MB208: Theoretical and Computational Neuroscience
Course description
The course will provide an introduction to quantitative neuroscience, emphasizing the role of mathematical and empirical methodology towards understanding neurobiological function. In this process, however, we will try and minimize the use of complicated math and place emphasis instead on the intuitive relationships between the equations and neurophysiology! We will span the multiple scales of analysis in neuroscience, and roughly proceed from the systems to the cellular/molecular end of the spectrum, running through the network scale in the process.
We will assume that you are taking the course because you want to learn the material seriously and benefit from the class interactions and assignments. If you plan on taking the course just for completing your credit requirements or for “just passing” the course or for obtaining a specific grade, this course is definitely not for you! Given that this is an interdisciplinary course with a diverse profile of students, we strongly encourage class participation.
Course structure and grading
Of
the total 40 lectures that form the course, 25 will be covered by
Rishi and 15 by Arun. The topics of lectures in chronological
order are given below.
As we are a 3:1 course, we will have separate tutorial classes
(apart from the
main 3 hours of lectures per week) where we introduce you to
programming in
specific simulation environments. While we encourage you to learn
and code in
these simulation environments, it is NOT
binding that you have to use
these for your assignments and projects. You are free to use any
simulation
environment of your choice to complete the requirements of the
assignments/project.
A
number of take-home, programming-related assignments
will
be handed over in the class from time to time. The assignments are
to be
submitted electronically through an email to the respective
instructor before
the specified deadline. Some assignments have associated
presentation classes. Assignments
will typically be graded on a ten-point scale, and a delay in
submission of the
assignment would lead to loss of marks. The schedule is designed
such that you
will have ample time to finish the assignments if you start early!
Be warned
that if you start the assignments just the day before the
assignment, you will
not have enough time to complete it!
The
final evaluation will be based on a theoretical/computational
project that you put together, preferably based on you own idea.
You are free
to pick your topic of choice for the project, with the only
constraint that it
should be related to the course contents!! The first step in this
process is a proposal
presentation, where you present to the
instructors and the other students
as to what you propose to do for your final project. The
evaluation will be
based largely on the scientific content and the cogence of the
proposal, but
emphasis will also be placed on its relations to literature, its
implications,
and your presentation abilities. The project proposal presentation
is around a
month before the proposal submission. Start thinking about the
project early
on, and it is extremely important for you to ensure that the
project is an
effective synthesis of what you learnt in
both parts of this course.
Around
10 days before the project report submission, there will be a
progress evaluation presentation, where
you discuss about how far you
have progressed and what challenges remain. Finally, you submit a
project
report in the form a Journal
of Physiology manuscript (refer to details there
on what should be reported in each section of the manuscript, and
what kind of
analysis is expected from modeling studies). These reports are
distributed
among all students so that the final presentation class can be an
interactive
session. The final project presentation
comes after the submission,
where you will be expected to present and defend your completed
project in the
classroom. Evaluation will be based on scientific, programming,
writing and
presentation aspects, although the scientific part will carry the
most
weightage.
The overall grading structure is as follows:
Programming assignments and class presentation: 70%
Proposal presentation, project report and final presentation: 30%
Honor code
You will be expected to maintain the highest levels
of academic and scientific integrity. Especially given the
interdisciplinary nature of the course, close exchange of ideas
among yourselves is encouraged. However, any material submitted for
grading (assignments and project) must be
your own work. Further, copying any
material (including those from the web) without proper citation is
considered as plagiarism, and is strictly prohibited
from being part of any of the submitted material. Any material
(including ideas) taken from anywhere must
have appropriate citations. Finally, in the assignments and the
project, what we are looking for are individuality and
innovativeness — your opinions, your ideas and your
analyses, not repetitions of somebody else’s! Therefore, do not
reproduce any material verbatim from any source, even if the source
is cited. Concordantly, in the process of completing the programming
assignments and the final project, remember the spirit of the course
that you are taking this course only because you want to learn from
the lectures and assignments; follow that in letter and spirit! Let
learning be the goal, not completion of the assignments/project by
hook or crook (AKA copying from the web or a friend, and changing
variable and function names, etc.)! Any deviations from
any of these principles will result in an F grade.
Do NOT expect any lenience if you violate the honor code
in any manner whatsoever.
PART I (ARUN: 15 lectures and 4 tutorial
classes)
Total
marks
for Assignments associated with this part: 30
L1
3
Jan 2024 W Introductory class
T
4 Jan 2024 Th MATLAB Tutorial
AA0 Assignment 0 posted
L2
5
Jan 2024 F Probability
AA1 Assignment 1 posted
L3
8
Jan 2024 M Random variables
L4
10
Jan 2024 W Functions of random variables
L5
15
Jan 2024 M Cross-correlation
ADA1 Data Assignment posted
AA2 Assignment 2 posted
L6
17 Jan 2024 W
Linear
Algebra 1
L7
19 Jan 2024 F
Linear
Algebra 2
L8 22 Jan 2024 M
Linear Algebra 3
L9
26 Jan 2024 F
Eigenvalues
L10
29
Jan 2024 M Fourier series
AA3 Assignment 3 posted
L11
31
Jan 2024 W Entropy
L12
2
Feb 2024 F Mutual Information
L13
5
Feb 2024 M Efficient Coding
L14
7
Feb 2024 W Linear Classifiers
L15
9
Feb 2024 F Signal detection theory
PART II (RISHI: 25 lectures and 8
tutorial classes)
Total
marks
for Assignments associated with this part: 40
T1
10 Feb 2024 Sa Tutorial 1: Basic electric circuits
L17
14 Feb 2024 W
Introduction
to single neuron models
L18
16 Feb 2024 F
Modeling
neuronal passive properties
T2
17 Feb 2024 Sa Tutorial 2: Basic
programming and introduction
to
NEURON
RA0 Assignment 0 posted
(Submission
deadline: 23 Feb 2024)
L19
19
Feb 2024 M How to model an entire passive neuron with dendrites?
L20
21
Feb 2024 W Modeling ion channels
L21
23
Feb 2024 F HH-type model: modeling various ion channels using
it
and its inadequacies
T3
24 Feb 2024 Sa Tutorial 3: Passive
models
RA1 Assignment 1 posted
(Submission
deadline: 4 Mar 2024)
Markov
models
L23
28
Feb 2024 W Addressing the inadequacies of the HH-type model-II:
Stochastic
channel models
L24
1
Mar 2024 F Ion channels in single neuronal models:
Modeling
somatodendritic variability
T4
2 Mar 2024 Sa Tutorial 4:
Implementing the H–H model
RA2 Assignment 2 posted
(Submission
deadline: 14 Mar 2024)
L25
4
Mar 2024 M Modeling synapses and incorporating them into
single
neuron models
L26
6
Mar 2024 W Calcium dynamics: Modeling the ON mechanisms
L27
8
Mar 2024 F Calcium dynamics: Modeling the OFF mechanisms
T5
9 Mar 2024 Sa Tutorial 5: Custom
H–H models and Markovian models
RA3 Assignment 3 posted
(Submission
deadline: 24 Mar 2024)
L28
11
Mar 2024 M Modeling signaling pathways
L29
13
Mar 2024 W Noise and the nervous system
L30
15
Mar 2024 F Network models
T6
16 Mar 2024 Sa Tutorial 6: Modeling
with
3D reconstructions
L31
18
Mar 2024 M A neuron can be a network!
L32
20
Mar 2024 W Oscillations in the brain
22
Mar 2024 F
Project
proposal presentation
T7
23 Mar 2024 Sa Tutorial 7: Calcium
handling and calcium-dependent
channels
RA4 Assignment 4 posted
(Submission
deadline: 4 Apr 2024)
L33
25
Mar 2024 M Intrinsic oscillations
L34
27
Mar 2024 W Network oscillations
L35
29
Mar 2024 F Synfire chains, temporal coding, and coincidence detection
T8
30 Mar 2024 Sa Tutorial 8: Modeling
synapses and networks
L36
1
Apr 2024 M Modeling local field potentials
L37
3
Apr 2024 W Short-term synaptic plasticity models
L38
5
Apr 2024 F Long-term synaptic plasticity models
CP1
6 Apr 2024 Sa
Class
presentation on Assignments 3 and 4 (RA3 & RA4)
L39
8
Apr 2024 M Mechanistic models for synaptic plasticity
L40
10
Apr 2024 W Interactions between various forms of neuronal plasticity
FINAL
PROJECT
DATES
22
Mar 2024 F
Project
proposal
presentation
12
Apr
2024 F Project progress evaluation
22
Apr
2024 M Project submission deadline
24
Apr
2024 W Final project presentation
