scholarly journals Dynamic State and Parameter Estimation Applied to Neuromorphic Systems

2012 ◽  
Vol 24 (7) ◽  
pp. 1669-1694 ◽  
Author(s):  
Emre Ozgur Neftci ◽  
Bryan Toth ◽  
Giacomo Indiveri ◽  
Henry D. I. Abarbanel
Keyword(s):  
Parameter Estimation ◽  
Large Scale ◽  
Computational Models ◽  
Internal State ◽  
Network Models ◽  
Experimental System ◽  
Neural System ◽  
Dynamic State ◽  
Neural Systems ◽  
State Variables

Neuroscientists often propose detailed computational models to probe the properties of the neural systems they study. With the advent of neuromorphic engineering, there is an increasing number of hardware electronic analogs of biological neural systems being proposed as well. However, for both biological and hardware systems, it is often difficult to estimate the parameters of the model so that they are meaningful to the experimental system under study, especially when these models involve a large number of states and parameters that cannot be simultaneously measured. We have developed a procedure to solve this problem in the context of interacting neural populations using a recently developed dynamic state and parameter estimation (DSPE) technique. This technique uses synchronization as a tool for dynamically coupling experimentally measured data to its corresponding model to determine its parameters and internal state variables. Typically experimental data are obtained from the biological neural system and the model is simulated in software; here we show that this technique is also efficient in validating proposed network models for neuromorphic spike-based very large-scale integration (VLSI) chips and that it is able to systematically extract network parameters such as synaptic weights, time constants, and other variables that are not accessible by direct observation. Our results suggest that this method can become a very useful tool for model-based identification and configuration of neuromorphic multichip VLSI systems.

Neurocomputational Models: Theory, Application, Philosophical Consequences

2009 ◽  
Author(s):  
Chris Eliasmith
Keyword(s):  
Neural Coding ◽  
Large Scale ◽  
Neural Dynamics ◽  
Neural Systems ◽  
State Variables ◽  
Neural Representations ◽  
Optimal Linear ◽  
Theory Application ◽  
Linear Decoding ◽  
Population Representation

This article describes the neural engineering framework (NEF), a systematic approach to studying neural systems that has collected and extended a set of consistent methods that are highly general. The NEF draws heavily on past work in theoretical neuroscience, integrating work on neural coding, population representation, and neural dynamics to enable the construction of large-scale biologically plausible neural simulations. It is based on the principles that neural representations defined by a combination of nonlinear encoding and optimal linear decoding and that neural dynamics are characterized by considering neural representations as control theoretic state variables.

scholarly journals Estimation of ECHAM5 climate model closure parameters with adaptive MCMC

2010 ◽  
Vol 10 (20) ◽  
pp. 9993-10002 ◽  
Author(s):  
H. Järvinen ◽  
P. Räisänen ◽  
M. Laine ◽  
J. Tamminen ◽  
A. Ilin ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Objective Function ◽  
Large Scale ◽  
Computational Models ◽  
Climate Models ◽  
Climate Model ◽  
Expert Knowledge ◽  
Adaptive Mcmc ◽  
Posterior Probability Density ◽  
Probability Densities

Abstract. Climate models contain closure parameters to which the model climate is sensitive. These parameters appear in physical parameterization schemes where some unresolved variables are expressed by predefined parameters rather than being explicitly modeled. Currently, best expert knowledge is used to define the optimal closure parameter values, based on observations, process studies, large eddy simulations, etc. Here, parameter estimation, based on the adaptive Markov chain Monte Carlo (MCMC) method, is applied for estimation of joint posterior probability density of a small number (n=4) of closure parameters appearing in the ECHAM5 climate model. The parameters considered are related to clouds and precipitation and they are sampled by an adaptive random walk process of the MCMC. The parameter probability densities are estimated simultaneously for all parameters, subject to an objective function. Five alternative formulations of the objective function are tested, all related to the net radiative flux at the top of the atmosphere. Conclusions of the closure parameter estimation tests with a low-resolution ECHAM5 climate model indicate that (i) adaptive MCMC is a viable option for parameter estimation in large-scale computational models, and (ii) choice of the objective function is crucial for the identifiability of the parameter distributions.

scholarly journals The Role of Large-Scale Feedbacks in Cumulus Convection Parameter Estimation

2016 ◽  
Vol 29 (11) ◽  
pp. 4099-4119 ◽  
Author(s):  
Shan Li ◽  
Shaoqing Zhang ◽  
Zhengyu Liu ◽  
Xiaosong Yang ◽  
Anthony Rosati ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Large Scale ◽  
Climate Model ◽  
Global Climate ◽  
Cumulus Convection ◽  
State Variables ◽  
Climate Drift ◽  
Convection Parameterization ◽  
Convection Parameter

Abstract Uncertainty in cumulus convection parameterization is one of the most important causes of model climate drift through interactions between large-scale background and local convection that use empirically set parameters. Without addressing the large-scale feedback, the calibrated parameter values within a convection scheme are usually not optimal for a climate model. This study first designs a multiple-column atmospheric model that includes large-scale feedbacks for cumulus convection and then explores the role of large-scale feedbacks in cumulus convection parameter estimation using an ensemble filter. The performance of convection parameter estimation with or without the presence of large-scale feedback is examined. It is found that including large-scale feedbacks in cumulus convection parameter estimation can significantly improve the estimation quality. This is because large-scale feedbacks help transform local convection uncertainties into global climate sensitivities, and including these feedbacks enhances the statistical representation of the relationship between parameters and state variables. The results of this study provide insights for further understanding of climate drift induced from imperfect cumulus convection parameterization, which may help improve climate modeling.

scholarly journals The SONATA Data Format for Efficient Description of Large-Scale Network Models

2019 ◽  
Author(s):  
Kael Dai ◽  
Juan Hernando ◽  
Yazan N. Billeh ◽  
Sergey L. Gratiy ◽  
Judit Planas ◽  
...  
Keyword(s):  
Network Architecture ◽  
High Performance ◽  
Large Scale ◽  
Computational Models ◽  
Model Building ◽  
Network Models ◽  
Data Format ◽  
Large Scale Network ◽  
Scale Network ◽  
Biological Realism

AbstractIncreasing availability of comprehensive experimental datasets and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational models in neuroscience. To support construction and simulation, as well as sharing of such large-scale models, a broadly applicable, flexible, and high-performance data format is necessary. To address this need, we have developed the Scalable Open Network Architecture TemplAte (SONATA) data format. It is designed for memory and computational efficiency and works across multiple platforms. The format represents neuronal circuits and simulation inputs and outputs via standardized files and provides much flexibility for adding new conventions or extensions. SONATA is used in multiple modeling and visualization tools, and we also provide reference Application Programming Interfaces and model examples to catalyze further adoption. SONATA format is free and open for the community to use and build upon with the goal of enabling efficient model building, sharing, and reproducibility.

Parameter Estimation of Nonlinear Biomedical Systems Using Extended Kalman Filter Algorithm

2017 ◽  
pp. 76-99 ◽  
Author(s):  
Kamalanand Krishnamurthy
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Computational Models ◽  
Three Dimensional ◽  
Model Parameters ◽  
State Variables ◽  
Three Dimensional Model ◽  
Biomedical Systems ◽  
Estimation Of Model Parameters

Parameter estimation is a central issue in mathematical modelling of biomedical systems and for the development of patient specific models. The technique of estimating parameters helps in obtaining diagnostic information from computational models of biological systems. However, in most of the biomedical systems, the estimation of model parameters is a challenging task due to the nonlinearity of mathematical models. In this chapter, the method of estimation of nonlinear model parameters from measurements of state variables, using the extended Kalman filter, is extensively explained using an example of the three-dimensional model of the HIV/AIDS system.

Parameter Estimation of Nonlinear Biomedical Systems Using Extended Kalman Filter Algorithm

2018 ◽  
pp. 690-713
Author(s):  
Kamalanand Krishnamurthy
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Computational Models ◽  
Three Dimensional ◽  
Model Parameters ◽  
State Variables ◽  
Three Dimensional Model ◽  
Biomedical Systems ◽  
Estimation Of Model Parameters

Parameter estimation is a central issue in mathematical modelling of biomedical systems and for the development of patient specific models. The technique of estimating parameters helps in obtaining diagnostic information from computational models of biological systems. However, in most of the biomedical systems, the estimation of model parameters is a challenging task due to the nonlinearity of mathematical models. In this chapter, the method of estimation of nonlinear model parameters from measurements of state variables, using the extended Kalman filter, is extensively explained using an example of the three-dimensional model of the HIV/AIDS system.

scholarly journals Inferring brain-wide interactions using data-constrained recurrent neural network models

2020 ◽  
Author(s):  
Matthew G. Perich ◽  
Charlotte Arlt ◽  
Sofia Soares ◽  
Megan E. Young ◽  
Clayton P. Mosher ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Large Scale ◽  
Computational Models ◽  
Network Models ◽  
Brain Regions ◽  
Neural Network Models ◽  
Neural Data ◽  
Neural Recordings ◽  
Using Data

ABSTRACTBehavior arises from the coordinated activity of numerous anatomically and functionally distinct brain regions. Modern experimental tools allow unprecedented access to large neural populations spanning many interacting regions brain-wide. Yet, understanding such large-scale datasets necessitates both scalable computational models to extract meaningful features of interregion communication and principled theories to interpret those features. Here, we introduce Current-Based Decomposition (CURBD), an approach for inferring brain-wide interactions using data-constrained recurrent neural network models that directly reproduce experimentally-obtained neural data. CURBD leverages the functional interactions inferred by such models to reveal directional currents between multiple brain regions. We first show that CURBD accurately isolates inter-region currents in simulated networks with known dynamics. We then apply CURBD to multi-region neural recordings obtained from mice during running, macaques during Pavlovian conditioning, and humans during memory retrieval to demonstrate the widespread applicability of CURBD to untangle brain-wide interactions underlying behavior from a variety of neural datasets.

scholarly journals Assessing the role of inhibition in stabilizing neocortical networks requires large-scale perturbation of the inhibitory population

2017 ◽  
Author(s):  
Sadra Sadeh ◽  
R. Angus Silver ◽  
Thomas Mrsic-Flogel ◽  
Dylan Richard Muir
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
Network Models ◽  
Operating Regime ◽  
High Gain ◽  
Inhibitory Neurons ◽  
Wide Field ◽  
Cortical Networks ◽  
Inhibitory Feedback ◽  
Inhibitory Population

AbstractNeurons within cortical microcircuits are interconnected with recurrent excitatory synaptic connections that are thought to amplify signals (Douglas and Martin, 2007), form selective subnetworks (Ko et al., 2011) and aid feature discrimination. Strong inhibition (Haider et al., 2013) counterbalances excitation, enabling sensory features to be sharpened and represented by sparse codes (Willmore et al., 2011). This “balance” between excitation and inhibition makes it difficult to assess the strength, or gain, of recurrent excitatory connections within cortical networks, which is key to understanding their operational regime and the computations they perform. Networks of neurons combining an unstable high-gain excitatory population with stabilizing inhibitory feedback are known as inhibition-stabilized networks (ISNs; Tsodyks et al. 1997). Theoretical studies using reduced network models predict that ISNs produce paradoxical responses to perturbation, but experimental perturbations failed to find evidence for ISNs in cortex (Atallah et al., 2012). We re-examined this question by investigating how cortical network models consisting of many neurons behave following perturbations, and found that results obtained from reduced network models fail to predict responses to perturbations in more realistic networks. Our models predict that a large proportion of the inhibitory network must be perturbed in order to robustly detect an ISN regime in cortex. We propose that wide-field optogenetic suppression of inhibition under a promoter targeting all inhibitory neurons may provide a perturbation of sufficient strength to reveal the operating regime of cortex. Our results suggest that detailed computational models of optogenetic perturbations are necessary to interpret the results of experimental paradigms.Significance statementMany useful computational mechanisms proposed for cortex require local excitatory recurrence to be very strong, such that local inhibitory feedback is necessary to avoid epileptiform runaway activity (an “inhibition-stabilized network” or “ISN” regime). However, recent experimental results suggest this regime may not exist in cortex. We simulated activity perturbations in cortical networks of increasing realism, and found that in order to detect ISN-like properties in cortex, large proportions of the inhibitory population must be perturbed. Current experimental methods for inhibitory perturbation are unlikely to satisfy this requirement, implying that existing experimental observations are inconclusive about the computational regime of cortex. Our results suggest that new experimental designs, targetting a majority of inhibitory neurons, may be able to resolve this question.

scholarly journals Estimation of ECHAM5 climate model closure parameters with adaptive MCMC

2010 ◽  
Vol 10 (5) ◽  
pp. 11951-11973
Author(s):  
H. Järvinen ◽  
P. Räisänen ◽  
M. Laine ◽  
J. Tamminen ◽  
A. Ilin ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Objective Function ◽  
Large Scale ◽  
Computational Models ◽  
Climate Models ◽  
Climate Model ◽  
Expert Knowledge ◽  
Adaptive Mcmc ◽  
Posterior Probability Density ◽  
Probability Densities

Abstract. Climate models contain closure parameters to which the model climate is sensitive. These parameters appear in physical parameterization schemes where some unresolved variables are expressed by predefined parameters rather than being explicitly modeled. Currently, best expert knowledge is used to define the optimal closure parameter values, based on observations, process studies, large eddy simulations, etc. Here, parameter estimation, based on the adaptive Markov chain Monte Carlo (MCMC) method, is applied for estimation of joint posterior probability density of a small number (n=4) of closure parameters appearing in the ECHAM5 climate model. The parameters considered are related to clouds and precipitation and they are sampled by an adaptive random walk process of the MCMC. The parameter probability densities are estimated simultaneously for all parameters, subject to an objective function. Five alternative formulations of the objective function are tested, all related to the net radiative flux at the top of the atmosphere. Conclusions of the closure parameter estimation tests with a low-resolution ECHAM5 climate model indicate that (i) adaptive MCMC is a viable option for parameter estimation in large-scale computational models, and (ii) choice of the objective function is crucial for the identifiability of the parameter distributions.

scholarly journals Neural System Identification with Cortical Information Flow

2019 ◽  
Author(s):  
L. Ambrogioni ◽  
K. Seeliger ◽  
U. Güçlü ◽  
M. A. J. van Gerven
Keyword(s):  
System Identification ◽  
Information Flow ◽  
Gradient Descent ◽  
Large Scale ◽  
Brain Regions ◽  
Neural System ◽  
Stochastic Gradient Descent ◽  
Neural Systems ◽  
Neural Computations ◽  
New Framework

AbstractCortical information flow (CIF) is a new framework for system identification in neuroscience. CIF models represent neural systems as coupled brain regions that each embody neural computations. These brain regions are coupled to observed data specific to that region. Neural computations are estimated via stochastic gradient descent. We show using a large-scale fMRI dataset that, in this manner, we can estimate models that learn meaningful neural computations. Our framework is general in the sense that it can be used in conjunction with any (combination of) neural recording techniques. It is also scalable, providing neuroscientists with a principled approach to make sense of the high-dimensional neural datasets.

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