scholarly journals Introducing divisive inhibition in the Wilson-Cowan model

2019 ◽  
Author(s):  
Christoforos A. Papasavvas ◽  
Yujiang Wang
Keyword(s):  
Computational Models ◽  
Neural Field ◽  
Cortical Circuits ◽  
Cortical Networks ◽  
Crucial Step ◽  
Inhibitory Mechanisms ◽  
Neural Mass ◽  
Different Types ◽  
Neural Field Models

AbstractBoth subtractive and divisive inhibition has been recorded in cortical circuits and recent findings suggest that different interneuronal populations are responsible for the different types of inhibition. This calls for the formulation and description of these inhibitory mechanisms in computational models of cortical networks. Neural mass and neural field models typically only feature subtractive inhibition. Here, we introduce how divisive inhibition can be incorporated in such models, using the Wilson-Cowan modelling formalism as an example. In addition, we show how the subtractive and divisive modulations can be combined. Including divisive inhibition in neural mass models is a crucial step in understanding its role in shaping oscillatory phenomena in cortical networks.

scholarly journals Modeling the Generation of Phase-Amplitude Coupling in Cortical Circuits: From Detailed Networks to Neural Mass Models

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Roberto C. Sotero
Keyword(s):  
Computational Models ◽  
High Frequency Oscillation ◽  
Mathematical Framework ◽  
Cortical Circuits ◽  
Frequency Oscillation ◽  
Neuronal Populations ◽  
Neural Mass ◽  
Phase Amplitude ◽  
The Brain ◽  
Neuroscience Community

Phase-amplitude coupling (PAC), the phenomenon where the amplitude of a high frequency oscillation is modulated by the phase of a lower frequency oscillation, is attracting an increasing interest in the neuroscience community due to its potential relevance for understanding healthy and pathological information processing in the brain. PAC is a diverse phenomenon, having been experimentally detected in at least ten combinations of rhythms: delta-theta, delta-alpha, delta-beta, delta-gamma, theta-alpha, theta-beta, theta-gamma, alpha-beta, alpha-gamma, and beta-gamma. However, a complete understanding of the biophysical mechanisms generating this diversity is lacking. Here we review computational models of PAC generation that range from detailed models of neuronal networks, where each cell is described by Hodgkin-Huxley-type equations, to neural mass models (NMMs) where only the average activities of neuronal populations are considered. We argue that NMMs are an appropriate mathematical framework (due to the small number of parameters and variables involved and the richness of the dynamics they can generate) to study the PAC phenomenon.

AFM-Based Nanoindentation Process: A Comparative Study

2012 ◽  
Author(s):  
Rapeepan Promyoo ◽  
Hazim El-Mounayri ◽  
Kody Varahramyan
Keyword(s):  
Md Simulation ◽  
Computational Models ◽  
Surface Deformation ◽  
Experimental Results ◽  
Computational Time ◽  
Machined Surface ◽  
Indentation Force ◽  
Subsurface Deformation ◽  
Different Types ◽  
Copper Aluminum

Atomic force microscopy (AFM) has been widely used for nanomachining and fabrication of micro/nanodevices. This paper describes the development and validation of computational models for AFM-based nanomachining. Molecular Dynamics (MD) technique is used to model and simulate mechanical indentation at the nanoscale for different types of materials, including gold, copper, aluminum, and silicon. The simulation allows for the prediction of indentation forces at the interface between an indenter and a substrate. The effects of tip materials on machined surface are investigated. The material deformation and indentation geometry are extracted based on the final locations of the atoms, which have been displaced by the rigid tool. In addition to the modeling, an AFM was used to conduct actual indentation at the nanoscale, and provide measurements to which the MD simulation predictions can be compared. The MD simulation results show that surface and subsurface deformation found in the case of gold, copper and aluminum have the same pattern. However, aluminum has more surface deformation than other materials. Two different types of indenter tips including diamond and silicon tips were used in the model. More surface and subsurface deformation can be observed for the case of nanoindentation with diamond tip. The indentation forces at various depths of indentation were obtained. It can be concluded that indentation force increases as depth of indentation increases. Due to limitations on computational time, the quantitative values of the indentation force obtained from MD simulation are not comparable to the experimental results. However, the increasing trends of indentation force are the same for both simulation and experimental results.

scholarly journals Modeling fMRI signals can provide insights into neural processing in the cerebral cortex

2015 ◽  
Vol 114 (2) ◽  
pp. 768-780 ◽  
Author(s):  
Simo Vanni ◽  
Fariba Sharifian ◽  
Hanna Heikkinen ◽  
Ricardo Vigário
Keyword(s):  
Computational Models ◽  
Real Life ◽  
Mass Action ◽  
System Level ◽  
Specific System ◽  
Physiological Mechanisms ◽  
Mathematical Functions ◽  
Neural Mass ◽  
Recent Developments ◽  
The Relationship

Every stimulus or task activates multiple areas in the mammalian cortex. These distributed activations can be measured with functional magnetic resonance imaging (fMRI), which has the best spatial resolution among the noninvasive brain imaging methods. Unfortunately, the relationship between the fMRI activations and distributed cortical processing has remained unclear, both because the coupling between neural and fMRI activations has remained poorly understood and because fMRI voxels are too large to directly sense the local neural events. To get an idea of the local processing given the macroscopic data, we need models to simulate the neural activity and to provide output that can be compared with fMRI data. Such models can describe neural mechanisms as mathematical functions between input and output in a specific system, with little correspondence to physiological mechanisms. Alternatively, models can be biomimetic, including biological details with straightforward correspondence to experimental data. After careful balancing between complexity, computational efficiency, and realism, a biomimetic simulation should be able to provide insight into how biological structures or functions contribute to actual data processing as well as to promote theory-driven neuroscience experiments. This review analyzes the requirements for validating system-level computational models with fMRI. In particular, we study mesoscopic biomimetic models, which include a limited set of details from real-life networks and enable system-level simulations of neural mass action. In addition, we discuss how recent developments in neurophysiology and biophysics may significantly advance the modelling of fMRI signals.

scholarly journals Evaluating Word Representation Features in Biomedical Named Entity Recognition Tasks

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Buzhou Tang ◽  
Hongxin Cao ◽  
Xiaolong Wang ◽  
Qingcai Chen ◽  
Hua Xu
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Biomedical Domain ◽  
Crucial Step ◽  
Named Entity ◽  
Different Types ◽  
Word Representation ◽  
Biomedical Named Entity Recognition

Biomedical Named Entity Recognition (BNER), which extracts important entities such as genes and proteins, is a crucial step of natural language processing in the biomedical domain. Various machine learning-based approaches have been applied to BNER tasks and showed good performance. In this paper, we systematically investigated three different types of word representation (WR) features for BNER, including clustering-based representation, distributional representation, and word embeddings. We selected one algorithm from each of the three types of WR features and applied them to the JNLPBA and BioCreAtIvE II BNER tasks. Our results showed that all the three WR algorithms were beneficial to machine learning-based BNER systems. Moreover, combining these different types of WR features further improved BNER performance, indicating that they are complementary to each other. By combining all the three types of WR features, the improvements inF-measure on the BioCreAtIvE II GM and JNLPBA corpora were 3.75% and 1.39%, respectively, when compared with the systems using baseline features. To the best of our knowledge, this is the first study to systematically evaluate the effect of three different types of WR features for BNER tasks.

Dynamic Approximation of Spatiotemporal Receptive Fields in Nonlinear Neural Field Models

2002 ◽  
Vol 14 (8) ◽  
pp. 1801-1825 ◽  
Author(s):  
Thomas Wennekers
Keyword(s):  
Order Approximation ◽  
Receptive Fields ◽  
Neural Field ◽  
Orientation Tuning ◽  
Field Equations ◽  
Dynamic Tuning ◽  
Dimensional Approximation ◽  
Low Dimensional ◽  
Spatiotemporal Response ◽  
Neural Field Models

This article presents an approximation method to reduce the spatiotemporal behavior of localized activation peaks (also called “bumps”) in nonlinear neural field equations to a set of coupled ordinary differential equations (ODEs) for only the amplitudes and tuning widths of these peaks. This enables a simplified analysis of steady-state receptive fields and their stability, as well as spatiotemporal point spread functions and dynamic tuning properties. A lowest-order approximation for peak amplitudes alone shows that much of the well-studied behavior of small neural systems (e.g., the Wilson-Cowan oscillator) should carry over to localized solutions in neural fields. Full spatiotemporal response profiles can further be reconstructed from this low-dimensional approximation. The method is applied to two standard neural field models: a one-layer model with difference-of-gaussians connectivity kernel and a two-layer excitatory-inhibitory network. Similar models have been previously employed in numerical studies addressing orientation tuning of cortical simple cells. Explicit formulas for tuning properties, instabilities, and oscillation frequencies are given, and exemplary spatiotemporal response functions, reconstructed from the low-dimensional approximation, are compared with full network simulations.

scholarly journals Trading off the cost of conflict against expected rewards

2018 ◽  
Author(s):  
Nura Sidarus ◽  
Stefano Palminteri ◽  
Valérian Chambon
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Cognitive Control ◽  
Reversal Learning ◽  
Computational Models ◽  
Learning Task ◽  
Learning Rates ◽  
Different Types ◽  
Irrelevant Distractors ◽  
The Cost

AbstractValue-based decision-making involves trading off the cost associated with an action against its expected reward. Research has shown that both physical and mental effort constitute such subjective costs, biasing choices away from effortful actions, and discounting the value of obtained rewards. Facing conflicts between competing action alternatives is considered aversive, as recruiting cognitive control to overcome conflict is effortful. Yet, it remains unclear whether conflict is also perceived as a cost in value-based decisions. The present study investigated this question by embedding irrelevant distractors (flanker arrows) within a reversal-learning task, with intermixed free and instructed trials. Results showed that participants learned to adapt their choices to maximize rewards, but were nevertheless biased to follow the suggestions of irrelevant distractors. Thus, the perceived cost of being in conflict with an external suggestion could sometimes trump internal value representations. By adapting computational models of reinforcement learning, we assessed the influence of conflict at both the decision and learning stages. Modelling the decision showed that conflict was avoided when evidence for either action alternative was weak, demonstrating that the cost of conflict was traded off against expected rewards. During the learning phase, we found that learning rates were reduced in instructed, relative to free, choices. Learning rates were further reduced by conflict between an instruction and subjective action values, whereas learning was not robustly influenced by conflict between one’s actions and external distractors. Our results show that the subjective cost of conflict factors into value-based decision-making, and highlights that different types of conflict may have different effects on learning about action outcomes.

scholarly journals Synaptic Dynamics as Convolutional Units

2020 ◽  
Author(s):  
Julian Rossbroich ◽  
Daniel Trotter ◽  
Katalin Tóth ◽  
Richard Naud
Keyword(s):  
Neural Networks ◽  
Action Potentials ◽  
Computational Models ◽  
Mathematical Framework ◽  
Challenging Problem ◽  
Maximum Likelihood Approach ◽  
Different Types ◽  
Likelihood Approach ◽  
Synaptic Dynamics ◽  
Nonlinear Operation

AbstractSynaptic dynamics differ markedly across connections and strongly regulate how action potentials are being communicated. To model the range of synaptic dynamics observed in experiments, we develop a flexible mathematical framework based on a linear-nonlinear operation. This model can capture various experimentally observed features of synaptic dynamics and different types of heteroskedasticity. Despite its conceptual simplicity, we show it is more adaptable than previous models. Combined with a standard maximum likelihood approach, synaptic dynamics can be accurately and efficiently characterized using naturalistic stimulation patterns. These results make explicit that synaptic processing bears algorithmic similarities with information processing in convolutional neural networks.Author summaryUnderstanding how information is transmitted relies heavily on knowledge of the underlying regulatory synaptic dynamics. Existing computational models for capturing such dynamics are often either very complex or too restrictive. As a result, effectively capturing the different types of dynamics observed experimentally remains a challenging problem. Here, we propose a mathematically flexible linear-nonlinear model that is capable of efficiently characterizing synaptic dynamics. We demonstrate the ability of this model to capture different features of experimentally observed data.

scholarly journals Activity based checkpoints ensure circuit stability in the olfactory system

2017 ◽  
Author(s):  
Kiely N James ◽  
Benjamin T Throesch ◽  
Weston Davini ◽  
Kevin T Eade ◽  
Sulagna Ghosh ◽  
...  
Keyword(s):  
Olfactory Bulb ◽  
Computational Models ◽  
Adult Life ◽  
Adult Brain ◽  
Cortical Circuits ◽  
Mt Neurons ◽  
Perceptual Stability ◽  
The Face ◽  
Sensory Activity ◽  
Activity Dependent

AbstractOlfactory circuits function at birth, yet are continuously remodeled through the integration of adult-born interneurons into the olfactory bulb in a manner that preserves olfactory perceptual stability throughout adult life. The mechanisms that ensure appropriate circuit stability in this dynamic context remain poorly understood. Since interneurons sculpt the excitatory output of mitral and tufted (MT) neurons to the olfactory cortex, we predicted that MT neurons might instruct interneuron wiring in the adult brain. By blocking synaptic transmission from MT neurons we show that MT neuronal activity is critical to maintain olfactory bulb integrity and interneuron survival. Blocking interneuron death uncovered a second activity-dependent checkpoint regulating dendrite branching. In contrast, cortical circuits and MT neurons remain stable in the face of these silent and degenerating olfactory circuits. These studies identify a circuit-specific role for non-sensory activity in regulating integration of neurons into the adult brain, as predicted by previous computational models.

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