Track Categories

The track category is the heading under which your abstract will be reviewed and later published in the conference printed matters if accepted. During the submission process, you will be asked to select one track category for your abstract.

Neuro engineering is the convergence of neuroscience, device development, computation, and mathematics, and is one of the most exciting new ventures in science and technology today. It brings together state-of-the-art technologies, algorithms, experimental research and concepts. Computational neuroscience is the field of study in which interdisciplinary science links up the various fields of neuroscience, computer science, physics and applied mathematics together. It is the primary investigative method used in order to understand the functioning and mechanism of  these computational models are used to enclosing the hypotheses which can be directly tested by current or future biological and/or psychological experiments.

  • Track 1-1Neuroscience
  • Track 1-2Engineering
  • Track 1-3Neuro mechanics
  • Track 1-4Neuromodulation
  • Track 1-5Neural regrowth and repair

Mathematical and statistical models have played vital roles in neuroscience, especially by concerning the electrical activity of neurons recorded individually, or collectively across large networks. As the field moves forward quickly, new challenges are emerging. For maximal effectiveness, those working to advance computational neuroscience will need to appreciate and exploit the complementary strengths of mechanistic theory and the statistical paradigm.


  • Track 2-1Neural data analysis
  • Track 2-2Neural modelling
  • Track 2-3Neural networks
  • Track 2-4Theoretical neuroscience

Neuroscience can be described as a scientific study of human nervous system which covers all the disciplines of neural system. The perfect organization of nerve cells to functional circuits (neural networks) which helps in communication is dealt by the study of neurobiology. The functioning of brain and spinal cord is obtained through the process of imaging modalities which is called as brain mapping.Cellular Neuroscience and Molecular neuroscience involves the study of neurons at a cellular and molecular level. Clinical Neuroscience  is a branch of neuroscience focussing on the diseases and disorders of the brain and central nervous system. Clinical neuroscience serves as a future of Psychiatry.

  • Track 3-1Molecular Neuroscience
  • Track 3-2Cellular Neuroscience
  • Track 3-3Clinical Neuroscience
  • Track 3-4Drug Addiction and Rehabilitation

Psychiatric disorders such as autism and schizophrenia arise from abnormalities in brain systems that cause cognitive, emotional and social functions. The brain is enormously complex and its ample feedback loops on multiple scales prevent instinctive explanation of circuit functions. Interaction with experiments, theory and computational modelling are essential for understanding how, neural circuits generate flexible behaviours and their impairments give rise to psychiatric symptoms. This highlights the recent progress in applying computational neuroscience to the study of mental disorders.

  • Track 4-1Biologically-based neural circuit
  • Track 4-2Endophenotypes across brain disorder
  • Track 4-3Big data and model aided diagnosis
  • Track 4-4Biophysically – based neural circuit modelling: understanding across levels
  • Track 4-5Looking forward: building a new cross-disciplinary field

Brain imaging gives a non-intrusive window into the workings of the human central nervous system. Neuroimaging is done utilizing procedures like fMRI, PET, MEG and so on. These guide bloodstream in the brain instead of neuronal action if there should arise an occurrence of EEG. Scientists utilize an assortment of neuroimaging devices to study the cerebrum. Computed Tomography (CT) scans are oblique X-ray slices that demonstrate the thickness of mind structures. Magnetic resonance imaging (MRI) utilizes changes in electrically charged particles in a magnetic field to form images of the brain.

  • Track 5-1Magnetic Resonance Imaging
  • Track 5-2Positron Emission Tomography
  • Track 5-3Electroencephalography
  • Track 5-4Functional Magnetic Resonance Imaging
  • Track 5-5Computed tomography

Connectomics is an integral part of network neuroscience.  It involves in a mixture of experimental and computational approaches to brain connectivity. First, connectomics provides comprehensive maps of neural connections, with the ultimate goal of achieving complete coverage of any given nervous system. Second, connectomics delivers insights into the principles that trigger network architecture and uncovers how these principles support network function. These dual aims can be accomplished through the convergence of new experimental techniques for mapping connections and new network science methods for modelling and analysing the resulting large connectivity datasets. Connectomics naturally mergers empirical and computational approaches to gain fundamentally new insights into structure and function of brain networks.

  • Track 6-1Graph-theoretical Measures
  • Track 6-2Brain Parcellation
  • Track 6-3Registration and Parcellation Propagation
  • Track 6-4Constructing the Connectivity Matrix
  • Track 6-5Network Analysis

Magneto- and electroencephalography (M/EEG) measure the electromagnetic signals produced by brain activity. In order to address the issue of limited signal-to-noise ratio (SNR) with raw data, acquisitions consist of multiple repetitions of the same experiment. An important challenge arising from such data is the variability of brain activations over the repetitions. The Latent Support Vector Machine (LSVM) formulation, where the latent shifts are inferred while learning the classifier parameters. The conditional shifts are used to improve the SNR of the M/EEG data, and to infer the chronometry and the sequence of activations across the brain regions that are involved in the experimental task. Results are validated on a long-term memory retrieval task, showing significant improvement using the proposed latent discriminative method.

  • Track 7-1Magnetoencephalography
  • Track 7-2Electroencephalography
  • Track 7-3Latent SVM
  • Track 7-4Independent component analysis
  • Track 7-5Functional connectivity
  • Track 7-6Single-trial variability

Neural modelling field (NMF) is a mathematical framework for machine learning which combines ideas from neural networksfuzzy logic, and model based recognition.NMF is interpreted as a mathematical description of mind’s mechanisms, including concepts, emotions, instincts, imagination, thinking, and understanding. At each level in NMF encapsulating the knowledge; they generate so-called top-down signals, interacting with input, bottom-up signals. These interactions are administrated by dynamic equations, which drive concept-model learning, adaptation, and formation of new concept-models for better correspondence to the input, bottom-up signals. Engineers employ quantitative tools that can be used for understanding and interacting with complex neural systems. Methods of studying and generating chemical, electrical, magnetic, and optical signals responsible for extracellular field potentials and synaptic transmission in neural tissue aid researchers in the modulation of neural system activity. To understand properties of neural system activity, engineers use signal processing techniques and computational modelling.


  • Track 8-1Elements of neuronal dynamics
  • Track 8-2Elementary neuron models
  • Track 8-3Neuronal Coding
  • Track 8-4Biologically detailed models: The Hodgkin-Huxley Model
  • Track 8-5Spiking neuron models
  • Track 8-6Spiking neural networks

Neuromorphic engineering, also known as neuromorphic computing, this describes  the use of very-large-scale integration (VLSI) systems containing electronic analogue circuits to mimic neuro-biological architectures present in the nervous system. The term neuromorphic has been used to describe analogue, digital, mixed-mode analogue/digital VLSI, and software systems that implement models of neural systems (for perceptionmotor control, or multisensory integration).

  • Track 9-1Neuromorphic hardware
  • Track 9-2Neuromorphic algorithms
  • Track 9-3Neuromorphic applications
  • Track 9-4Neuromorphic circuits
  • Track 9-5Neuromorphic systems
  • Track 9-6Neuromorphic devices

Neuroprosthetics (also called neural prosthetics) related to neuroscience and biomedical engineering concerned with developing neural prostheses. They are compared with a brain–computer interface, which connects the brain to a computer rather than a device meant to change missing biological functionality. Brain-machine interface technology is a logical overall direction of neuroprosthetics. Advance technology that will be required for clinical translation of brain-machine interfaces are already under development, including a new generation of recording electrodes, the decoding and interpretation of signals basic intention and planning, actuators for implementation of mental plans in virtual or real contexts, direct somatosensory feedback to the nervous system to improve actions, and training to encourage plasticity in neural circuits.

  • Track 10-1Auditory prosthetics
  • Track 10-2Prosthetics for pain relief
  • Track 10-3Bladder control implants
  • Track 10-4Motor prosthetics for conscious control of movement
  • Track 10-5Sensory/motor prosthetics

neuron consists of a cell body and its processes, an axon and one or more dendrites. Neurons function in the initiation and conduction of impulses. They transmit impulses to other neurons or cells by releasing neurotransmitters at synapses. The central nervous system (CNS) is specialised in processing information originating from a variety of internal and external sources. Within the CNS, the elementary building blocks are compactly interconnected in hierarchical and parallel pathways. Information originating from sensory neurons in contact with the body periphery is gradually transformed along these pathways to generate specific actions through signals relayed by motor neurons to peripheral organs.

  • Track 11-1Action potential
  • Track 11-2Information processing
  • Track 11-3Synapse
  • Track 11-4Neuron map

Neuroinformatics is a combination of neuroscience and information science. It deals with the organisation of neuroscience data and knowledge bases of nervous system and application of tools and computational models for data analysis, acquisition, visualization and distribution. Brain mapping helps the scientists to know what the brain does and how various parts of the brain work. Brain mapping is a set of neuroscience techniques used to view the structural and functional aspects of the brain onto a spatial representation called maps. Due to the advancements and research in the field of neuroinformatics, large amount of data is being analysed and interpreted using various tools.

  • Track 12-1Development and Management of Databases
  • Track 12-2Cognitive and Computational Modelling
  • Track 12-3Tools Used in Neuroinformatics
  • Track 12-4Current Research and Applications

Cognitive neuroscience is the study of how the brain enables the mind. Brain science that explores how an individual neuron operate and communicate to form complex neuronal buildings that comprise the human brain. Cognitive science uses the experimental methods of cognitive psychology and artificial intelligence to create and test models of higher-level cognition such as thought and language.  Methods employed in cognitive neuroscience include psychophysical experiments, functional neuroimaging, electrophysiological studies of neural systems and, increasingly, cognitive genomics and behavioural genetics.


  • Track 13-1Phrenology
  • Track 13-2Localizationist view
  • Track 13-3Aggregate field view
  • Track 13-4Emergence of neuropsychology

Systems neuroscience is a subdiscipline of neuroscience and systems biology that studies the structure and function of neural circuits and systems. The study concerned with how nerve cells behave when it is linked together to form neural pathwaysneural circuits, and larger brain networks. To find the relation between molecular and cellular approaches to understand the brain structure and function using either single-unit recording or multi-electrode recording, functional magnetic resonance imaging(fMRI) and PET scans. Using animal models to emulate core cognitive process and to study algorithms and neural circuits in detail.

  • Track 14-1Memory
  • Track 14-2Spatial Cognition
  • Track 14-3Cognitive Control
  • Track 14-4Sensory Coding

Neurostimulation is focused on modulation of the nervous system's activity using invasive (e.g. microelectrodes) or non-invasive means (e.g. transcranial magnetic stimulation or transcranial electric stimulation, tES such as  tDCS or transcranial alternating current stimulation, tACS). Neurostimulation usually refers to the electromagnetic approaches to neuromodulation. It serves as a  part of neural prosthetics for hearing aids, artificial vision, artificial limbs, and brain-machine interfaces. Alternatively, transcranial magnetic stimulation and transcranial electric stimulation have been proposed as non-invasive methods in which either a magnetic field or trans cranially applied electric currents cause neurostimulation.

  • Track 15-1Brain stimulation
  • Track 15-2Deep brain stimulation
  • Track 15-3Non-invasive brain stimulation
  • Track 15-4Transcranial magnetic stimulation
  • Track 15-5Transcranial electrical stimulation