Milad Lankarany

Dr. Lankarany is an early career investigator with a unique combination of knowledge: he has a PhD in electrical engineering (Concordia University, Montreal, Canada) and expertise in theoretical and computational neuroscience (Post-doc, SickKids, Canada), as well as advanced signal processing and information theory (Industrial Post-doc, Myant Inc). He has 10+ years of experience working with digital and analogue circuits. During his 2 years at the company Myant, he implemented advanced signal processing algorithms on micro-controllers and FPGAs and designed and developed analog circuits for sensing very weak electrophysiological signals (like EEG) using dry electrodes. He also gained experience performing in vitro experiments and using the dynamic clamp technique during his postdoctoral training at the Hospital for Sick Children (SickKids).
  • Scientist, Krembil Brain Institute, Division of Clinical and Computational Neuroscience, Krembil Research Institute, University Health Network (UHN)
  • Assistant Professor, Institute of Biomedical Engineering, University of Toronto?
  • Cross-appointed Faculty, Department of Physiology, University of Toronto
  • Affiliate Scientist, The KITE Research Institute, UHN
  • Dr. Lankarany's lab, the Neural System and Brain Signal Processing Lab, develops and uses advanced methods in Computational Neuroscience and Engineering as well as cutting-edge Neurotechnology to uncover information processing mechanisms of neural systems, in order to treat neurological disorders and to advance biologically-inspired computational frameworks.
  • Sayan Faraz, Idir Mellal, and Milad Lankarany (2020), “Optimal Representation of Biologically Realistic Feedforward Neural Network (BrFNN) using A Novel Abstract Network Model”, IEEE Journal of Selected Topics in Signal Processing (in press)
  • M. Lankarany, Dhekra Al-Basha, S. Ratte, and S. A. Prescott (2019), “Synchrony-Division Multiplexing: Simultaneous Representation of Distinct Stimulus Features using Synchronous and Asynchronous spikes,” Proceedings of the National Academy of Science (PNAS), 116(20):10097-10102
  • L. S. Lesperance, S. Ratte, Milad Lankarany, Tianhe Zhange, and S. A. Prescott (2018), “Artifactual hyperpolarization during extracellular electrical stimulation: proposed mechanism of high-rate spinal cord stimulation disproved,” Brain Stimulation, 11(3): 582-591.
  • M. Lankarany (2017), “Estimating Excitatory and Inhibitory Synaptic Conductances from Spike Trains using a Recursive Bayesian Approach,” BioRxiv, doi:
  • M. Lankarany, J. Heiss, I. Lampl and T. Toyoizumi (2016), “Simultaneous Bayesian estimation of excitatory and inhibitory synaptic conductances by exploiting multiple recorded trials,” Frontiers in Computational Neuroscience,
  • M. Lankarany, W.-P. Zhu and M. N. S. Swamy (2014), “Joint Estimation of States and Parameters of Hodgkin Huxley Model using Kalman Filtering,” Neurocomputing, Vol. 136.

Neural Coding, Information Processing, Mathematical Modeling, Adaptive Algorithms, Neuro-technology