Latent Attractors: A Model for Context-Dependent Place Representations in the Hippocampus

2000 ◽  
Vol 12 (5) ◽  
pp. 1009-1043 ◽  
Author(s):  
Simona Doboli ◽  
Ali A. Minai ◽  
Phillip J. Best
Keyword(s):  
Computational Models ◽  
Strong Dependence ◽  
Pyramidal Cells ◽  
Afferent Input ◽  
Connectionist Model ◽  
Behavioral Context ◽  
Channel System ◽  
Proposed Model ◽  
Context Dependent ◽  
Computational Basis

Cells throughout the rodent hippocampal system show place-specific patterns of firing called place fields, creating a coarse-coded representation of location. The dependencies of this place code—or cognitive map—on sensory cues have been investigated extensively, and several computational models have been developed to explain them. However, place representations also exhibit strong dependence on spatial and behavioral context, and identical sensory environments can produce very different place codes in different situations. Several recent studies have proposed models for the computational basis of this phenomenon, but it is still not completely understood. In this article, we present a very simple connectionist model for producing context-dependent place representations in the hippocampus. We propose that context dependence arises in the den-tate gyrus-hilus (DGH) system, which functions as a dynamic selector, disposing a small group of granule and pyramidal cells to fire in response to afferent stimulus while depressing the rest. It is hypothesized that the DGH system dynamics has “latent attractors,” which are unmasked by the afferent input and channel system activity into subpopulations of cells in the DG, CA3, and other hippocampal regions as observed experimentally. The proposed model shows that a minimally structured hippocampus-like system can robustly produce context-dependent place codes with realistic attributes.

Computational basis of decision-making impairment in multiple sclerosis

2021 ◽  
pp. 135245852110593
Author(s):  
Rodrigo S Fernández ◽  
Lucia Crivelli ◽  
María E Pedreira ◽  
Ricardo F Allegri ◽  
Jorge Correale
Keyword(s):  
Multiple Sclerosis ◽  
Decision Making ◽  
Computational Models ◽  
Iowa Gambling Task ◽  
Memory Impairments ◽  
Hierarchical Bayesian Analysis ◽  
Control Subjects ◽  
Neurocognitive Impairments ◽  
Neurocognitive Evaluation ◽  
Computational Basis

Background: Multiple sclerosis (MS) is commonly associated with decision-making, neurocognitive impairments, and mood and motivational symptoms. However, their relationship may be obscured by traditional scoring methods. Objectives: To study the computational basis underlying decision-making impairments in MS and their interaction with neurocognitive and neuropsychiatric measures. Methods: Twenty-nine MS patients and 26 matched control subjects completed a computer version of the Iowa Gambling Task (IGT). Participants underwent neurocognitive evaluation using an expanded version of the Brief Repeatable Battery. Hierarchical Bayesian Analysis was used to estimate three established computational models to compare parameters between groups. Results: Patients showed increased learning rate and reduced loss-aversion during decision-making relative to control subjects. These alterations were associated with: (1) reduced net gains in the IGT; (2) processing speed, executive functioning and memory impairments; and (3) higher levels of depression and current apathy. Conclusion: Decision-making deficits in MS patients could be described by the interplay between latent computational processes, neurocognitive impairments, and mood/motivational symptoms.

scholarly journals A Computational Model to Assess Poststenting Wall Stresses Dependence on Plaque Structure and Stenosis Severity in Coronary Artery

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Zuned Hajiali ◽  
Mahsa Dabagh ◽  
Payman Jalali
Keyword(s):  
Coronary Arteries ◽  
Computational Models ◽  
Strong Dependence ◽  
Internal Stresses ◽  
Shear Stresses ◽  
Stress Concentrations ◽  
Opening Pressure ◽  
Fibrous Cap ◽  
Stent Restenosis ◽  
Von Mises

The current study presents computational models to investigate the poststenting hemodynamic stresses and internal stresses over/within the diseased walls of coronary arteries which are in different states of atherosclerotic plaque. The finite element method is applied to build the axisymmetric models which include the plaque, arterial wall, and stent struts. The study takes into account the mechanical effects of the opening pressure and its association with the plaque severity and the morphology. The wall shear stresses and the von Mises stresses within the stented coronary arteries show their strong dependence on the plaque structure, particularly the fibrous cap thickness. Higher stresses occur in severely stenosed coronaries with a thinner fibrous cap. Large stress concentrations around the stent struts cause injury or damage to the vessel wall which is linked to the mechanism of restenosis. The in-stent restenosis rate is also highly dependent on the opening pressure, to the extent that stenosed artery is expanded, and geometry of the stent struts. The present study demonstrates, for the first time, that the restenosis is to be viewed as a consequence of biomechanical design of a stent repeating unit, the opening pressure, and the severity and morphology of the plaque.

scholarly journals Intracellular and Computational Characterization of the Intracortical Inhibitory Control of Synchronized Thalamic Inputs In Vivo

1997 ◽  
Vol 78 (1) ◽  
pp. 335-350 ◽  
Author(s):  
Diego Contreras ◽  
Alain Destexhe ◽  
Mircea Steriade
Keyword(s):  
Inhibitory Control ◽  
Computational Models ◽  
Pyramidal Cells ◽  
Excitatory Postsynaptic Potential ◽  
Thalamic Stimulation ◽  
Postsynaptic Potential ◽  
Spike Wave

Contreras, Diego, Alain Destexhe, and Mircea Steriade. Intracellular and computational characterization of the intracortical inhibitory control of synchronized thalamic inputs in vivo. J. Neurophysiol. 78: 335–350, 1997. We investigated the presence and role of local inhibitory cortical control over synchronized thalamic inputs during spindle oscillations (7–14 Hz) by combining intracellular recordings of pyramidal cells in barbiturate-anesthetized cats and computational models. The recordings showed that 1) similar excitatory postsynaptic potential (EPSP)/inhibitory postsynaptic potential (IPSP) sequences occurred either during spindles or following thalamic stimulation; 2) reversed IPSPs with chloride-filled pipettes transformed spindle-related EPSP/IPSP sequences into robust bursts with spike inactivation, resembling paroxysmal depolarizing shifts during seizures; and 3) dual simultaneous impalements showed that inhibition associated with synchronized thalamic inputs is local. Computational models were based on reconstructed pyramidal cells constrained by recordings from the same cells. These models showed that the transformation of EPSP/IPSP sequences into fully developed spike bursts critically needs a relatively high density of inhibitory currents in the soma and proximal dendrites. In addition, models predict significant Ca2+ transients in dendrites due to synchronized thalamic inputs. We conclude that synchronized thalamic inputs are subject to strong inhibitory control within the cortex and propose that 1) local impairment of inhibition contributes to the transformation of spindles into spike-wave-type discharges, and 2) spindle-related inputs trigger Ca2+ events in cortical dendrites that may subserve plasticity phenomena during sleep.

iRNAD: a computational tool for identifying D modification sites in RNA sequence

2019 ◽  
Vol 35 (23) ◽  
pp. 4922-4929 ◽  
Author(s):  
Zhao-Chun Xu ◽  
Peng-Mian Feng ◽  
Hui Yang ◽  
Wang-Ren Qiu ◽  
Wei Chen ◽  
...  
Keyword(s):  
Computational Models ◽  
Operating Characteristic ◽  
Cross Validation ◽  
Characteristic Curve ◽  
Support Vector ◽  
Final Model ◽  
Rna Sequence ◽  
Functional Roles ◽  
Proposed Model ◽  
User Friendly

Abstract Motivation Dihydrouridine (D) is a common RNA post-transcriptional modification found in eukaryotes, bacteria and a few archaea. The modification can promote the conformational flexibility of individual nucleotide bases. And its levels are increased in cancerous tissues. Therefore, it is necessary to detect D in RNA for further understanding its functional roles. Since wet-experimental techniques for the aim are time-consuming and laborious, it is urgent to develop computational models to identify D modification sites in RNA. Results We constructed a predictor, called iRNAD, for identifying D modification sites in RNA sequence. In this predictor, the RNA samples derived from five species were encoded by nucleotide chemical property and nucleotide density. Support vector machine was utilized to perform the classification. The final model could produce the overall accuracy of 96.18% with the area under the receiver operating characteristic curve of 0.9839 in jackknife cross-validation test. Furthermore, we performed a series of validations from several aspects and demonstrated the robustness and reliability of the proposed model. Availability and implementation A user-friendly web-server called iRNAD can be freely accessible at http://lin-group.cn/server/iRNAD, which will provide convenience and guide to users for further studying D modification.

Analytical Solution of Hydrostatic Pocket Tilting

2016 ◽  
Vol 821 ◽  
pp. 113-119 ◽  
Author(s):  
Eduard Stach ◽  
Jiří Falta ◽  
Matěj Sulitka
Keyword(s):  
Computational Models ◽  
Load Capacity ◽  
Navier Stokes ◽  
Navier Stokes Equation ◽  
Detailed Model ◽  
Limit Values ◽  
Inertia Effects ◽  
Proposed Model ◽  
Machine Tool Structure

Tilting (parallelism error) of guiding surfaces may cause reduction of load capacity of hydrostatic (HS) guideways and bearings in machine tools (MT). Using coupled finite element (FE) computational models of MT structures, it is nowadays possible to determine the extent of guiding surfaces deformation caused by thermal effects, gravitational force, cutting forces and inertia effects. Assessment of maximum allowable tilt has so far been based merely on experience. The paper presents a detailed model developed for description of the effect of HS bearing tilt on the load capacity characteristics of HS guideways. The model allows an evaluation of the tilt influence on the change of the characteristics as well as determination of the limit values of allowable tilt in interaction with compliant machine tool structure. The proposed model is based on the model of flow over the land of the HS pocket under extended Navier-Stokes equation. The model is verified using an experimental test rig.

scholarly journals DNA6mA-MINT: DNA-6mA Modification Identification Neural Tool

Genes ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 898 ◽  
Author(s):  
Mobeen Ur Rehman ◽  
Kil To Chong
Keyword(s):  
Computational Models ◽  
Short Term Memory ◽  
Binary Sequence ◽  
The Neural Network ◽  
Proposed Model ◽  
Time Parameters ◽  
Long Short Term Memory ◽  
High Level ◽  
User Friendly ◽  
Fold Cross Validation

DNA N6-methyladenine (6mA) is part of numerous biological processes including DNA repair, DNA replication, and DNA transcription. The 6mA modification sites hold a great impact when their biological function is under consideration. Research in biochemical experiments for this purpose is carried out and they have demonstrated good results. However, they proved not to be a practical solution when accessed under cost and time parameters. This led researchers to develop computational models to fulfill the requirement of modification identification. In consensus, we have developed a computational model recommended by Chou’s 5-steps rule. The Neural Network (NN) model uses convolution layers to extract the high-level features from the encoded binary sequence. These extracted features were given an optimal interpretation by using a Long Short-Term Memory (LSTM) layer. The proposed architecture showed higher performance compared to state-of-the-art techniques. The proposed model is evaluated on Mus musculus, Rice, and “Combined-species” genomes with 5- and 10-fold cross-validation. Further, with access to a user-friendly web server, publicly available can be accessed freely.

The Pyramidal Cell and its Local-Circuit Interneurons: A Hypothetical Unit of the Mammalian Cerebral Cortex

1990 ◽  
Vol 2 (3) ◽  
pp. 180-194 ◽  
Author(s):  
Miguel Marín-Padilla
Keyword(s):  
Cerebral Cortex ◽  
Pyramidal Cell ◽  
Three Dimensional ◽  
Pyramidal Cells ◽  
Cortical Function ◽  
Local Circuit ◽  
Proposed Model ◽  
Dimensional Distribution ◽  
Chandelier Cells ◽  
Species Specific

A pyramidal cell with five of its local-circuit interneurons (Cajal–Retzius, Martinotti, Cajal double-bouquet, basket, and chandelier cells), constitutes a distinct structural/functional assemblage of the mammalian neocortex. This pyramidal/local-circuit neuronal assemblage is proposed herein as a basic neocortical unit. This unit is shared by all mammals, embodies both specific structural as well as functional elements, and constitutes an essential developmental building block of the neocortex. In the model, the pyramidal cell represents a distinct, stable, projective, excitatory neuron that has remained essentially unchanged in the course of mammalian phylogeny. On the other hand, its local-circuit interneurons are more likely to be inhibitory and less stable, designed perhaps to adapt, and modify in response to environmental needs. The proposed model infers that the number of pyramidal cells contacted by each local-circuit interneuron as well as the number of synaptic contacts established with each one are elements acquired post-natally in response to individual needs. Thereby, the overall three-dimensional distribution and extent of these pyramidal/local-circuit neuronal assemblages should be species-specific, variable among individual of the same species, and able to adapt in response to environmental needs. The model introduces a different approach, perhaps a new vantage point, for the study of the basic structural organization of the mammalian cerebral cortex. Relationships of the proposed model to cortical function in general and to learning behavior in particular are discussed.

scholarly journals Computational Models of the Notch Network Elucidate Mechanisms of Context-dependent Signaling

2009 ◽  
Vol 5 (5) ◽  
pp. e1000390 ◽  
Author(s):  
Smita Agrawal ◽  
Colin Archer ◽  
David V. Schaffer
Keyword(s):  
Computational Models ◽  
Context Dependent

scholarly journals Metamodel for nodal pressure estimation at near real-time in water distribution systems using artificial neural networks

2017 ◽  
Vol 20 (2) ◽  
pp. 486-496 ◽  
Author(s):  
Gustavo Meirelles Lima ◽  
Bruno Melo Brentan ◽  
Daniel Manzi ◽  
Edevar Luvizotto
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Distribution Systems ◽  
Water Distribution ◽  
Computational Models ◽  
Pressure Sensors ◽  
Water Supply Systems ◽  
Pressure Data ◽  
Proposed Model ◽  
Artificial Neural

Abstract The development of computational models for analysis of the operation of water supply systems requires the calibration of pipes' roughness, among other parameters. Inadequate values of this parameter can result in inaccurate solutions, compromising the applicability of the model as a decision-making tool. This paper presents a metamodel to estimate the pressure at all nodes of a distribution network based on artificial neural networks (ANNs), using a set of field data obtained from strategically located pressure sensors. This approach aims to increase the available pressure data, reducing the degree of freedom of the calibration problem. The proposed model uses the inlet flow of the district metering area and pressure data monitored in some nodes, as input data to the ANN, obtaining as output, the pressure values for nodes that were not monitored. Two case studies of real networks are presented to validate the efficiency and accuracy of the method. The results ratify the efficiency of ANN as state forecaster, showing the high applicability of the metamodel tool to increase a database or to identify abnormal events during an operation.

A Connectionist Model of Attitude Formation and Change

2005 ◽  
Vol 9 (3) ◽  
pp. 231-274 ◽  
Author(s):  
Frank Van Overwalle ◽  
Frank Siebler
Keyword(s):  
Network Architecture ◽  
Learning Algorithm ◽  
Attitude Formation ◽  
Dual Process ◽  
Connectionist Model ◽  
Algebraic Models ◽  
Connectionist Network ◽  
Proposed Model ◽  
Processing Modes ◽  
Processing Mechanisms

This article discusses a recurrent connectionist network, simulating empirical phenomena usually explained by current dual-process approaches of attitudes, thereby focusing on the processing mechanisms that may underlie both central and peripheral routes of persuasion. Major findings in attitude formation and change involving both processing modes are reviewed and modeled from a connectionist perspective. We use an autoassociative network architecture with a linear activation update and the delta learning algorithm for adjusting the connection weights. The network is applied to well-known experiments involving deliberative attitude formation, as well as the use of heuristics of length, consensus, expertise, and mood. All these empirical phenomena are successfully reproduced in the simulations. Moreover, the proposed model is shown to be consistent with algebraic models of attitude formation (Fishbein & Ajzen, 1975). The discussion centers on how the proposed network model may be used to unite and formalize current ideas and hypotheses on the processes underlying attitude acquisition and how it can be deployed to develop novel hypotheses in the attitude domain.

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