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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

Model structure. (A) Information flow in the hippocampus. The perforant path is the major input to the hippocampus. The axons of the perforant path mainly arise in layer II of the entorhinal cortex (ECII). Axons from ECII/IV project to the granule cells of the DG. The mossy fibers are the axons of the DG granule cells and extend from the DG to CA3 pyramidal cells, forming their major input. Information is transferred by axons that project from the CA3 to the CA1 region. The information from CA1 to the subiculum (SUB) and on the entorhinal cortex (EC) performs the principal output from the hippocampus. (B) The model is composed of a neural network with 800 neurons in EC and 4000 neurons in the DG. Fully separated input pattern in EC may trigger separated neurons in the DG. (C) Increase in the number of activated neurons in EC or the connectivity rate between layers may lead to overlap in pattern of activated neurons in the DG (shown by red) which results in a decrease in pattern separation efficiency of the DG.
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Figure 1: Model structure. (A) Information flow in the hippocampus. The perforant path is the major input to the hippocampus. The axons of the perforant path mainly arise in layer II of the entorhinal cortex (ECII). Axons from ECII/IV project to the granule cells of the DG. The mossy fibers are the axons of the DG granule cells and extend from the DG to CA3 pyramidal cells, forming their major input. Information is transferred by axons that project from the CA3 to the CA1 region. The information from CA1 to the subiculum (SUB) and on the entorhinal cortex (EC) performs the principal output from the hippocampus. (B) The model is composed of a neural network with 800 neurons in EC and 4000 neurons in the DG. Fully separated input pattern in EC may trigger separated neurons in the DG. (C) Increase in the number of activated neurons in EC or the connectivity rate between layers may lead to overlap in pattern of activated neurons in the DG (shown by red) which results in a decrease in pattern separation efficiency of the DG.

Mentions: The hippocampus is a brain structure that plays a critical role in consolidating information from short-term memory into long-term memory. In the classic tri-synaptic pathway, information proceeds from the entorhinal cortex (EC) to the dentate gyrus (DG) to CA3 and then to CA1 which is known as the main hippocampal output (Van Strien et al., 2009; Newman and Hasselmo, 2014; Figure 1A). The animal’s brain ability to discriminate between similar experiences is a crucial feature of episodic memory. It is believed that information processing in the hippocampus complies with ‘compressed sensing theory’ (Petrantonakis and Poirazi, 2014). The formation of discrete representations in memory is thought to depend on a pattern separation process whereby cortical inputs are decorrelated as they enter the early stages of the hippocampus (Gilbert et al., 2001; Leutgeb et al., 2007). Computational models suggest that such function is dependent on pattern separation (Bakker et al., 2008; Yassa and Stark, 2011). Pattern separation is defined as the ability to transform a set of similar input patterns into a less-similar set of output patterns, which is believed to be dynamically regulated by hilar neurons. The storage capacity of such memory system, in terms of the number of patterns that can be stored and retrieved, is maximized if the patterns to be stored do not overlap extensively (Marr, 1970). In this context, overlap between patterns is defined as the degree to which individual elements in one pattern are also active in another pattern.


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

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

Model structure. (A) Information flow in the hippocampus. The perforant path is the major input to the hippocampus. The axons of the perforant path mainly arise in layer II of the entorhinal cortex (ECII). Axons from ECII/IV project to the granule cells of the DG. The mossy fibers are the axons of the DG granule cells and extend from the DG to CA3 pyramidal cells, forming their major input. Information is transferred by axons that project from the CA3 to the CA1 region. The information from CA1 to the subiculum (SUB) and on the entorhinal cortex (EC) performs the principal output from the hippocampus. (B) The model is composed of a neural network with 800 neurons in EC and 4000 neurons in the DG. Fully separated input pattern in EC may trigger separated neurons in the DG. (C) Increase in the number of activated neurons in EC or the connectivity rate between layers may lead to overlap in pattern of activated neurons in the DG (shown by red) which results in a decrease in pattern separation efficiency of the DG.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Model structure. (A) Information flow in the hippocampus. The perforant path is the major input to the hippocampus. The axons of the perforant path mainly arise in layer II of the entorhinal cortex (ECII). Axons from ECII/IV project to the granule cells of the DG. The mossy fibers are the axons of the DG granule cells and extend from the DG to CA3 pyramidal cells, forming their major input. Information is transferred by axons that project from the CA3 to the CA1 region. The information from CA1 to the subiculum (SUB) and on the entorhinal cortex (EC) performs the principal output from the hippocampus. (B) The model is composed of a neural network with 800 neurons in EC and 4000 neurons in the DG. Fully separated input pattern in EC may trigger separated neurons in the DG. (C) Increase in the number of activated neurons in EC or the connectivity rate between layers may lead to overlap in pattern of activated neurons in the DG (shown by red) which results in a decrease in pattern separation efficiency of the DG.
Mentions: The hippocampus is a brain structure that plays a critical role in consolidating information from short-term memory into long-term memory. In the classic tri-synaptic pathway, information proceeds from the entorhinal cortex (EC) to the dentate gyrus (DG) to CA3 and then to CA1 which is known as the main hippocampal output (Van Strien et al., 2009; Newman and Hasselmo, 2014; Figure 1A). The animal’s brain ability to discriminate between similar experiences is a crucial feature of episodic memory. It is believed that information processing in the hippocampus complies with ‘compressed sensing theory’ (Petrantonakis and Poirazi, 2014). The formation of discrete representations in memory is thought to depend on a pattern separation process whereby cortical inputs are decorrelated as they enter the early stages of the hippocampus (Gilbert et al., 2001; Leutgeb et al., 2007). Computational models suggest that such function is dependent on pattern separation (Bakker et al., 2008; Yassa and Stark, 2011). Pattern separation is defined as the ability to transform a set of similar input patterns into a less-similar set of output patterns, which is believed to be dynamically regulated by hilar neurons. The storage capacity of such memory system, in terms of the number of patterns that can be stored and retrieved, is maximized if the patterns to be stored do not overlap extensively (Marr, 1970). In this context, overlap between patterns is defined as the degree to which individual elements in one pattern are also active in another pattern.

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