Shailesh.Appukuttanappukuttan.shailesh@gmail.com
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ABOUT

PERSONAL DETAILS
1 Avenue de la Terrasse, 91190 Gif-sur-Yvette
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shailesh.appukuttan@unic.cnrs-gif.fr
I am interested in the application of computational techniques to biological research. Currently, I am employed in the Human Brain Project (HBP). My work involves the design and development of a model validation framework for neuroscience, and its integration into existing model development workflows.

BIO-1

ABOUT ME

I did my undergraduate studies in computer science, and then worked for a year in the IT industry, before rejoining academia. In August 2015, I completed my integrated Masters-PhD in biomedical engineering. My doctoral work involved the modeling of 3-D syncytium of smooth muscle networks to understand their functioning and control by the central and peripheral nervous systems. We collaborated with an experimental lab on the same.

Alongside, to broaden my knowledge, I took up projects such as modeling of Callosal neurons and also assisted a colleague in modeling Medium Spiny Neurons. Being a computer science engineer who moved to biosciences, I would position myself as a researcher who is well equipped on the computational and mathematical aspects. To strengthen my biological understanding, I volunteered and was selected for teaching assistantship in relevant courses such as Biopotentials, Bioelectricity and Advanced Cellular Electrophysiology. Alongside, I had been the head teaching assistant for a course on virtual instrumentation for three years. This led to the development of a set of four tutorials which are now available as mini e-books on Amazon.

Currently, I am a postdoctoral researcher in the Neuroinformatics Group (led by Dr. Andrew Davison) at UNIC, CNRS (Paris) working on the Human Brain Project. My work involves the design and development of a model validation framework for neuroscience, and its integration into existing model development workflows. Alongside, I am also involved in the development of PyNN, a simulator-independent language for building neuronal network models.

BIO-2

EXTRACURRICULAR ACTIVITIES

2009-2015 : Won Five Football Tournaments at IIT Bombay
2010-2012 : Served as Departmental Student Council Web Secretary
2009-2010 : Best Defender Award at IFL, IIT Bombay
2007-2008 : Served as Sports Secretary of SIES Graduate School of Technology
2007-2008 : Helped conduct IEEE Technical Festival and Cultural Festival
2006-2007 : Served as Deputy Sports Secy of SIES Graduate School of Technology
2006-2008 : Organized SIES GST Sports Festival – Lakshya ’07, ‘08
2005-2008 : Captained SIES Graduate School of Technology Football Team
MEMBERSHIP
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RESUME

ACADEMIC AND PROFESSIONAL POSITIONS
  • 2017
    Date
    Gif-sur-Yvette, France

    Postdoctoral Researcher

    Université Paris-Saclay, CNRS, Institut des Neurosciences Paris-Saclay

    • Design and development of EBRAINS Live Paper platform • Design and development of model validation framework • Developing multi-simulator model representation format
  • 2016
    2016
    Mumbai, India

    Visiting Faculty

    NMIMS SD-School Of Science

    • Invited to teach an undergraduate course on Linux & R • 15 hours of lectures + 60 hours of practicals
  • 2015
    2016
    Mumbai, India

    Research Associate

    IIT Bombay

    • Continued on my doctoral research project • Parallely initiating a project on Parkinson’s Disease
  • 2015
    2009
    Mumbai, India

    Doctoral Student

    IIT Bombay

    • Integrated Masters + PhD • Computational modeling of bladder smooth muscle
  • 2009
    2011
    Navi Mumbai, India

    Co-founder and Technical Head

    TechShiksha

    • Educational initiative to instill scientific thinking in children • Worked with government, private schools and NGOs
  • 2008
    2009
    Mumbai, India

    Technical Associate

    TechMahindra

    • Worked on Siebel platform on a project for British Telecom • Topped both 'Induction training' and 'Siebel training'
EDUCATION
  • 2009
    2015
    Mumbai, India

    Integrated Masters + Ph.D. − Biomedical Engineering

    Indian Institute of Technology

    Obtained: CPI 9.52/10.00 GATE 2009 CS Rank: 239, Percentile: 99.43
  • 2004
    2008
    Navi Mumbai, India

    Bachelor of Engineering − Computer Science

    SIES Graduate School of Technology

    Obtained: 69.15%
  • 2002
    2004
    Mumbai, India

    Intermediate /+2 − Computer Science

    Atomic Energy Junior College

    Obtained: 90.33%
  • 2001
    2002
    Mumbai, India

    Matriculation

    Atomic Energy Central School -2

    Obtained: 89.90%
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PROJECTS

RESEARCH PROJECTS
  • 2018
    now
    Gif-sur-Yvette, France

    EBRAINS Live Papers

    Université Paris-Saclay, CNRS, Institut des Neurosciences Paris-Saclay

    Position: Postdoctoral Researcher
    Mentor: Dr. Andrew Davison, Neuro-PSI (UNIC), CNRS

    Funding agency: European Union’s Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreements No. 785907 and No. 945539 (Human Brain Project SGA2 and SGA3)

    Computational approaches to neuroscience, such as development of models and data analysis, lack an established system for distributing code, data and other related resources. The absence of such a system significantly diminishes the utility of scientific outputs such as published models and datasets within the neuroscience community, and also hinders the reproducibility of data analyses. This has also severely impeded the promotion and progress of community-based, collaborative modelling efforts. We have developed an online platform, called EBRAINS Live Papers, for sharing scientific resources in neuroscience. It aims to enable researchers to easily access the data resources employed in published studies and understand in detail the provenance of published results and figures.
  • 2017
    now
    Gif-sur-Yvette, France

    Model Representation and Standards

    Université Paris-Saclay, CNRS, Institut des Neurosciences Paris-Saclay

    Position: Postdoctoral Researcher
    Mentor: Dr. Andrew Davison, Neuro-PSI (UNIC), CNRS

    Funding agency: European Union’s Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreements No. 720270 and No. 785907 (Human Brain Project SGA1 and SGA2)

    Work with community partners and with model developers to adopt and/or develop standards for representing brain models, and for sharing them. Specifically, develop open APIs for morphology analysis, classification, conversion, and manipulation; establish open standards for performant, interoperable model representations (from the cellular-level to the point neuron levels); develop open APIs to access information on models; and work to create representations for models of synaptic plasticity. Work with others to ensure that the APIs and model representations are consistent with high performance on both traditional HPC and neuromorphic computing systems. As far as possible use and/or extend existing community standards (e.g. NeuroML, NineML, PyNN).
  • 2017
    now
    Gif-sur-Yvette, France

    Validation Framework Services and Apps

    Université Paris-Saclay, CNRS, Institut des Neurosciences Paris-Saclay

    Position: Postdoctoral Researcher
    Mentor: Dr. Andrew Davison, Neuro-PSI (UNIC), CNRS

    Funding agency: European Union’s Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreements No. 720270 and No. 785907 (Human Brain Project SGA1 and SGA2)

    Develop services and apps facilitating community model-building and community validation of models, and use them to perform preliminary validations of these models. The work to be performed includes enhancements of the existing validation framework in collaboration with corresponding other HBP tasks; tools facilitating web access to validation experiments and their results; and outreach activities. In addition, work actively to build community support with a special emphasis on validation experiments coming from the community. The apps developed will make a fundamental contribution to community-driven modelling, and will encourage community-driven app development.
  • 2010
    2016
    Mumbai, India

    Computational Modeling of Bladder Smooth Muscle

    IIT Bombay

    Position: Research Scholar
    Mentor: Prof. Rohit Manchanda, IIT Bombay

    Funding agency: Department of Biotechnology (DBT), India [BT/PR12973/MED/122/47/2016] and the UK-India Education and Research Initiative (UKIERI) [UKUTP20110055]

    Certain smooth muscles, such as the detrusor of the urinary bladder, exhibit a variety of spikes that differ markedly in their amplitudes and time courses. The origin of this diversity is poorly understood but is often attributed to the syncytial nature of smooth muscle and its distributed innervation.
    In order to help clarify such issues, we developed a three-dimensional electrical model of syncytial smooth muscle implemented using the compartmental modeling technique, with special reference to the bladder detrusor. Values of model parameters were sourced or derived from experimental data. The model was validated against various modes of stimulation employed experimentally and the results were found to accord with both theoretical predictions and experimental observations. Model outputs also satisfied criteria characteristic of electrical syncytia, such as correlation between the spatial spread and temporal decay of electrotonic potentials as well as positively skewed amplitude frequency histogram for sub-threshold potentials, and lead to interesting conclusions.
    Based on analysis of syncytia of different sizes, it was found that a size of 21-cube may be considered the critical minimum size for an electrically infinite syncytium. Set against experimental results, we conjecture the existence of electrically sub-infinite bundles in the detrusor. Moreover, the absence of coincident activity between closely spaced cells potentially implies, counter-intuitively, highly efficient electrical coupling between such cells. The model thus provided a heuristic platform for the interpretation of electrical activity in syncytial tissues.
    A number of enhancements and extensions to the basic model had been developed and discussed, to obtain a better understanding of syncytial tissues. These include the investigation of initiation, propagation and modulation of action potentials in a syncytium and the development of a physiologically more realistic gap junction model. Relevance to physiological function were discussed, and their implications assessed, at each stage.
COURSE PROJECTS
  • 2010
    2010
    Mumbai, India

    Virtual Instrumentation for Real Time ECG Signal Processing

    IIT Bombay

    Guide: Prof. Soumyo Mukherji, IIT Bombay
    Topped course & awarded ‘AA’ grade; Appointed Head Teaching Assistant
  • 2010
    2010
    Mumbai, India

    Molecular Classification of Cancer by Gene Expression Monitoring

    IIT Bombay

    Guide: Prof. Subramani Arunkumar, IIT Bombay
    Seminar on same topic awarded ‘AA’ grade
  • 2010
    2010
    Mumbai, India

    Micro-machined Retinal Prosthesis

    IIT Bombay

    Guide: Prof. Rohit Srivastava, IIT Bombay
    Awarded ‘AA’ grade in course
  • 2009
    2009
    Mumbai, India

    Computational Methods for Cancer Gene Identification and Classification

    IIT Bombay

    Guide: Prof. Pramod Wangikar, IIT Bombay
    Selected as one of the best projects & awarded ‘AA’ grade
  • 2007
    2008
    Navi Mumbai, India

    Commutation Information System (CIS)

    SIES Graduate School of Technology

    Guide: Prof. Aparna Bannore, SIES Graduate School of Technology
    Worked with support from local transport networks
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PUBLICATIONS

PUBLICATIONS LIST
05 Oct 2021

EBRAINS Live Papers – Interactive resource sheets for computational studies in neuroscience

OSF Preprints

Appukuttan, S., Bologna, L. L., Migliore, M., Schürmann, F., & Davison, A. P. (2021). EBRAINS Live Papers-Interactive resource sheets for computational studies in neuroscience. OSP Preprints: https://doi.org/10.31219/osf.io/4uvdy.

Journal Paper Selected Appukuttan, S., Bologna, L. L., Migliore, M., Schürmann, F., & Davison, A. P.

EBRAINS Live Papers – Interactive resource sheets for computational studies in neuroscience

Appukuttan, S., Bologna, L. L., Migliore, M., Schürmann, F., & Davison, A. P.
Journal Paper Selected
About The Publication
We present here an online platform for sharing resources underlying publications in neuroscience. It enables authors to easilyupload and distribute digital resources, such as data, code, and notebooks, in a structured and systematic way. Interactivity is aprominent feature of the Live Papers, with features to download, visualise or simulate data, models and results presented in thecorresponding publications. The resources are hosted on reliable data storage servers to ensure long term availability andeasy accessibility. All data are managed via the EBRAINS Knowledge Graph, thereby helping maintain data provenance, andenabling tight integration with tools and services offered under the EBRAINS ecosystem.
01 Oct 2021

Effect of variations in gap junctional coupling on the frequency of oscillatory action potentials in a smooth muscle syncytium

Frontiers in Physiology

Appukuttan, S., Brain, K., & Manchanda, R. (2021). Effect of variations in gap junctional coupling on the frequency of oscillatory action potentials in a smooth muscle syncytium. Frontiers in Physiology.

Journal Paper Selected Appukuttan, S., Brain, K., & Manchanda, R.

Effect of variations in gap junctional coupling on the frequency of oscillatory action potentials in a smooth muscle syncytium

Appukuttan, S., Brain, K., & Manchanda, R.
Journal Paper Selected
About The Publication
Gap junctions provide pathways for intercellular communication between adjacent cells, allowing exchange of ions and small molecules. Based on the constituent protein subunits, gap junctions are classified into different subtypes varying in their properties such as unitary conductances, sensitivity to transjunctional voltage, and gating kinetics. Gap junctions couple cells electrically, and therefore the electrical activity originating in one cell can affect and modulate the electrical activity in adjacent cells. Action potentials can propagate through networks of such electrically coupled cells, and this spread is influenced by the nature of gap junctional coupling. Our study aims to computationally explore the effect of differences in gap junctional properties on oscillating action potentials in electrically coupled tissues. Further, we also explore variations in the biophysical environment by altering the size of the syncytium, the location of the pacemaking cell, as well as the occurrence of multiple pacemaking cells within the same syncytium. Our simulation results suggest that the frequency of oscillations is governed by the extent of coupling between cells and the gating kinetics of different gap junction subtypes. The location of pacemaking cells is found to alter the syncytial behavior, and when multiple oscillators are present, there exists an interplay between the oscillator frequency and their relative location within the syncytium. Such variations in the frequency of oscillations can have important implications for the physiological functioning of syncytial tissues.
29 Jan 2021

HippoUnit: A software tool for the automated testing and systematic comparison of detailed models of hippocampal neurons based on electrophysiological data

PLoS Computational Biology

Sáray, S., Rössert, C. A., Appukuttan, S., Migliore, R., Vitale, P., Lupascu, C. A., ... & Káli, S. (2021). HippoUnit: A software tool for the automated testing and systematic comparison of detailed models of hippocampal neurons based on electrophysiological data. PLoS computational biology, 17(1), e1008114.

Journal Paper Selected Sáray, S., Rössert, C. A., Appukuttan, S., Migliore, R., Vitale, P., Lupascu, C. A., ... & Káli, S.

HippoUnit: A software tool for the automated testing and systematic comparison of detailed models of hippocampal neurons based on electrophysiological data

Sáray, S., Rössert, C. A., Appukuttan, S., Migliore, R., Vitale, P., Lupascu, C. A., ... & Káli, S.
Journal Paper Selected
About The Publication
Anatomically and biophysically detailed data-driven neuronal models have become widely used tools for understanding and predicting the behavior and function of neurons. Due to the increasing availability of experimental data from anatomical and electrophysiological measurements as well as the growing number of computational and software tools that enable accurate neuronal modeling, there are now a large number of different models of many cell types available in the literature. These models were usually built to capture a few important or interesting properties of the given neuron type, and it is often unknown how they would behave outside their original context. In addition, there is currently no simple way of quantitatively comparing different models regarding how closely they match specific experimental observations. This limits the evaluation, re-use and further development of the existing models. Further, the development of new models could also be significantly facilitated by the ability to rapidly test the behavior of model candidates against the relevant collection of experimental data. We address these problems for the representative case of the CA1 pyramidal cell of the rat hippocampus by developing an open-source Python test suite, which makes it possible to automatically and systematically test multiple properties of models by making quantitative comparisons between the models and electrophysiological data. The tests cover various aspects of somatic behavior, and signal propagation and integration in apical dendrites. To demonstrate the utility of our approach, we applied our tests to compare the behavior of several different rat hippocampal CA1 pyramidal cell models from the ModelDB database against electrophysiological data available in the literature, and evaluated how well these models match experimental observations in different domains. We also show how we employed the test suite to aid the development of models within the European Human Brain Project (HBP), and describe the integration of the tests into the validation framework developed in the HBP, with the aim of facilitating more reproducible and transparent model building in the neuroscience community.
14 May 2020

Implementation of Syncytial Models in NEURON Simulator for Improved Efficiency

2020 28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)

Appukuttan, S., Mandge, D., & Manchanda, R. (2020). Implementation of Syncytial Models in NEURON Simulator for Improved Efficiency. In 2020 28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) (pp. 266-273). IEEE.

Conferences Appukuttan, S., Mandge, D., & Manchanda, R.

Implementation of Syncytial Models in NEURON Simulator for Improved Efficiency

Appukuttan, S., Mandge, D., & Manchanda, R.
Conferences
About The Publication
NEURON is the most widely used computational modeling platform in the field of neuroscience, allowing development of single cell models as well as huge networks of cells. With increase in the sizes of networks, performance and efficiency becomes a growing concern. To investigate larger networks it is necessary to have access to appropriate computational resources. It is equally imperative, and typically more economical, to extract the maximum performance from the available resources. This can be achieved by optimizing the model implementation, along with appropriate utilization of the built-in parallelization and performance enhancement features of the NEURON simulator. In the present study, we have explored, tested and benchmarked the performance benefits gained from these approaches.
17 Jan 2019

Electrophysiology of Syncytial Smooth Muscle

Journal of Experimental Neuroscience

Manchanda, R., Appukuttan, S., & Padmakumar, M. (2019). Electrophysiology of Syncytial Smooth Muscle. Journal of experimental neuroscience, 13, 1179069518821917.

Journal Paper Selected Manchanda, R., Appukuttan, S., & Padmakumar, M.

Electrophysiology of Syncytial Smooth Muscle

Manchanda, R., Appukuttan, S., & Padmakumar, M.
Journal Paper Selected
About The Publication
As in other excitable tissues, two classes of electrical signals are of fundamental importance to the functioning of smooth muscles: junction potentials, which arise from neurotransmission and represent the initiation of excitation (or in some instances inhibition) of the tissue, and spikes or action potentials, which represent the accomplishment of excitation and lead on to contractile activity. Unlike the case in skeletal muscle and in neurons, junction potentials and spikes in smooth muscle have been poorly understood in relation to the electrical properties of the tissue and in terms of their spatiotemporal spread within it. This owes principally to the experimental difficulties involved in making precise electrical recordings from smooth muscles and also to two inherent features of this class of muscle, ie, the syncytial organization of its cells and the distributed innervation they receive, which renders their biophysical analysis problematic. In this review, we outline the development of hypotheses and knowledge on junction potentials and spikes in syncytial smooth muscle, showing how our concepts have frequently undergone radical changes and how recent developments hold promise in unraveling some of the many puzzles that remain. We focus especially on computational models and signal analysis approaches. We take as illustrative examples the smooth muscles of two organs with distinct functional characteristics, the vas deferens and urinary bladder, while also touching on features of electrical functioning in the smooth muscles of other organs.
20 Sep 2018

Investigation of the syncytial nature of detrusor smooth muscle as a determinant of action potential shape

Frontiers in Physiology

Appukuttan, S., Padmakumar, M., Young, J. S., Brain, K. L., & Manchanda, R. (2018). Investigation of the syncytial nature of detrusor smooth muscle as a determinant of action potential shape. Frontiers in physiology, 9, 1300.

Journal Paper Selected Appukuttan, S., Padmakumar, M., Young, J. S., Brain, K. L., & Manchanda, R.

Investigation of the syncytial nature of detrusor smooth muscle as a determinant of action potential shape

Appukuttan, S., Padmakumar, M., Young, J. S., Brain, K. L., & Manchanda, R.
Journal Paper Selected
About The Publication
Unlike most excitable cells, certain syncytial smooth muscle cells are known to exhibit spontaneous action potentials of varying shapes and sizes. These differences in shape are observed even in electrophysiological recordings obtained from a single cell. The origin and physiological relevance of this phenomenon are currently unclear. The study presented here aims to test the hypothesis that the syncytial nature of the detrusor smooth muscle tissue contributes to the variations in the action potential profile by influencing the superposition of the passive and active signals. Data extracted from experimental recordings have been compared with those obtained through simulations. The feature correlation studies on action potentials obtained from the experimental recordings suggest the underlying presence of passive signals, called spontaneous excitatory junction potentials (sEJPs). Through simulations, we are able to demonstrate that the syncytial organization of the cells, and the variable superposition of the sEJPs with the “native action potential”, contribute to the diversity in the action potential profiles exhibited. It could also be inferred that the fraction of the propagated action potentials is very low in the detrusor. It is proposed that objective measurements of spontaneous action potential profiles can lead to a better understanding of bladder physiology and pathology.
27 Oct 2017

A Method for the Analysis of AP Foot Convexity: Insights into Smooth Muscle Biophysics

Frontiers in Bioengineering and Biotechnology

Appukuttan, S., Padmakumar, M., Brain, K. L., & Manchanda, R. (2017). A Method for the Analysis of AP Foot Convexity: Insights into Smooth Muscle Biophysics. Frontiers in bioengineering and biotechnology, 5.

Journal Paper Selected Appukuttan, S., Padmakumar, M., Brain, K. L., & Manchanda, R.

A Method for the Analysis of AP Foot Convexity: Insights into Smooth Muscle Biophysics

Appukuttan, S., Padmakumar, M., Brain, K. L., & Manchanda, R.
Journal Paper Selected
About The Publication
Action potential (AP) profiles vary based on the cell type, with cells of the same type typically producing APs with similar shapes. But in certain syncytial tissues, such as the smooth muscle of the urinary bladder wall, even a single cell is known to exhibit APs with diverse profiles. The origin of this diversity is not currently understood, but is often attributed to factors such as syncytial interactions and the spatial distribution of parasympathetic nerve terminals. Thus, the profile of an action potential is determined by the inherent properties of the cell and influenced by its biophysical environment. The analysis of an AP profile, therefore, holds potential for constructing a biophysical picture of the cellular environment. An important feature of any AP is its depolarization to threshold, termed the AP foot, which holds information about the origin of the AP. Currently, there exists no established technique for the quantification of the AP foot. In this study, we explore several possible approaches for this quantification, namely, exponential fitting, evaluation of the radius of curvature, triangulation altitude, and various area based methods. We have also proposed a modified area-based approach (CX,Y) which quantifies foot convexity as the area between the AP foot and a predefined line. We assess the robustness of the individual approaches over a wide variety of signals, mimicking AP diversity. The proposed (CX,Y) method is demonstrated to be superior to the other approaches, and we demonstrate its application on experimentally recorded AP profiles. The study reveals how the quantification of the AP foot could be related to the nature of the underlying synaptic activity and help shed light on biophysical features such as the density of innervation, proximity of varicosities, size of the syncytium, or the strength of intercellular coupling within the syncytium. The work presented here is directed toward exploring these aspects, with further potential toward clinical electrodiagnostics by providing a better understanding of whole-organ biophysics.
01 Oct 2017

Modeling extracellular fields for a three-dimensional network of cells using neuron

Journal of Neuroscience Methods

Appukuttan, S., Brain, K. L., & Manchanda, R. (2017). Modeling extracellular fields for a three-dimensional network of cells using neuron. Journal of Neuroscience Methods, 290, 27-38.

Journal Paper Selected Appukuttan, S., Brain, K. L., & Manchanda, R.

Modeling extracellular fields for a three-dimensional network of cells using neuron

Appukuttan, S., Brain, K. L., & Manchanda, R.
Journal Paper Selected
About The Publication
Background Computational modeling of biological cells usually ignores their extracellular fields, assuming them to be inconsequential. Though such an assumption might be justified in certain cases, it is debatable for networks of tightly packed cells, such as in the central nervous system and the syncytial tissues of cardiac and smooth muscle. New method In the present work, we demonstrate a technique to couple the extracellular fields of individual cells within the NEURON simulation environment. The existing features of the simulator are extended by explicitly defining current balance equations, resulting in the coupling of the extracellular fields of adjacent cells. Results With this technique, we achieved continuity of extracellular space for a network model, thereby allowing the exploration of extracellular interactions computationally. Using a three-dimensional network model, passive and active electrical properties were evaluated under varying levels of extracellular volumes. Simultaneous intracellular and extracellular recordings for synaptic and action potentials were analyzed, and the potential of ephaptic transmission towards functional coupling of cells was explored. Comparison with existing method(s) We have implemented a true bi-domain representation of a network of cells, with the extracellular domain being continuous throughout the entire model. This has hitherto not been achieved using NEURON, or other compartmental modeling platforms. Conclusions We have demonstrated the coupling of the extracellular field of every cell in a three-dimensional model to obtain a continuous uniform extracellular space. This technique provides a framework for the investigation of interactions in tightly packed networks of cells via their extracellular fields.
05 May 2017

Investigation of action potential propagation in a syncytium

Biomedical Research Journal

Appukuttan, S., Brain, K., & Manchanda, R. (2017). Investigation of action potential propagation in a syncytium. Biomed. Res. J, 4(1), 102-115.

Journal Paper Appukuttan, S., Brain, K., & Manchanda, R.

Investigation of action potential propagation in a syncytium

Appukuttan, S., Brain, K., & Manchanda, R.
Journal Paper
About The Publication
Certain excitable cells, such as those in cardiac and smooth muscle, are known to form electrical syncytia. Cells within a syncytium are coupled to adjacent cells by means of structures known as gap junctions, which provide electrical continuity between cells. This results in the spread and propagation of electrical activity, such as action potentials (APs), from the originating cell to other cells in its syncytium. We propose that this ability of APs to propagate through an electrical syncytium depends on various syncytial features, and also the AP profile. The current study attempts to investigate these various factors using a computational approach. Simulations were conducted on a model of a three-dimensional syncytium using the NEURON simulation platform. The results confirm that the capacity of action potentials to propagate in a syncytium is influenced by the features of the action potential, and also the arrangement of cells within the syncytium. The excitability of biophysically identical cells was found to differ based on the size of the syncytium, their location within it, and the extent of gap junctional coupling between neighboring cells. Only a window of gap junctional coupling levels allowed both the initiation and propagation of action potentials. The results clearly exhibit the role of AP diversity and syncytial features in determining the spread of action potentials. This has significant implications for understanding the functioning of syncytial tissues, such as the detrusor smooth muscle, both in physiology and in disease.
04 Jan 2016

Influence of Gap Junction Subtypes on Passive and Active Electrical Properties of Syncytial Tissues

2016 International Conference on Systems in Medicine and Biology (ICSMB)

Appukuttan, S., Sathe, R., & Manchanda, R. (2016). Influence of Gap Junction Subtypes on Passive and Active Electrical Properties of Syncytial Tissues. Accepted at ICSMB 2016, India. IEEE Xplore Digital Library.

Conferences Appukuttan, S., Sathe, R., & Manchanda, R.

Influence of Gap Junction Subtypes on Passive and Active Electrical Properties of Syncytial Tissues

Appukuttan, S., Sathe, R., & Manchanda, R.
Conferences
About The Publication
Gap junctions are intercellular pores which provide a means of communication in syncytial tissues. They play a vital role in the normal physiological functioning and development of such tissues. A variety of such pores have been reported with differences in their biophysical properties. The current study aims at computationally modeling these gap junctional channels and investigating their influence in determining the behavior of electrically coupled networks of cells. Towards this end, a comparison of passive and active properties has been undertaken by employing different gap junction subtypes.
28 Oct 2015

Independence of AP propagation velocity to transjunctional voltage dependence of gap junctional coupling

2015 International Conference on Biomedical Engineering and Computational Technologies (SIBIRCON)

Appukuttan, S., & Manchanda, R. (2016). Independence of AP propagation velocity to transjunctional voltage dependence of gap junctional coupling. The Siberian Scientific Medical Journal, 36(1), 80-85.

Conferences Appukuttan, S., & Manchanda, R.

Independence of AP propagation velocity to transjunctional voltage dependence of gap junctional coupling

Appukuttan, S., & Manchanda, R.
Conferences
About The Publication
Gap junctions are protein structures that form transmembrane channels between adjacent cells, thereby allowing the direct passage of ions and small molecules. They play an important role in the physiological functioning of the individual cells, and also the tissue. Experimental studies have reported a variety of gap junction subtypes, with differences in their biophysical properties, such as their unitary conductances and sensitivity to transjunctional voltage. Our study aims at computationally exploring the effect of these differences towards the spread of action potentials in syncytial tissues. Results from our simulations suggest that the propagation velocity of action potentials is independent of the transjunctional voltage dependence of the gap junction subtype. The propagation velocity was found to be constant across all subtypes tested, when the maximal conductances were set equal. This was verified using action potentials of widely varying time courses. We attribute this trend to the much slower gating kinetics of gap junctions in comparison to the time course of action potentials, and more specifically the short period where a significant transjunctional voltage is maintained.
15 Oct 2015

Modular approach to modeling homotypic and heterotypic gap junctions

2015 IEEE 5th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)

Appukuttan, S., Sathe, R., & Manchanda, R. (2015). Modular approach to modeling homotypic and heterotypic gap junctions. In Computational Advances in Bio and Medical Sciences (ICCABS), 2015 IEEE 5th International Conference on (pp. 1-6). IEEE.

Conferences Appukuttan, S., Sathe, R., & Manchanda, R.

Modular approach to modeling homotypic and heterotypic gap junctions

Appukuttan, S., Sathe, R., & Manchanda, R.
Conferences
About The Publication
Gap junctions are intercellular pores allowing direct passage of ions and small molecules between adjacent cells. They are physiologically significant, playing important roles in cellular growth and tissue function. Most models of gap junctions focus on single channel currents. Here we present a model exhibiting macroscopic currents in homotypic gap junctions, and thereby allowing evaluation of cellular features. The technique is novel in its modular approach, whereby modules of hemi-channels are developed, followed by their serial combination to obtain the complete gap junction. It is an improvement over earlier models as it is able to demonstrate the phenomena of contingent gating. We present certain biophysical properties observed in a 3-D syncytium under different gap junction subtypes. Finally, we test the efficacy of this approach towards modeling of heterotypic gap junctions.
01 Jun 2015

Syncytial basis for diversity in spike shapes and their propagation in detrusor smooth muscle

International Conference On Computational Science (ICCS) 2015

Appukuttan, S., Brain, K., & Manchanda, R. (2015). Syncytial basis for diversity in spike shapes and their propagation in detrusor smooth muscle. Procedia Computer Science, 51, 785-794.

Conferences Appukuttan, S., Brain, K., & Manchanda, R.

Syncytial basis for diversity in spike shapes and their propagation in detrusor smooth muscle

Appukuttan, S., Brain, K., & Manchanda, R.
Conferences
About The Publication
Syncytial tissues, such as the smooth muscle of the urinary bladder wall, are known to produce action potentials (spikes) with marked differences in their shapes and sizes. The need for this diversity is currently unknown, and neither is their origin understood. The small size of the cells, their syncytial arrangement, and the complex nature of innervation poses significant challenges for the experimental investigation of such tissues. To obtain better insight, we present here a three-dimensional electrical model of smooth muscle syncytium, developed using the compartmental modeling technique, with each cell possessing active channel mechanisms capable of producing an action potential. This enables investigation of the syncytial effect on action potential shapes and their propagation. We show how a single spike shape could undergo modulation, resulting in diverse shapes, owing to the syncytial nature of the tissue. Differences in the action potential features could impact their capacity to propagate through a syncytium. This is illustrated through comparison of two distinct action potential mechanisms. A better understanding of the origin of the various spike shapes would have significant implications in pathology, assisting in evaluating the underlying cause and directing their treatment.
01 Jan 2015

Computational investigation of action potentials in a syncytium

Health 3C: USA-India 2015

Appukuttan, S. & Manchanda, R. (2015). Computational investigation of action potentials in a syncytium. In: Abstract Book - Health 3C: USA-India 2015, Mumbai, India.

Abstract Appukuttan, S. & Manchanda, R.

Computational investigation of action potentials in a syncytium

Appukuttan, S. & Manchanda, R.
Abstract
About The Publication
The smooth muscle of the bladder comprises an electrical 3-dimensional syncytium, the properties of which are not precisely understood. Using values for parameters from literature, we have already developed a 3-D computational model of the passive detrusor syncytium, on NEURON platform. The simulation results were in agreement with theoretical and experimental expectations. We then incorporated active channel mechanisms into each of the cells, thereby empowering them with the capacity to generate action potentials. It was found that the action potential shape and the syncytial topology, such as the extent and pattern of coupling between cells, all played an important role in determining their spread. The above approach and findings signify a wide scope for further exploration of factors determining the initiation and propagation of action potentials in a syncytium.
08 Oct 2014

A computational model of urinary bladder smooth muscle syncytium

Journal of Computational Neuroscience

Appukuttan, S., Brain, K. L., & Manchanda, R. (2015). A computational model of urinary bladder smooth muscle syncytium. Journal of Computational Neuroscience, 38(1), 167-187.

Journal Paper Selected Appukuttan, S., Brain, K. L., & Manchanda, R.

A computational model of urinary bladder smooth muscle syncytium

Appukuttan, S., Brain, K. L., & Manchanda, R.
Journal Paper Selected
About The Publication
Certain smooth muscles, such as the detrusor of the urinary bladder, exhibit a variety of spikes that differ markedly in their amplitudes and time courses. The origin of this diversity is poorly understood but is often attributed to the syncytial nature of smooth muscle and its distributed innervation. In order to help clarify such issues, we present here a three-dimensional electrical model of syncytial smooth muscle developed using the compartmental modeling technique, with special reference to the bladder detrusor. Values of model parameters were sourced or derived from experimental data. The model was validated against various modes of stimulation employed experimentally and the results were found to accord with both theoretical predictions and experimental observations. Model outputs also satisfied criteria characteristic of electrical syncytia such as correlation between the spatial spread and temporal decay of electrotonic potentials as well as positively skewed amplitude frequency histogram for sub-threshold potentials, and lead to interesting conclusions. Based on analysis of syncytia of different sizes, it was found that a size of 21-cube may be considered the critical minimum size for an electrically infinite syncytium. Set against experimental results, we conjecture the existence of electrically sub-infinite bundles in the detrusor. Moreover, the absence of coincident activity between closely spaced cells potentially implies, counterintuitively, highly efficient electrical coupling between such cells. The model thus provides a heuristic platform for the interpretation of electrical activity in syncytial tissues.
.05

POSTERS

MY POSTERS
15 Oct 2021

EBRAINS Live Papers – interactive resources and supplementary materials for neuroscience

Human Brain Project Annual Summit; 2021 OCT 16-18; Online

Appukuttan S., Bologna L. L., Schürmann F., Migliore M. & Davison A. P. EBRAINS Live Papers - interactive resources and supplementary materials for neuroscience. Poster presented at: Human Brain Project Annual Summit; 2021 Oct 15; Online.

Poster
20 Jul 2020

Systematic testing and validation of models of hippocampal neurons against electrophysiological data

29th Annual Computational Neuroscience Meeting, CNS 2020; 2020 Jul 20; Online

Sára S., Rössert C. A., Appukuttan S., Davison A. P., Muller E., Freund T. F., Káli S. Systematic testing and validation of models of hippocampal neurons against electrophysiological data. Poster presented at: CNS 2020; 2020 Jul 20; Online.

Poster
19 Oct 2019

Scientific validation of data-driven neuroscience models despite complexity and sparse data

Society for Neuroscience (SFN): 2019 Oct 19-23, Chicago, USA

Garcia-Rodriguez P, Sharma L, Appukuttan S., Davison A.P. Scientific validation of data-driven neuroscience models despite complexity and sparse data. Poster presented at SFN: 2019 Oct 19-23, Chicago, USA.

Poster
01 Sep 2019

Overview of Test Suites for Validation of Data-Driven Models in Neuroscience

International Neuroinformatics Coordinating Facility (INCF): 2019 Sep 1-2, Warsaw, Poland

Appukuttan S., Garcia P.E., Sharma B.L., Davison A.P. Overview of Test Suites for Validation of Data-Driven Models in Neuroscience. Poster presented at INCF: 2019 Sep 1-2, Warsaw, Poland.

Poster
13 Jul 2019

Systematic automated validation of detailed models of hippocampal neurons against electrophysiological data

28th Annual Computational Neuroscience Meeting, CNS 2019; 2019 Jul 13-17; Barcelona, Spain

Sáray S., Rössert C.A., Appukuttan S., Davison A.P., Muller E., Freund T.F., Káli S. Systematic automated validation of detailed models of hippocampal neurons against electrophysiological data. Poster presented at: 28th Annual Computational Neuroscience Meeting, CNS 2019; 2019 Jul 13-17; Barcelona, Spain.

Poster
21 May 2019

Modeling and visualization of neural activity data

INCF workshop on Data Management and Sharing in Neuroinformatics; 2019 May 21; Marseille, France

Legouée E., Appukuttan S., Ates O., Fragnaud H., Suzen M., Denker, M., Davison A.P. Modeling and visualization of neural activity data. Poster presented at: INCF workshop on Data Management and Sharing in Neuroinformatics; 2019 May 21; Marseille, France.

Poster
05 Dec 2018

Curation and reuse of neural activity data

GDR NeuralNet; 2018 Dec 5-7; Paris, France

Legouée E., Appukuttan S., Ates O., Fragnaud H., Suzen M., Denker, M., Davison A.P. Curation and reuse of neural activity data. Poster presented at: GDR NeuralNet; 2018 Dec 5-7; Paris, France.

Poster
03 Nov 2018

Systematic Statistical Validation of Data-Driven Models in Neuroscience

Society for Neuroscience (SFN): 2018 Nov 3-7, San Diego, USA

Appukuttan S., Garcia P.E., Sharma B.L., Sáray S., Káli S, Davison A.P. Systematic Statistical Validation of Data-Driven Models in Neuroscience. Poster presented at SFN: 2018 Nov 3-7, San Diego, USA.

Poster
16 Oct 2018

Curation and reuse of neural activity data

Human Brain Project Annual Summit; 2018 Oct 16-18; Maastricht, Netherlands

Legouée E., Appukuttan S., Ates O., Fragnaud H., Suzen M., Denker, M., Davison A.P. Curation and reuse of neural activity data. Poster presented at: Human Brain Project Annual Summit; 2018 Oct 16-18; Maastricht, Netherlands.

Poster
16 Oct 2018

The brain simulation platform of the human brain project v2.0: from single cell to circuit building and beyond

Human Brain Project Annual Summit; 2018 Oct 16-18; Maastricht, Netherlands

Bologna L.L., Lupascu C.A., Migliore R., Antonel S.M., Ivaska G., Appukuttan S., Courcol J-D., Schürmann F., Davison A.P., Migliore M. The brain simulation platform of the human brain project v2.0: from single cell to circuit building and beyond. Poster presented at: Human Brain Project Annual Summit; 2018 Oct 16-18; Maastricht, Netherlands.

Poster
26 Sep 2018

Validating Computational Neuroscience Models

Bernstein Conference. 2018 Sep 26-28; Berlin, Germany

Sharma B.L., Appukuttan S., Garcia-Rodriguez P.E., Fragnaud H., Legouée E., Davison A.P. Validating Computational Neuroscience Models. Poster presented at: Bernstein Conference. 2018 Sep 26-28; Berlin, Germany. doi: 10.12751/nncn.bc2018.0228

Poster
09 Aug 2018

A web services framework for model validation in neuroscience

INCF Neuroinformatics; 2018 Aug 9-10; Montréal, Canada

Appukuttan S., Fragnaud H., Sharma L., Gonin J., Garcia P.E., Davison A.P. A web services framework for model validation in neuroscience. Poster presented at: INCF Neuroinformatics; 2018 Aug 9-10; Montréal, Canada.

Poster
07 Jul 2018

Systematic construction and validation of detailed models of hippocampal neurons using reproducible, collaborative workflows

11th FENS Forum of Neuroscience; 2018 Jul 7-11; Berlin, Germany

Sáray S., Migliore R., Lupascu C.A., Bologna L.L., Rössert C.A., Romani A., Courcol J-D., Antonel S. , Van Geit, W., Thomson A., Mercer A., Lange S., Falck J., Appukuttan S., Garcia-Rodriguez P.E., Davison A.P., Muller E., Schürmann F., Migliore M., Freund T.F., Káli S. Systematic construction and validation of detailed models of hippocampal neurons using reproducible, collaborative workflows. Poster presented at: 11th FENS Forum of Neuroscience; 2018 Jul 7-11; Berlin, Germany.

Poster
17 Oct 2017

Human Brain Project Validation Framework: Workflow and Services

Human Brain Project Annual Summit; 2017 Oct 17-20; Glasgow, UK

Sharma L., Appukuttan S., Garcia P.E., Fragnaud H., Gonin J., Davison A.P. Human Brain Project Validation Framework: Workflow and Services. Poster presented at: Human Brain Project Annual Summit; 2017 Oct 17-20; Glasgow, UK.

Poster
17 Oct 2017

Towards automation of experiment-driven building and validation of a mesocircuit model

Human Brain Project Annual Summit; 2017 Oct 17-20; Glasgow, UK

Von Papen M., Voges N., Dabrowska P., Gutzen R., Denker M., Dahmen D., Helias M., Senk J., Hagen E., Diesmann M., Sharma L., Appukutan S., Davison A.P., Grün S. Towards automation of experiment-driven building and validation of a mesocircuit model. Poster presented at: Human Brain Project Annual Summit; 2017 Oct 17-20; Glasgow, UK.

Poster
13 Sep 2017

Automated parameter fitting and testing of detailed neuronal models

Bernstein Conference. 2017 Sep 13-15; Göttingen, Germany

Sáray S., Appukuttan S., Bagi B., Garcia-Rodriguez P.E., Kovách P., Lupascu C.A., Mohácsi M., Rössert C.A., Tar L., Török M.P., Davison A.P., Migliore M., Muller E. , Freund T.F., Káli S. Automated parameter fitting and testing of detailed neuronal models. Poster presented at: Bernstein Conference. 2017 Sep 13-15; Göttingen, Germany. doi: 10.12751/nncn.bc2017.0134

Poster
17 Jun 2013

Computational modeling of electrical activity in bladder smooth muscle: Development of a compartmental syncytium

OIST Computational Neuroscience Course (OCNC) 2013, Okinawa Institute of Science and Technology, Okinawa, Japan

Appukuttan, S. & Manchanda, R. (2013). Computational modeling of electrical activity in bladder smooth muscle: Development of a compartmental syncytium. At: OIST Computational Neuroscience Course (OCNC) 2013, Okinawa Institute of Science and Technology, Okinawa, Japan.

Poster
13 Sep 2011

Computational modeling of electrical activity in bladder smooth muscle syncytium

1st International Symposium on Advances in Molecular Sciences, Bangalore, India

Appukuttan, S. & Manchanda, R. (2011). Computational modeling of electrical activity in bladder smooth muscle syncytium. At: 1st International Symposium on Advances in Molecular Sciences, Bangalore, India.

Poster
.06

TEACHING

Training & Workshops
  • June
    2021
    Online

    Python for beginners

    INCF/OCNS Software WG tutorial

    The INCF/OCNS Software Working Group conducted three beginner/intermediate level tutorials at the CNS*2021 Online conference. These covered the command line (Bash), using Git and GitHub, and development in the Python programming language.

    URL: https://ocns.github.io/SoftwareWG/2021/06/09/software-wg-tutorials-at-cns-2021-online-bash-git-and-python.html
  • May
    2021
    Online

    EBRAINS Infrastructure Training on Model Validation

    EBRAINS/HBP, CNRS

    This training guided modellers and experimentalists in model validation (comparing simulation results to experimental data) using the EBRAINS Model Validation Framework. Participants learned how to: develop model-agnostic validation tests adapt existing models so they can be more easily validated use the EBRAINS model and test catalogue register, search, view and compare the results of validation tests The training took place over four days, with two days of presentations and hands-on demos, followed by two days of participants working on their own validation projects with the assistance of the tutors.

    URL: https://www.humanbrainproject.eu/en/education/training-on-model-validation
  • Feb
    2014
    Chandrapur, India

    NEURON workshop for beginners

    IIT Bombay

    Conducted a 3 day workshop on computational modeling at GCOE, Chandrapur, India.
TEACHING ASSISTANTSHIPS
  • 2013
    2015
    Mumbai, India

    BB803 − Advanced Cellular Electrophysiology

    IIT Bombay

    Instructor: Prof. Rohit Manchanda Content: Current-voltage curves for voltage-gated ion channels: generation and analysis. Ca channel I-V curves: Goldman equation. Input resistance: theory, measurement, inferencing. Applications to skeletal and smooth muscle. Extensions of cable theory: predictions of cable equation; finite cables. Electrical models of neurotransmission in neurons, skeletal muscle and smooth muscle. Modelling of synaptic potentials based on impulse response. Special properties of syncytial tissues: input resistance, current-voltage relations, behaviour of synaptic potentials and spikes. Ca dynamics: components of Ca flux. Computational modelling: the compartmental modelling approach. Modelling passive structures, active properties, neurotransmission. Paper discussion. Text/References: Methods in neuronal modeling : from ions to networks; Eds C. Koch, I. Segev. Cambridge : MIT Press 1998. Computational neuroscience : realistic modeling for experimentalists; Ed: De Schutter, E. Boca Raton : CRC Press 2001. Foundations of cellular neurophysiology; Johnston, D., Wu, S. Cambridge : MIT Press, 1995 Cellular biophysics. Weiss, T.F. Cambridge : MIT Press, 1996 The NEURON book. Carnevale, T, Hines MJ. Cambridge : Cambridge University Press 2005
  • 2011
    2013
    Mumbai, India

    BM636 − Bioelectricity

    IIT Bombay

    Instructor: Prof. Rohit Manchanda Content: Action potential of excitable cells: Quantitative description, Hodgkin-Huxley model, significance of parameters in Hodgkin-Huxley equations; Voltage-clamp experiments : design, and analysis of results; Factors determining the initiation, amplitudes, and kinetic properties of action potentials. Passive membrane electrical properties: Cellular resistance, capacitance, time constant and space constant, methods of measurement; Importance in cellular excitation and signaling: Impulse propagation. Electrophysiology of synaptic transmission: Prejunctional and postjunctional electrical events; time courses of transmitter-activated membrane currents and potentials in skeletal and smooth muscle; Electrical models of the skeletal and smooth muscle membranes. Text/References: B. Katz : Nerve, Muscle, and Synapse, Mc-Graw Hill, New York, 1966. J.G. Nicholls, A.R. Martin & B. Wallace : From Neuron to Brain, 3rd ed., Sinauer, Sunderland, 1992. J.J.B. Jack, D. Noble & R.W. Tsien : Electric Current Flow in Excitable Cells, Oxford University Press, 1983. R.D. Barr & R.L. Plonsey : Bioelectricity: A Quantitative Approach, Academic Press, N.Y., 1988. E.R. Kandel & J. Shwartz (ed.) : Principles of Neural Science, 3rd ed., 1991.
  • 2011
    2013
    Mumbai, India

    BM651 − Biopotentials

    IIT Bombay

    Instructor: Prof. Rohit Manchanda Content: Introduction to molecular and cellular Biology; Molecules, membranes and cells; Cell structure and function: organelles, cytoskeleton and plasma membrane; Metabolism and energy cycles; Synthesis of proteins and nucleic acids; Transport across cell membranes and cytoplasm; Cell to cell biochemical signaling: hormones, receptors and synaptic transmission; Cytoskeleton and movement, Actin and Myosin; Energetics of ion pumps. Origin of biopotentials; Resting membrane potential; The resting membrane as a potassium electrode; Nernst potential; Selective permeability and the Donnan equilibrium; Action potentials: ionic basis, properties of generation and conduction, examples in different cell types, relation to surface-recorded signals; Synaptic potentials: passive properties and integration. Text/References: B. Alberts, D. Bray, J. Levis, M. Raff, K. Roberts & J. D. Watson : Molecular Biology of the Cell, Garland Publishing Inc., New York, USA. 1983. J. Darnell, H. Lodish, D. Baltimore : Molecular Cell Biology, Scientific American Books, New York, USA. 1996. D.J. Aidley: The Physiology of Excitable cells, 3rd Ed., Cambridge University Press, 1990. D. M. Prescott : Cells, Jones & Bartlett, Boston, 1988. A. Loewy, et al. : Cell Structure and Function: An Integrated Approach, 3rd Edition, Saunders, Chicago, 1991.
  • 2010
    2013
    Mumbai, India

    BM627 − Virtual Instrumentation

    IIT Bombay

    Instructor: Prof. Soumyo Mukherji Content: Introduction to bioelectric signals. Analog to Digital Conversion and Data Acquisition Cards. Hardware interfacing. Programming in C for Virtual Instrumentation. Building Graphical User interfaces for use in data acquisition. Signal sampling fundamentals for Data Acquisition. Basic signal processing techniques. Acquisition of general waveforms and biosignals. Issues in online monitoring. Web-based online monitoring. Text/References: Biomedical Signal Analysis by RM Rangayan Microcomputer Interfacing by J.J. Carr LabWindows CVI manuals (National Instruments)
.07

OPEN SOURCE

MODEL.DB SUBMISSIONS
Extracellular fields for a three-dimensional network of cells using NEURON >
Appukuttan S, Brain KL, Manchanda R (2017) Modeling extracellular fields for a three-dimensional network of cells using NEURON. J Neurosci Methods 290:27-38.

URL: https://senselab.med.yale.edu/modeldb/ShowModel.cshtml?model=240957
License : - NA -
HOC NMODL MATLAB
Gap junction subtypes >
Appukuttan S, Sathe R, Manchanda R (2016) Influence of gap junction subtypes on passive and active electrical properties of syncytial tissues 2016 International Conference on Systems in Medicine and Biology (ICSMB) :128-131.

URL: https://senselab.med.yale.edu/modeldb/ShowModel.cshtml?model=244692
License : - NA -
HOC NMODL
Multi-timescale adaptive threshold model >
Kobayashi R, Tsubo Y, Shinomoto S (2009) Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold. Front Comput Neurosci 3:9.

URL: https://senselab.med.yale.edu/modeldb/ShowModel.cshtml?model=226422
License : - NA -
Python NMODL
SIM.TOOL.DB SUBMISSIONS
mknrndll shortcut for Windows >
This NEURON plug-in would provide a shortcut for compiling .mod files by adding ‘mknrndll’ as an option to mouse right-click context menu. It also avoids having to specify the compile directory by implicitly accepting the folder inside which you right-clicked as the location.

URL: https://senselab.med.yale.edu/SimToolDB/showTool.cshtml?Tool=154752
License : - NA -
HOC
Section Copy Tool >
Copies biophysical data (Distributed Mechanisms & L, nseg, Ra) from a specified section to all sections contained in the given SectionList.

URL: https://senselab.med.yale.edu/SimToolDB/showTool.cshtml?Tool=155716
License : - NA -
HOC
SELECTED GITHUB REPOSITORIES
ebrains-live-papers >
EBRAINS Live Paper Platform + EBRAINS Live Paper Builder Tool.
URL: https://github.com/appukuttan-shailesh/ebrains-live-papers
License : Apache-2.0 License
ReactJS JavaScript HTML CSS
hbp-validation-client >
A Python package for working with the Human Brain Project Model Validation Framework.
URL: https://github.com/appukuttan-shailesh/hbp-validation-client
License : BSD 3-Clause "New" or "Revised" License
Python JavaScript HTML CSS
hbp_archive >
A high-level API for interacting with the Human Brain Project archival storage at CSCS.
URL: https://github.com/appukuttan-shailesh/hbp_archive
License : Apache License 2.0
Python
PyNN >
A Python package for simulator-independent specification of neuronal network models.
URL: https://github.com/appukuttan-shailesh/PyNN
License : CeCILL (Ce[a] C[nrs] I[nria] L[ogiciel] L[ibre])
Python
nest-simulator >
The NEST simulator.
URL: https://github.com/appukuttan-shailesh/nest-simulator
License : GNU General Public License v2.0
Python C++
morphounit >
A SciUnit library for data-driven testing of neuronal morphologies.
URL: https://github.com/appukuttan-shailesh/morphounit
License : BSD 3-Clause "New" or "Revised" License
Python
eFELunit >
A SciUnit library for data-driven testing of eFEL features extracted from computational models.
URL: https://github.com/appukuttan-shailesh/eFELunit
License : BSD 3-Clause "New" or "Revised" License
Python
basalunit >
A SciUnit library for data-driven testing of basal ganglia models.
URL: https://github.com/appukuttan-shailesh/basalunit
License : BSD 3-Clause "New" or "Revised" License
Python
usecases >
This repository contains all the information related to the use cases that are shown in the HBP's Brain Simulation Platform.
URL: https://github.com/appukuttan-shailesh/usecases
License : - NA -
Vue JavaScript Python CSS HTML
hbp-bsp-live-papers >
Human Brain Project - Brain Simulation Platform "Live Papers".
URL: https://github.com/appukuttan-shailesh/hbp-bsp-live-papers
License : - NA -
HTML CSS
bluenaas_capabilities >
A SciUnit library for handling NEURON-Python models having different internal implementations/formats.
URL: https://github.com/appukuttan-shailesh/bluenaas_capabilities
License : BSD 3-Clause "New" or "Revised" License
Python
.09

CONTACT

Drop me a line

GET IN TOUCH

You can contact me via email at the following addresses, or alternatively via the form below:
> appukuttan.shailesh@gmail.com
> shailesh.appukuttan@cnrs.fr
> shailesh.a@iitb.ac.in