MI for different firing thresholds of the single neuron. The results illustrate a non-linear dependency of MI on connectivity rate and firing probability of inputs. An increase in firing threshold leads to a shift of high MI to right. Figure 4 shows MI for different firing threshold at low and high connectivity rate, 0.2 and 0.8, respectively. The results show that for a low connectivity rate (equal to 0.2) increase in θ leads to a decrease in average MI while it increases MI for high connectivity rate.

Bottom Line: Known neurons number of the EC and the DG is used to construct a neural network by electrophysiological features of granule cells of the DG.Separated inputs as activated neurons in the EC with different firing probabilities are presented into the DG.This work presents a mechanistic explanation for experimental observations in the hippocampus, in combination with theoretical measures.

View Article: PubMed Central - PubMed

Affiliation: Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study, George Mason University Fairfax, VA, USA.

ABSTRACT
Information processing in the hippocampus begins by transferring spiking activity of the entorhinal cortex (EC) into the dentate gyrus (DG). Activity pattern in the EC is separated by the DG such that it plays an important role in hippocampal functions including memory. The structural and physiological parameters of these neural networks enable the hippocampus to be efficient in encoding a large number of inputs that animals receive and process in their life time. The neural encoding capacity of the DG depends on its single neurons encoding and pattern separation efficiency. In this study, encoding by the DG is modeled such that single neurons and pattern separation efficiency are measured using simulations of different parameter values. For this purpose, a probabilistic model of single neurons efficiency is presented to study the role of structural and physiological parameters. Known neurons number of the EC and the DG is used to construct a neural network by electrophysiological features of granule cells of the DG. Separated inputs as activated neurons in the EC with different firing probabilities are presented into the DG. For different connectivity rates between the EC and DG, pattern separation efficiency of the DG is measured. The results show that in the absence of feedback inhibition on the DG neurons, the DG demonstrates low separation efficiency and high firing frequency. Feedback inhibition can increase separation efficiency while resulting in very low single neuron's encoding efficiency in the DG and very low firing frequency of neurons in the DG (sparse spiking). This work presents a mechanistic explanation for experimental observations in the hippocampus, in combination with theoretical measures. Moreover, the model predicts a critical role for impaired inhibitory neurons in schizophrenia where deficiency in pattern separation of the DG has been observed.

No MeSH data available.


Related in: MedlinePlus