Ph.D, MSc

Dr. Asha Vijayan currently serves as Assistant Professor at the School of Biotechnology. She received her masters in Bioinformatics, Amrita Vishwa Vidyapeetham, Amritapuri in 2009.


B. Sc. Biotechnology, Chemistry, Zoology (triple main) from Fatima Mata National College, Kollam
M. Sc. Bioinformatics from Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Kollam
Ph. D., Amrita Vishwa Vidyapeetham

Earlier Affiliation

Teaching Assistant, Amrita School of Biotechnology (09/09/09-30/06/10)
Junior Lecturer, Amrita School of Biotechnology (01/07/10-01/07/12)
Lecturer, Amrita School of Biotechnology (01/07/12-01/01/14)
Senior Lecturer, Amrita School of Biotechnology (01/01/14 - 01/07/15)



  1. BIF409/ Structural Bioinformatics/ M.Sc Bioinformatics/ 4 Credits/ Semester 2
  2. CBB301/ Introductory Bioinformatics/ B.Sc. Biotechnology and B.Sc. Microbiology/ 2 Credits/ Semester 6


  1. BIF514/ Bioinformatics/ M.Sc. Biotechnology/ 3 Credits/ Semester 3
  2. BIF410/ Introduction to Bioinformatics/ M.Sc. Bioinformatics/ 3 Credits/ Semester 1
  3. BIF487/ Programming for Bioinformatics/ M.Sc. Bioinformatics/ 1 Credit/ Semester 1
  4. BIF551/ Systems Biology/ M.Sc. Bioinformatics/ 2 Credits/ Semester 3


  1. Bioinformatics for MCA and B. Tech. final years at Amrita School of Engineering


Publication Type: Journal Article

Year of Publication Title


Dr. Bipin G. Nair, Arathi G. Rajendran, Asha Vijayan, Chaitanya Medini, and Dr. Shyam Diwakar, “Computational modelling of cerebellum granule neuron temporal responses for auditory and visual stimuli”, International Journal of Advanced Intelligence Paradigms, vol. Vol.18 No.3, pp.356 - 372, 2021.[Abstract]

Sensorimotor signals from the cerebral cortex modulate the pattern generating metaheuristic capabilities of cerebellum. To better understand the functional integration of multisensory information by the single granule neurons and the role of multimodal information in motor guidance of cerebellum, we have modelled granular layer microcircuit in the cerebellum and analysed the encoding of information during the auditory and visual stimuli. A multi-compartmental granule neuron model comprising of excitatory and inhibitory synapses was used and in vivo like behaviour was modelled with the short and long bursts. The change in intrinsic parameters in the model helped to quantify the effect of spike-time dependent plasticity in the firing of granule neurons. Computer simulations implicate coding correlation of output patterns to temporal excitatory stimuli. We observed the role of induced plasticity and granular layer role in sparse recoding of auditory and visual inputs and the model predict how plasticity mechanisms affect the average amount of information transmitted through the single granule neurons during multimodal stimuli.

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Dr. Shyam Diwakar, Dr. Bipin G. Nair, Krishna Chaitanya Medini, Asha Vijayan, and Arathi G. Rajendran, “Computational Modelling of Cerebellum Granule Neuron Temporal Responses for Auditory and Visual Stimuli”, International Journal of Advanced Intelligence Paradigms, vol. 10, p. 1, 2018.


Asha Vijayan, Chaitanya Nutakki, Dhanush Kumar, Dr. Krishnashree Achuthan, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Enabling a freely accessible open source remotely controlled robotic articulator with a neuro-inspired control algorithm”, International Journal of Interactive Mobile Technologies, vol. 13, no. 1, pp. 61-75, 2017.[Abstract]

Internet-enabled technologies for robotics education are gaining importance as online platforms facilitating and promoting skill training. Understanding the use and design of robotics is now introduced at university undergraduate levels, but in developing economies establishing usable hardware and software platforms face several challenges like cost, equipment etc. Remote labs help providing alternatives to some of the challenges. We developed an online laboratory for bioinspired robotics using a low-cost 6 degree-of-freedom robotic articulator with a neuro-inspired controller. Cerebellum-inspired neural network algorithm approximates forward and inverse kinematics for movement coordination. With over 210000 registered users, the remote lab has been perceived as an interactive online learning tool and a practice platform. Direct feedback from 60 students and 100 university teachers indicated that the remote laboratory motivated self-organized learning and was useful as teaching material to aid robotics skill education.

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Asha Vijayan, Chaitanya Nutakki, Chaitanya Medini, Hareesh Singanamala, Dr. Bipin G. Nair, Krishnasree Achuthan, and Dr. Shyam Diwakar, “Classifying Movement Articulation for Robotic Arms via Machine Learning”, Journal of Intelligent Computing, vol. 4, no. 3, pp. 123-134, 2013.[Abstract]

Articulation via target-oriented approaches have been commonly used in robotics. Movement of a robotic arm can involve targeting via a forward or inverse kinematics approach to reach the target. We attempted to transform the task of controlling the motor articulation to a machine learning approach. Towards this goal, we built an online robotic arm to extract articulation datasets and have used SVM and Naïve Bayes techniques to predict multi-joint articulation. For controlling the preciseness and efficiency, we developed pick and place tasks based on pre-marked positions and extracted training datasets which were then used for learning. We have used classification as a scheme to replace prediction-correction approach as usually attempted in traditional robotics. This study reports significant classification accuracy and efficiency on real and synthetic datasets generated by the device. The study also suggests SVM and Naive Bayes algorithms as alternatives for computational intensive prediction-correction learning schemes for articulator movement in laboratory environments.

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Publication Type: Conference Paper

Year of Publication Title


Hemalatha Sasidharakurup, Pyaree Dash, Asha Vijayan, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Computational modelling of apoptosis in parkinson's disease using biochemical systems theory”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, 2017.[Abstract]

In this study, we present a computational model of Parkinson's disease (PD) that includes different biological interactions that leads to neural cell death with the use of biochemical systems theory. The model incorporates a set of important pathways in PD including dopaminergic pathway, mitochondrial pathway and P53 - DNA damage pathway. Modeling signaling pathways and simulations were performed using biochemical systems theory. Initial concentrations have been taken from experimental data in literature and were used to model the changes. Results generated by dopaminergic diseased pathway show 45% decrease in dopamine, compared to normal condition. In addition, the activity of MOMP, Caspase 9 and Apoptosome expression in diseased condition within mitochondrial pathway model have been observed in the results. The expression levels of BAX and MOMP were reconstructed and simulations suggest oligomerization of BAK leads to the elevation of MOMP. An increase in oxidative stress and apoptosis level also has been observed in the PD condition, compared to the control allowing comparisons between normal and diseased conditions with these mathematical models.

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Asha Vijayan, Vivek Gopan, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Comparing robotic control using a spiking model of cerebellar network and a gain adapting forward-inverse model”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, 2017.[Abstract]

Internal models inspired from the functioning of cerebellum are being increasingly used to predict and control movements of anthropomorphic manipulators. A major function of cerebellum is to fine tune the body movements with precision and are comparative to capabilities of artificial neural network. Several studies have focused on encoding the real-world information to neuronal responses but temporal information was not given due importance. Spiking neural network accounts to conversion of temporal information into the adaptive learning process. In this study, cerebellum like network was reconstructed which encodes spatial information to kinematic parameters, self-optimized by learning patterns as seen in rat cerebellum. Learning rules were incorporated into our model. Performance of the model was compared to an optimal control model and have evaluated the role of bioinspired models in representing inverse kinematics through applications to a low cost robotic arm developed at the lab. Artificial neural network of Kawato was used to compare with our existing model because of their similarity to biological circuit as seen in a real brain. Kawato's paired forward inverse model has used to train for fast movement based tasks which resembles human based motor tasks. Result suggest kinematics of a 6 DOF robotic arm was internally represented and this may have potential application in neuroprosthesis.

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Chaitanya Nutakki, Asha Vijayan, Hemalata Sasidharakurup, Dr. Bipin G. Nair, Dr. Krishnashree Achuthan, and Dr. Shyam Diwakar, “Low-Cost Robotic Articulator as an Online Education tool: Design, Deployment and Usage”, in Proceedings of IEEE International Conference on Robotics and Automation for Humanitarian Applications, Amrita Vishwa Vidyapeetham, Kollam, Kerala, 2016.[Abstract]

Humanitarian challenges in developing nations such as low cost prosthesis for the physically challenged, have also led to substantial progress in robotics. In this paper, we implemented and deployed a low-cost remotely controlled robotic articulator, as an education tool for university students and teachers. This tool is freely available online and is being employed to generate robotic datasets for novel algorithms. Using a server-client methodology and a browser-based user interface, the online lab allows learners to access and perform basic kinematics experiments and study robotic articulation. These experiments were developed for allowing students to enhance laboratory skills in robotics and improve practical experience without concerns for equipment access restrictions or cost.

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Chaitanya Nutakki, Asha Vijayan, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Implementing Cerebellar Biophysics for Trajectory planning in Robotic arms”, in Proceedings of the International symposium on Translational Neuroscience & XXXIII Annual Conference of the Indian Academy of Neurosciences, Panjab University, Chandigarh , India, 2015.


Chaitanya Medini, Asha Vijayan, Ritu Maria Zacharia, Lekshmi Priya Rajagopal, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Spike Encoding for Pattern Recognition: Comparing Cerebellum Granular Layer Encoding and BSA algorithms”, in Proceedings of the Fourth International Conference on Advances in Computing, Communications and Informatics (ICACCI-2015), Kochi, India, 2015.[Abstract]

Spiking neural encoding models allow classification of real world tasks to suit for brain-machine interfaces in addition to serving as internal models. We developed a new spike encoding model inspired from cerebellum granular layer and tested different classification techniques like SVM, Naïve Bayes, MLP for training spiking neural networks to perform pattern recognition tasks on encoded datasets. As a precursor to spiking network-based pattern recognition, in this study, real world datasets were encoded into spike trains. The objective of this study was to encode information from datasets into spiking neuron patterns that were relevant for spiking neural networks and for conventional machine learning algorithms. In this initial study, we present a new approach similar to cerebellum granular layer encoding and compared it with BSA encoding techniques. We have also compared the efficiency of the encoded dataset with different datasets and with standard machine learning algorithms. © 2015 IEEE.

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Asha Vijayan, Chaitanya Medini, Anjana Palolithazhe, Bhagyalakshmi Muralidharan, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Modeling Pattern Abstraction in Cerebellum and Estimation of Optimal Storage Capacity”, in Proceedings of the Fourth International Conference on Advances in Computing, Communications and Informatics (ICACCI-2015), Kochi, India, 2015.[Abstract]

Precise fine-tuning of motor movements has been known to be a vital function of cerebellum, which is critical for maintaining posture and balance. Purkinje cell (PC) plays a prominent role in this fine-tuning through association of inputs and output alongside learning through error correction. Several classical studies showed that PC follows perceptron like behavior, which can be used to develop cerebellum like neural circuits to address the association and learning. With respect to the input, the PC learns the motor movement through update of synaptic weights. In order to understand how cerebellar circuits associate spiking information during learning, we developed a spiking neural network using adaptive exponential integrate and fire neuron model (AdEx) based on cerebellar molecular layer perceptron-like architecture and estimated the maximal storage capacity at parallel fiber-PC synapse. In this study, we explored information storage in cerebellar microcircuits using this abstraction. Our simulations suggest that perceptron mimicking PC behavior was capable of learning the output through modification via finite precision algorithm. The study evaluates the pattern processing in cerebellar Purkinje neurons via a mathematical model estimating the storage capacity based on input patterns and indicates the role of sparse encoding of granular layer neurons in such circuits. © 2015 IEEE.

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Dr. Shyam Diwakar, Sandeep Bodda, Chaitanya Nutakki, Asha Vijayan, Dr. Krishnashree Achuthan, and Dr. Bipin G. Nair, “Neural Control using EEG as a BCI Technique for Low Cost Prosthetic Arms”, in In Proceedings of the International Conference on Neural Computation Theory and Applications (NCTA-2014), Rome, Italy, 2014.[Abstract]

There have been significant advancements in brain computer interface (BCI) techniques using EEG-like methods. EEG can serve as non-invasive BMI technique, to control devices like wheelchairs, cursors and robotic arm. In this paper, we discuss the use of EEG recordings to control low-cost robotic arms by extracting motor task patterns and indicate where such control algorithms may show promise towards the humanitarian challenge. Studies have shown robotic arm movement solutions using kinematics and machine learning methods. With iterative processes for trajectory making, EEG signals have been known to be used to control robotic arms. The paper also showcases a case-study developed towards this challenge in order to test such algorithmic approaches. Non-traditional approaches using neuro-inspired processing techniques without implicit kinematics have also shown potential applications. Use of EEG to resolve temporal information may, indeed, help understand movement coordination in robotic arm.

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Chaitanya Medini, Asha Vijayan, Egidio D'Angelo, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Computationally Efficient Bio-realistic Reconstructions of Cerebellar Neuron Spiking Patterns”, in Proceedings of the 2014 International Conference on Interdisciplinary Advances in Applied Computing, Amrita Vishwa Vidyapeetham, Coimbatore, India, 2014.[Abstract]

Simple spiking models have been known to replicate detailed mathematical models firing properties with reliable accuracy in spike timing. We modified the adaptive exponential integrate and fire mathematical model to reconstruct different cerebellar neuronal firing patterns. We were able to reconstruct the firing dynamics of various types of cerebellar neurons and validated with previously published experimental studies. To model the neurons, we exploited particle swarm optimization to fit the parameters. The study showcases the match of electroresponsiveness of the neuronal models to data from biological neurons. Results suggest that models are close reconstructions of the biological data since frequency and spike-timing closely matched known values and were similar to those in previously published detailed computationally intensive biophysical models. Such spiking models have a number of applications including design of large-scale circuit models in order to understand physiological dysfunction and for various computational advantages.

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Asha Vijayan, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “A cerebellum-like approach for neuromorphic hardware based on bio-realistic model of cerebellar microcircuitry”, in Proceedings of International workshop on Hippocampus: From synapses to behaviour, INCF workshop - IISER, Pune, India, 2013.


Asha Vijayan, Chaitanya Medini, Hareesh Singanamala, Chaitanya Nutakki, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Classification of robotic arm movement using SVM and Naïve Bayes classifiers”, in Proceedings of Third International Conference on Innovative Computing Technology (INTECH 2013), London, 2013.[Abstract]

Target-oriented approaches have been commonly used in robotics. In 3D space, movement of a robotic arm depends on the target position which can either follow a forward or inverse kinematics approach to reach the target. Predicting the movement of a robotic arm requires prior learning through methods such as transformation matrices or other machine learning techniques. In this paper, we built an online robotic arm to extract movement datasets and have used machine learning algorithms to predict robotic arm articulation. For efficient training, small training datasets were used for learning purpose. Classification is used as a scheme to replace prediction-correction approach and to test whether the method can function as a replacement of usual forward kinematics schemes or predictor-corrector methods in directing a remotely controlled robotic articulator. This study reports significant classification accuracy and efficiency on real and synthetic datasets generated by the device. The study also suggests linear SVM and Naïve Bayes algorithms as alternatives for computational intensive learning schemes while predicting articulator movement in laboratory environments.

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PDF iconclassification-of-robotic-arm-movement-using-svm-and-naïve-bayes-classifiers.pdf


G. L. Sairam, Asha Vijayan, Gerald H Lushington, and Mahesh Visvanathan, “Evol optimer- tool for gene expression analysis”, in Proc. IEEE Conference on Bioinformatics and Biomedicine Workshop (BIBMW), 2010, pp. 169-172.


Asha Vijayan, Bessey Elen Skariah, Dr. Bipin G. Nair, Gerald H Lushington, Shabarinath Subramaniam, and Mahesh Visvanathan, “PathMapper-an integrative approach for oncogene pathway identification”, in IEEE International Conference on Bioinformatics and Biomedicine Workshop, 2009. BIBMW 2009., Washington, DC, 2009.[Abstract]

Although generation of high-throughput expression data is becoming customary, the fast, easy, and methodical presentation and analysis of these data in a biological context is still an obstruction. To tackle this necessity we have developed PathMapper, a standalone application which maps expression profiles of genes or proteins concurrently onto major, currently available regulatory, metabolic and cellular pathways. PathMapper automatically predicts protein functions directly from genes and can systematically identify differences between metabolic pathways and map genes onto pathways. MYSQL database is used to store, query, and manipulate the large amounts of data that are involved. PathMapper allows its users to (i) upload microarray data into a database; (ii) integrate gene expression with enzymes of the pathways; (iii) generate pathway diagrams (iv) visualize gene expression for each pathway. A graphical pathway represe- ntation permits the visualization of the expressed genes in a functional context. Based on publicly available pathway databases, PathMapper can be adapted to any organism and is currently available for human, mouse and rat genome arrays. About 20% of the probe sets of each array have been assigned to EC numbers by homology relationship and linked to its corresponding metabolic pathways. This tool can be downloaded and evaluated using the following Web link : (http://parasakti.amrita.ac.in/~amm07bi008/PathMapper). More »»

Publication Type: Conference Proceedings

Year of Publication Title


L. Ramakrishnan, Aarathi Krishna, Asha Vijayan, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Implementing cerebellar neural network in FPGA”, International symposium on Translational Neuroscience & XXXII Annual Conference of the Indian Academy of Neurosciences. NIMHANS, Bangalore, India, 2014.


Asha Vijayan, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Inverse Kinematics with cerebellar spiking neurons”, Amrita Bioquest . 2013.


Asha Vijayan and Dr. Shyam Diwakar, “Cerebellar neural dynamics with spiking neurons show generalization for inverse kinematics problem, INCF workshop”, INCF workshop. 2012.