Molecular and Cellular Neurophysiology

Code and credits: MB209, (3:1)

Course webpage: http://mbu.iisc.ac.in/~mb209/

Instructors: S. K. Sikdar and Rishikesh Narayanan

Duration: Aug–Dec. Semester

Syllabus: Membrane components and structures; membrane transport; passive and active electrical properties of the membrane-ionic mechanisms of membrane and action potential; quantifying ionic hypothesis by voltage-clamp technique; Hodgkin Huxley formalism; structure-function aspects of voltage and chemically gated ionic channels; excitatory and inhibitory postsynaptic potentials; patch-clamp technique; recording and analysis of electrophysiological data; measurement of Ca concentrations in single cells; cell membrane capacitance and exocytosis, confocal microscopy. Synaptic plasticity: short-term and long-term potentiation and depression; mechanisms underlying synaptic plasticity; dendritic structure; dendritic ion channels; active properties of dendrites; dendritic spikes and backpropagating action potentials; intrinsic plasticity; mechanisms underlying intrinsic plasticity.

Books and references

a. Hille, B., Ionic channels of excitable membranes. 2nd Edition, Sinauer Associates, Sunderland, Massachussets.
b. Rudy, B., and Iverson, L.E. (Eds.) Methods in Enzymology, 207, 1992.
c. Kandel, E.R., Schwartz, J.H., & Jessel, T.M., Essentials of Neural Science and Behaviour, Prentice Hall International, 1995.
d. Cowan, W.M., Südhof, T.C., Stevens, C.F., Synapses, The Johns Hopkins University Press, First edition, 2003.
e. Stuart, G., Spruston, N., Hausser, M., Dendrites, Oxford University Press, Second edition, 2008.

Theoretical and computational neuroscience

Code and credits: MB208, (3:1)

Course webpage: http://mbu.iisc.ac.in/~mb208/

Instructors: Rishikesh Narayanan and S. P. Arun

Duration: Jan–Apr. Semester

Syllabus: Need for and role of theory and computation in neuroscience; various scales of modeling; ion channel models; single neuron models; network and multiscale models; models of neural plasticity; oscillations in neural systems; central pattern generators; single neuron oscillators; oscillators as nonlinear dynamical systems; information representation; neural encoding and decoding; population codes; hierarchy and organization of sensory systems; receptive field and map modeling; case studies, computational laboratory and projects.

Prerequisites: MB209 (or basic exposure to ion channels and their functions), basic knowledge of linear algebra, probability, statistics and ordinary differential equations, and some programming knowledge.

Books and references

a. Peter Dayan and L. F. Abbott, Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems, The MIT press, 2005.
b. Christof Koch and Idan Segev (Eds), Methods in Neuronal Modeling: From Ions to Networks, The MIT press, second edition, 1998.
c. Eric De Schutter (Ed.), Computational modeling methods for neuroscientists, The MIT press, 2009.
d. Eugene Izhikevich, Dynamical systems in neuroscience: the geometry of excitability and bursting, The MIT press, 2006.
e. Kenji Doya, Shin Ishii, Alexandre Pouget, Rajesh PN Rao (Eds), Bayesian Brain: Probabilistic Approaches to Neural Coding, The MIT press, 2007.

Lectures in other courses

Apart from these two courses that I handle, I give a few lectures at the Introduction to Neuroscience (NS 201) course covering the basics of the Hodgkin-Huxley system, and on certain aspects of learning and memory. Further, I also give a few lectures at the "Selected topics in Image Processing" course (E9 242) on information processing in the early visual system.