Qualification: 
MCA, MSc, BSc
manjushanair@am.amrita.edu

Manjusha Nair currently serves as an Assistant Professor (Sr.Gr.) at the Department of Computer Science Applications at Amrita School of Engineering, Amritapuri.

Publications

Publication Type: Journal Article

Year of Publication Publication Type Title

2018

Journal Article

Manjusha Nair, Jinesh, M. K., Jayaraman, B., Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Temporal constrained objects for modelling neuronal dynamics”, PeerJ Computer Science, vol. 4, p. e159, 2018.[Abstract]


Background Several new programming languages and technologies have emerged in the past few decades in order to ease the task of modelling complex systems. Modelling the dynamics of complex systems requires various levels of abstractions and reductive measures in representing the underlying behaviour. This also often requires making a trade-off between how realistic a model should be in order to address the scientific questions of interest and the computational tractability of the model. Methods In this paper, we propose a novel programming paradigm, called \textit{temporal constrained objects,} which facilitates a principled approach to modelling complex dynamical systems. \textit{Temporal constrained objects} are an extension of \textit{constrained objects} with a focus on the analysis and prediction of the dynamic behaviour of a system. The structural aspects of a neuronal system are represented using objects, as in object-oriented languages, while the dynamic behaviour of neurons and synapses are modelled using declarative temporal constraints. Computation in this paradigm is a process of constraint satisfaction within a time-based simulation. Results We identified the feasibility and practicality in automatically mapping different kinds of neuron and synapse models to the constraints of \textit{temporal constrained objects}. Simple neuronal networks were modelled by composing circuit components, implicitly satisfying the internal constraints of each component and interface constraints of the composition. Simulations show that \textit{temporal constrained objects} provide significant conciseness in the formulation of these models. The underlying computational engine employed here automatically finds the solutions to the problems stated, reducing the code for modelling and simulation control. All examples reported in this paper have been programmed and successfully tested using the prototype language called TCOB. The code along with the programming environment are available at http://github.com/compneuro/TCOB_Neuron. Discussion \textit{Temporal constrained objects} provide powerful capabilities for modelling the structural and dynamic aspects of neural systems. Capabilities of the constraint programming paradigm, such as declarative specification, the ability to express partial information and non-directionality, and capabilities of the object-oriented paradigm especially aggregation and inheritance, make this paradigm the right candidate for complex systems and computational modelling studies. With the advent of multi-core parallel computer architectures and techniques or parallel constraint-solving, the paradigm of \textit{temporal constrained objects} lends itself to highly efficient execution which is necessary for modelling and simulation of large brain circuits.

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Publication Type: Conference Paper

Year of Publication Publication Type Title

2018

Conference Paper

Dr. Shyam Diwakar, Dr. Bipin G. Nair, Manjusha Nair, D, K., M, K., L., E., R, R., and N, N., “Design and Implementation of an Open-Source Browser-based Laboratory Platform for EEG Data Analysis”, in Proceedings of the Seventh International Conference on Advances in Computing, Communications and Informatics (ICACCI-2018), Bangalore, Karnataka, India, 2018.

2018

Conference Paper

Dr. Shyam Diwakar, Dr. Bipin G. Nair, Manjusha Nair, M, K., L, E., R, R., N, N., and D, K., “Experimental Recording and Computational Analysis of EEG signals for a Squeeze Task: Assessments and Impacts for Applications”, in Proceedings of the Seventh International Conference on Advances in Computing, Communications and Informatics (ICACCI-2018), Bangalore, Karnataka, India, 2018.

2017

Conference Paper

Manjusha Nair, Ushakumari, K., Ramakrishnan, A., Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Comparing parallel simulation of single and multi-compartmental spiking neuron models using gpgpu”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, 2017.[Abstract]


Characterizing neural responses and behavior require large scale simulation of brain circuits. Spatio-temporal information processing in large scale neural simulations often require compromises between computing resources and realistic details to be represented. In this work, we compared the implementations of point neuron models and biophysically detailed neuron models on serial and parallel hardware. GPGPU like architectures provide improved run time performance for multi compartmental Hodgkin-Huxley (HH) type neurons in a computationally cost effective manner. Single compartmental Adaptive Exponential Integrate and Fire (AdEx) model implementations, both in CPU and GPU outperformed embarrassingly parallel implementation of multi compartmental HH neurons. Run time gain of CPU implementation of AdEx cluster was approximately 10 fold compared to the GPU implementation of 10-compartmental HH neurons. GPU run time gain for Adex against GPU run time gain for HH was around 35 fold. The results suggested that careful selection of the neural model, capable enough to represent the level of details expected, is a significant parameter for large scale neural simulations.

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2017

Conference Paper

Manjusha Nair, Suresh, A. Puthenpeed, Manoharan, A., Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Information theoretic visualization of spiking neural networks”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, 2017.[Abstract]


Visualization is a flexible way to analyze simulated data and serves as a means for scientific discovery. Large scale neural simulations using high performance and distributed computing techniques produce huge amount of data for which visual analysis is generally difficult to perform. In this paper, a spiking neuron simulation environment was created to model and simulate networks of neurons of the cerebellum. Traditional visualization techniques were used to highlight relevant findings from small scale cerebellar networks. Time varying volume visualization using traditional techniques was found infeasible as network size increased. New data abstractions were required to depict the data that changes over time. With large scale cerebellar networks, Information theoretic methods were used to reduce dimensionality and to extract valuable information from data. We suggested that, information theory can be used as an efficient scientific data analysis and visualization tool to evaluate and validate computational models of cerebellar like structures.

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2016

Conference Paper

Chaitanya Nutakki, Nair, A., Chaitanya Medini, Manjusha Nair, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Computational reconstruction of fMRI-BOLD from neural activity”, in 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India, 2016.[Abstract]


In this paper, we model function magnetic resonance imaging signals generated by neural activity (fMRI). fMRI measures changes in metabolic oxygen in blood in brain circuits based on changes in biophysical factors like concentration of total cerebral blood flow, oxy-hemoglobin and deoxy-hemoglobin content. A modified version of the Windkessel model by incorporating compliance has been used with a balloon model to generate cerebellar granular layer and visual cortex blood oxygen-level dependent (BOLD) responses. Spike raster patterns were adapted from a biophysical granular layer model as input. The model fits volume changes in blood flow to predict the BOLD responses in the cerebellum granular layer and in visual cortex. As a comparison, we tested the balloon model and the modified Windkessel model with the mathematically reconstructed BOLD response under the same input condition. Delayed compliance contributed to BOLD signal and reconstructed signals were compared to experimental measurements indicating the usability of the approach. The current study allows to correlate dynamic changes of flow and oxygenation during brain activation which connects single neuron and network activity to clinical measurements.

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2016

Conference Paper

Chaitanya Medini, Thekkekuriyadi, A., Thayyilekandi, S., Manjusha Nair, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Modeling basal ganglia microcircuits using spiking neurons”, in 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India, 2016.[Abstract]


Basal ganglia and cerebellum have been implicated in critical roles related to control of voluntary motor movements for action selection and cognition. Basal ganglia primarily receive inputs from cortical areas as well as thalamic regions, and their functional architecture is parallel in nature which link several brain regions like cortex and thalamus. Striatum, substantia nigra, pallidum form different neuronal populations in basal ganglia circuit which were functionally distinct supporting sensorimotor, cognitive and emotional-motivational brain functions. In this paper, we have modelled and simulated basal ganglia neurons as well as basal ganglia circuit using integrate and fire neurons. Firing behaviour of subthalamic nucleus and global pallidus externa show how they modulate spike transmission in the circuit and could be used to model circuit dysfunctions in Parkinson's disease.

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2015

Conference Paper

Manjusha Nair, Madhu, P., Mohan, V., Rajendran, A. G., Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “GPGPU implementation of information theoretic algorithms for the analysis of granular layer neurons”, in 2015 International Conference on Computing and Network Communications (CoCoNet), IEEE, 2015.[Abstract]


Methods originally developed for communication systems are widely used in computational neuroscience to understand the information representation and processing performed by neurons and neural circuits in the brain. Information theoretic quantities Entropy and Mutual Information (MI) have been used in neuroscience as a metric to estimate the efficiency of information representation by neurons. These quantities are used here to measure the stimulus discrimination reliability of the cerebellar granule neurons using simulated response trains produced by a multi-compartmental model of Wistar rat neuron. With  1011 granule neurons in the cerebellum, understanding spatio-temporal processing in such structures demands efficient, fast algorithms. Since the serial version of the algorithm had multiple estimation loops which increased the process time considerably with the problem size, we re-implemented the MI algorithm in GPGPU hardware as an efficient way of parallelizing the MI computations. Task-level parallelism and GPU optimizations were used to improve the process time. Estimates on GPGPUs showed 15X time efficiency compared to the CPU version of the algorithm. In order to understand learning inside the cerebellar circuit, synaptic plasticity conditions were simulated in the neuron model. We were able to quantify the stimulus discrimination reliability of granule neurons under control, LTP and LTD conditions and the analysis revealed that stimulus discrimination capability of the neuron was increased during high plasticity state.

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2015

Conference Paper

Manjusha Nair, Surya, S., Kumar, R. S., Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Efficient simulations of spiking neurons on parallel and distributed platforms: Towards large-scale modeling in computational neuroscience”, in 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS), Trivandrum, India, 2015.[Abstract]


Human brain communicates information by means of electro-chemical reactions and processes it in a parallel, distributed manner. Computational models of neurons at different levels of details are used in order to make predictions for physiological dysfunctions. Advances in the field of brain simulations and brain computer interfaces have increased the complexity of this modeling process. With a focus to build large-scale detailed networks, we used high performance computing techniques to model and simulate the granular layer of the cerebellum. Neuronal firing patterns of cerebellar granule neurons were modeled using two mathematical models Hodgkin-Huxley (HH) and Adaptive Exponential Leaky Integrate and Fire (AdEx). The performance efficiency of these modeled neurons was tested against a detailed multi-compartmental model of the granule cell. We compared different schemes suitable for large scale simulations of cerebellar networks. Large networks of neurons were constructed and simulated. Graphic Processing Units (GPU) was employed in the pleasantly parallel implementation while Message Passing Interface (MPI) was used in the distributed computing approach. This allowed to explore constraints of different parallel architectures and to efficiently load balance the tasks by maximally utilizing the available resources. For small scale networks, the observed absolute speedup was 6X in an MPI based approach with 32 processors while GPUs gave 10X performance gain compared to a single CPU implementation. In large networks, GPUs gave approximately 5X performance gain in processing time compared to the MPI implementation. The results enabled us to choose parallelization schemes suitable for large-scale simulations of cerebellar circuits. We are currently extending the network model based on large scale simulations evaluated in this paper and using a hybrid - heterogeneous MPI based multi-GPU architecture for incorporating millions of cerebellar neurons for assessing physiolo- ical disorders in such circuits.

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2015

Conference Paper

A. G. Rajendran, Manjusha Nair, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Analyzing Mutual Information and Synaptic Efficacy at Mossy Fiber –Granule Cell Relay in Rat Cerebellum”, in Proceedings of the International symposium on Translational Neuroscience & XXXIII Annual Conference of the Indian Academy of Neurosciences, Panjab University, Chandigarh , 2015.

2014

Conference Paper

Manjusha Nair, Subramanyan, K., Dr. Shyam Diwakar, and Dr. Bipin G. Nair, “Parameter optimization and nonlinear fitting for computational models in neuroscience on GPGPUs”, in International Conference on High Performance Computing and Applications (ICHPCA), 2014 , C. V. Raman College of Engineering, Bhubaneswar, 2014.[Abstract]


One of the main challenges in computational modeling of neurons is to reproduce the realistic behaviour of the neurons of the brain under different behavioural conditions. Fitting electrophysiological data to computational models is required to validate model function and test predictions. Various tools and algorithms exist to fit the spike train recorded from neurons to computational models. All these require huge computational power and time to produce biologically feasible results. Large network models rely on the single neuron models to reproduce population activity. A stochastic optimization technique called Particle Swam Optimisation (PSO) was used here to fit spiking neuron model called Adaptive Exponential Leaky Integrate and Fire (AdEx) model to the firing patterns of different types of neurons in the granular layer of the cerebellum. Tuning a network of different types of spiking neurons is computationally intensive, and hence we used Graphic Processing Units (GPU) to run the parameter optimisation of AdEx using PSO. Using the basic principles of swam intelligence, we could optimize the n-dimensional space search of the parameters of the spiking neuron model. The results were significant and we observed a 15X performance in GPU when compared to CPU. We analysed the accuracy of the optimization process with the increase in width of the search space and tuned the PSO algorithm to suit the particular problem domain. This work has promising roles towards applied modeling and can be extended to many other disciplines of model-based predictions.

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PDF iconparameter-optimization-and-nonlinear-fitting-for-computational-models-in-neuroscience-on-gpgpus.pdf

2014

Conference Paper

Manjusha Nair, G., R. A., Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Analysis and Quantification of Neural Information Processing during Cerebellar Plasticity”, in Proceedings of the International symposium on Translational Neuroscience & XXXII Annual Conference of the Indian Academy of Neurosciences, NIMHANS, Bangalore , India, 2014.

2014

Conference Paper

Manjusha Nair, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Large-Scale Simulations of Cerebellar Microcircuit Relays using Spiking Neuron on GPUs.”, in Proceedings of the Eleventh International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, University of Cambridge, Cambridge, UK, 2014.[Abstract]


This paper uses scientific computing techniques in understanding cerebellar networks and attempts to model functional behavior of circuits via computational neuroscience. Since highly parallel programmable processors like Graphic Processing Units (GPUs) deliver a high compute capacity at low cost, we modeled granular layer neurons of the rat cerebellum on GPUs and reconstructed a network microcircuit of granular layer for predicting computational properties in such circuits. The main objective of this work was to reconstruct models apt for large scale simulations, involving thousands of neurons while maintaining an acceptable degree of biological details. The hypothesis relating to spatio-temporal information processing in the input layer of the cerebellum has been tested using mathematical modeling. The role of mossy fiber excitation and the modulatory role of Golgi cell inhibition on the granule cells were analyzed. A scalable network consisting of up to 2 million neurons were simulated in millisecond time-scale and the performance efficiency of GPUs over CPUs was compared. The main goal was to understand the scalability issues while implementing such large scale networks and to optimize shared memory access. In GPUs, multi-core parallelization allowed efficient management of computational overheads imposed by synaptic dynamics. We also briefly investigated the performance improvements focusing on decreasing memory access times and the role of optimization techniques. This work is a proof-of-concept implementation apt for densely-packed microcircuits of electrotonically compact neurons with targets to optimize real-time performance and scalability.

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2013

Conference Paper

Manjusha Nair, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Small Scale Modeling of Cerebellar Networks Using GPUs”, in International Conference on Biotechnology for innovative applications, Amrita Vishwa Vidyapeetham, Kerala, 2013.

2012

Conference Paper

Manjusha Nair and Dr. Shyam Diwakar, “Quantifying stimulus information in spike trains during plasticity”, in INCF workshop, India, 2012.

2011

Conference Paper

Manjusha Nair, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Information Coding in Single Granule Neuron of the Cerebellum”, in Proceedings of the International symposium on `Recent Trends in Neurosciences & XXIX Annual Conference of Indian Academy of Neurosciences, 2011.

2011

Conference Paper

Dr. Bipin G. Nair, Dr. Shyam Diwakar, H, P., C, M., Manjusha Nair, N, M., G, N., and E, D. ’A., “Modeling evoked local field potentials in the cerebellum granular layer and plasticity changes reveal single neuron effects in neural ensembles”, in Acta Physiologica, 2011.

Faculty Research Interest: 
207
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OFFERED
5
AMRITA
CAMPUSES
15
CONSTITUENT
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GRADE BY
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8th
RANK(INDIA):
NIRF 2018
150+
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