Sign up for Virtual Chats here. | View Academic Calendar 2022-23 here. | Admissions for TLP Class of 2024 are closed | Admissions to BTech program are open. Deadline July 10, 2023. Click here to apply 

Dr. Subhasis Ray

Assistant Professor, Plaksha University

Area of Expertise

Computational and experimental Neuroscience

Research Interests

  • Chemical senses and sensory-guided behavior
  • Neurophysiology and neural networks in animal brains
  • Computational modeling of biological neural networks
  • Tools for simulation and data analysis in Biology


  • Ph.D. in Biology, NCBS, TIFR, Bangalore, India
  • B.E. in Computer Science and Engineering, Jadavpur University, Kolkata, India

Past Associations

  • Postdoctoral Fellow, NICHD, National Institutes of Health (NIH), Bethesda, USA
  • Software Engineer, IBM India Pvt. Ltd., Kolkata, India

How does the brain process sensory information to generate behavior? Subhasis investigates this experimentally using simple model animals like insects, and complements that with computational modeling. He is also interested in developing software tools for facilitating computational modeling and data analysis in Neuroscience.

Before joining Plaksha, Subhasis was a Postdoctoral Fellow at NICHD/NIH, USA, where he investigated olfactory information processing in the insect brain using electrophysiology experiments and computational modeling. He received his PhD from National Centre for Biological Sciences, Tata Institute of Fundamental Research in Bangalore. There he worked on modeling cortical neural networks, as well as developing a biological simulator and formats for model and data description in neuroscience.

Prior to his PhD, Subhasis worked as a Software Engineer in the IT Industry after obtaining his undergraduate degree in Computer Science and Engineering from Jadavpur University, Kolkata.

Research area

How do animals navigate their environment using their senses? The brain combines different sensory streams like vision, smell, touch, and hearing along with information from prior experience such that an animal can move around to find food and mate, and escape predators and other dangers. We are interested in understanding the neural pathways and mechanisms underlying the integration of these sensory inputs and with memory.

For this, one can look at the behavior of animals while manipulating their sensory inputs. And at the physiological level, one can record the activities of neurons in the brain in response to various stimuli. By analyzing behavior and neuronal activity, one hopes to figure out the principles underlying navigation in animals. Simple model animals like insects are well suited for these studies because they are usually robust and easy to experiment with.

One can then use experimental data to build computational models and explore beyond what is possible in physical experiments, and obtain theoretical understanding of these principles.

Techniques applied in our research

We use computer vision algorithms to analyze behavior of animals from recorded videos, and apply signal processing , information theory, and other analytical tools for investigating electrophysiological data. We also develop biophysically detailed models of single neurons and their networks, and conduct in silico experiments with these models. This requires simulation tools, precise formats for describing models and neuronal (experimental and simulated) data, which form part of the field of Neuroinformatics.


S. Ray, K. Sun, and M. Stopfer, “Innate attraction and aversion to odors in locusts.” bioRxiv, p. 2023.04.05.535770, Apr. 06, 2023. doi: 10.1101/2023.04.05.535770.

S. Ray and M. A. Stopfer, “Argos: A toolkit for tracking multiple animals in complex visual environments,” Methods in Ecology and Evolution, vol. 13, no. 3, pp. 585-595, Mar. 2022, doi: 10.1111/2041-210X.13776.

S. Ray, Z. N. Aldworth, and M. A. Stopfer, “Feedback inhibition and its control in an insect olfactory circuit,” eLife, vol. 9, p. e53281, Mar. 2020, doi: 10.7554/eLife.53281.

S. Ray, C. Chintaluri, U. S. Bhalla, and D. K. Wójcik, “NSDF: Neuroscience Simulation Data Format,” Neuroinformatics, vol. 14, no. 2, pp. 147–167, Apr. 2016, doi: 10.1007/s12021-015-9282-5.

P. Gleeson, S. Crook, R. C. Cannon, M. L. Hines, G. O. Billings, M. Farinella, T. M. Morse, A. P. Davison, S. Ray, U. S. Bhalla, S. R. Barnes, Y. D. Dimitrova, and R. A. Silver, “NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail,” PLoS Comput Biol, vol. 6, no. 6, p. e1000815, Jun. 2010, doi: 10.1371/journal.pcbi.1000815.

S. Ray and U. S. Bhalla, “PyMOOSE: Interoperable scripting in Python for MOOSE,” Front. Neuroinform., vol. 2, p: n/a, Dec. 2008, doi: 10.3389/neuro.11.006.2008.


Subhasis' Google Scholar page

Book Chapters

Subhasis Ray "Anubhutir Bahir O Bhitar (অনুভূতির বাহিরভিতর)", in Pratihar, S. (ed) Pran Pelo Prithibi (প্রাণ পেলো পৃথিবী), A Collection of Essays. Kabitika, 2022

N. Dudani, U. S. Bhalla, and S. Ray, “MOOSE, the Multiscale Object-Oriented Simulation Environment”, in Jaeger, D. and Jung, R. (eds) Encyclopedia of Computational Neuroscience. New York, NY: Springer, pp. 14., 2013, doi:10.1007/978-1-4614-7320-6_257-1. ISBN: 978-1-4614-7320-6