Model- and scale-independent performance of a hippocampal CA3 network architecture

Menschik, Elliot D.; Yen, Shih-Cheng; Finkel, Leif H.

doi:10.1016/s0925-2312(99)00050-8

Cited by 12 publications

(6 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this model, interference with previous information was discovered, which lead to a pathological increase in the strength of synaptic connections. The Menschik and Finkel models were based on the loss of cholinergic connections in AD [ 12 , 13 , 14 ]. These models were inspired by an exponential increase in the strength of synaptic connections, which preceded the loss of cholinergic connections.…”

Section: Discussionmentioning

confidence: 99%

“…Models of neurons used in simulation systems of processes associated with associative memory must demonstrate the ability of time-space integration [ 2 , 9 , 10 , 11 ]. Models of neural networks are used in simulations of memory dysfunctions, such as AD, where theta and gamma oscillation is required, as well as various types of neurons and input patterns [ 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Computer Model of Synapse Loss During an Alzheimer’s Disease-Like Pathology in Hippocampal Subregions DG, CA3 and CA1—The Way to Chaos and Information Transfer

Świetlik

Białowas

Moryś

et al. 2019

Entropy

View full text Add to dashboard Cite

The aim of the study was to compare the computer model of synaptic breakdown in an Alzheimer’s disease-like pathology in the dentate gyrus (DG), CA3 and CA1 regions of the hippocampus with a control model using neuronal parameters and methods describing the complexity of the system, such as the correlative dimension, Shannon entropy and positive maximal Lyapunov exponent. The model of synaptic breakdown (from 13% to 50%) in the hippocampus modeling the dynamics of an Alzheimer’s disease-like pathology was simulated. Modeling consisted in turning off one after the other EC2 connections and connections from the dentate gyrus on the CA3 pyramidal neurons. The pathological model of synaptic disintegration was compared to a control. The larger synaptic breakdown was associated with a statistically significant decrease in the number of spikes (R = −0.79, P < 0.001), spikes per burst (R = −0.76, P < 0.001) and burst duration (R = −0.83, P < 0.001) and an increase in the inter-burst interval (R = 0.85, P < 0.001) in DG-CA3-CA1. The positive maximal Lyapunov exponent in the control model was negative, but in the pathological model had a positive value of DG-CA3-CA1. A statistically significant decrease of Shannon entropy with the direction of information flow DG->CA3->CA1 (R = −0.79, P < 0.001) in the pathological model and a statistically significant increase with greater synaptic breakdown (R = 0.24, P < 0.05) of the CA3-CA1 region was obtained. The reduction of entropy transfer for DG->CA3 at the level of synaptic breakdown of 35% was 35%, compared with the control. Entropy transfer for CA3->CA1 at the level of synaptic breakdown of 35% increased to 95% relative to the control. The synaptic breakdown model in an Alzheimer’s disease-like pathology in DG-CA3-CA1 exhibits chaotic features as opposed to the control. Synaptic breakdown in which an increase of Shannon entropy is observed indicates an irreversible process of Alzheimer’s disease. The increase in synapse loss resulted in decreased information flow and entropy transfer in DG->CA3, and at the same time a strong increase in CA3->CA1.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computer Model of Synapse Loss During an Alzheimer’s Disease-Like Pathology in Hippocampal Subregions DG, CA3 and CA1—The Way to Chaos and Information Transfer

Świetlik

Białowas

Moryś

et al. 2019

Entropy

View full text Add to dashboard Cite

show abstract

“…Hasselmo et al created a model based on runaway synaptic modification, as a mechanism of degradation of memory in AD [ 67 , 68 ], whereas the models of Menschik and Finkel, inspired by the exponential increase in the strength of synaptic connections, were based on the loss of cholinergic connections in Alzheimer’s disease [ 24 , 28 , 29 , 30 ]. Our model of the hippocampal network system was a full connectionist model, which means that each synaptic connection was individually positioned on a particular neuron, and there were various types of neurons and input patterns [ 46 , 47 , 48 , 49 ].…”

Section: Discussionmentioning

confidence: 99%

“…It was proven that both of those models clearly presented some universal behaviors depending on the manipulations of network factors. Application of gamma and theta oscillations with different numbers and types of neurons were used in simulations of memory disorders in Alzheimer’s disease [ 28 , 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

Effects of Inducing Gamma Oscillations in Hippocampal Subregions DG, CA3, and CA1 on the Potential Alleviation of Alzheimer’s Disease-Related Pathology: Computer Modeling and Simulations

Świetlik

Białowas

Moryś

et al. 2019

Entropy

View full text Add to dashboard Cite

The aim of this study was to evaluate the possibility of the gamma oscillation function (40–130 Hz) to reduce Alzheimer’s disease related pathology in a computer model of the hippocampal network dentate gyrus, CA3, and CA1 (DG-CA3-CA1) regions. Methods: Computer simulations were made for a pathological model in which Alzheimer’s disease was simulated by synaptic degradation in the hippocampus. Pathology modeling was based on sequentially turning off the connections with entorhinal cortex layer 2 (EC2) and the dentate gyrus on CA3 pyramidal neurons. Gamma induction modeling consisted of simulating the oscillation provided by the septo-hippocampal pathway with band frequencies from 40–130 Hz. Pathological models with and without gamma induction were compared with a control. Results: In the hippocampal regions of DG, CA3, and CA1, and jointly DG-CA3-CA1 and CA3-CA1, gamma induction resulted in a statistically significant improvement in terms of increased numbers of spikes, spikes per burst, and burst duration as compared with the model simulating Alzheimer’s disease (AD). The positive maximal Lyapunov exponent was negative in both the control model and the one with gamma induction as opposed to the pathological model where it was positive within the DG-CA3-CA1 region. Gamma induction resulted in decreased transfer entropy in accordance with the information flow in DG → CA3 and CA3 → CA1. Conclusions: The results of simulation studies show that inducing gamma oscillations in the hippocampus may reduce Alzheimer’s disease related pathology. Pathologically higher transfer entropy values after gamma induction returned to values comparable to the control model.

show abstract

“…The dense collaterals between pyramidal cells in the dorsal hippocampus (and especially the CA3-to-CA1 projections) have prompted investigators to associate this area to auto-associative memory23 as the route followed by cortically processed information to form long-term memory12 and to encode spatial information related to specific behavioral tasks 13. Parametric modeling methods (based on differential equations) have been used to describe the hippocampal neural dynamics15,18,20 but lead to complex representations that are not fully evaluated in terms of their predictive capability.…”

Section: Introductionmentioning

confidence: 99%

Boolean Modeling of Neural Systems with Point-Process Inputs and Outputs. Part II: Application to the Rat Hippocampus

et al. 2009

View full text Add to dashboard Cite

This paper presents a pilot application of the Boolean–Volterra modeling methodology presented in the companion paper (Part I) that is suitable for the analysis of systems with point-process inputs and outputs (e.g., recordings of the activity of neuronal ensembles). This application seeks to discover the causal links between two neuronal ensembles in the hippocampus of a behaving rat. The experimental data come from multi-unit recordings in the CA3 and CA1 regions of the hippocampus in the form of sequences of action potentials—treated mathematically as point-processes and computationally as spike-trains—that are collected in vivo during two behavioral tasks. The modeling objective is to identify and quantify the causal links among the neurons generating the recorded activity, using Boolean–Volterra models estimated directly from the data according to the methodological framework presented in the companion paper. The obtained models demonstrate the feasibility of the proposed approach using short data-records and provide some insights into the functional properties of the system (e.g., regarding the presence of rhythmic characteristics in the neuronal dynamics of these ensembles), making the proposed methodology an attractive tool for the analysis and modeling of multi-unit recordings from neuronal systems in a practical context.

show abstract

Model- and scale-independent performance of a hippocampal CA3 network architecture

Cited by 12 publications

References 14 publications

Computer Model of Synapse Loss During an Alzheimer’s Disease-Like Pathology in Hippocampal Subregions DG, CA3 and CA1—The Way to Chaos and Information Transfer

Computer Model of Synapse Loss During an Alzheimer’s Disease-Like Pathology in Hippocampal Subregions DG, CA3 and CA1—The Way to Chaos and Information Transfer

Effects of Inducing Gamma Oscillations in Hippocampal Subregions DG, CA3, and CA1 on the Potential Alleviation of Alzheimer’s Disease-Related Pathology: Computer Modeling and Simulations

Boolean Modeling of Neural Systems with Point-Process Inputs and Outputs. Part II: Application to the Rat Hippocampus

Contact Info

Product

Resources

About