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Computational Neuroscience

By Prof. Sharba Bandyopadhyay   |   IIT Kharagpur
Learners enrolled: 1754   |  Exam registration: 211
ABOUT THE COURSE:
Computational Neuroscience is the fundamental subject to provide quantitative understanding of information processing by neurons in the brain. Questions on, how humans and other animals learn efficiently, create and recall memories, make decisions among many others can be approached with the basic understanding of computation by neurons and its general principles. The course will include topics starting with the very basics of quantifying neuronal activity and modeling spiking by single neurons. Next the course dwells upon the general approaches used to understand representation of information by neurons and how such information may be readout for practical applications. Finally the course covers the computational modeling of implementing plasticity, the most important aspect of the brain, aiding in learning, memory and cognition.

INTENDED AUDIENCE: Students interested in Neural and Cognitive Sciences and AI

PREREQUISITES: 1st year college Mathematics and Biology

INDUSTRY SUPPORT: AI related industry
Summary
Course Status : Completed
Course Type : Elective
Language for course content : English
Duration : 12 weeks
Category :
  • Biological Sciences & Bioengineering
  • Computational Biology
Credit Points : 3
Level : Undergraduate/Postgraduate
Start Date : 24 Jul 2023
End Date : 13 Oct 2023
Enrollment Ends : 07 Aug 2023
Exam Registration Ends : 18 Aug 2023
Exam Date : 28 Oct 2023 IST

Note: This exam date is subject to change based on seat availability. You can check final exam date on your hall ticket.


Page Visits



Course layout

Week 1: Introduction to Neurons
1) Neuron structure
2) Networks of Neurons and Synapses
3) System of neural processing
4) Basic structures in the brain
5) Sensory - Executive - Behavior systems
Week 2: Excitable Membranes and Neural Activity
1) Membrane Potential and All or None Spike
2) Patch Clamp Techniques, Membrane Potential
3) Ion Channels
4) Current Injection - Synapses
5) Single neuron activity
Week 3: Point models: Hodgkin Huxley Equations (HHE)
1) Point and Compartmental Models of Neurons
2) Hodgkin Huxley Equations - I
3) Hodgkin Huxley Equations - II
4) Reducing the HHE and Moris-Lecar Equations (MLE) 5) Properties of MLE
Week 4: Analysis of Neural Models
1) Phase Plane Analysis - I
2) Phase Plane Analysis - II
3) Analyzing HHE
4) Bifurcations
5) Other Point Models
Week 5: Spike Trains: Encoding and Decoding - I
1) Random Variables and Random Processes
2) Spike Train Statistics and Response Measure
3) Receptive fields and Models of Receptive Fields
4) The Spike Triggered Average (Coding)
5) Stimulus Reconstruction (Decoding)
Week 6: Spike Trains: Encoding and Decoding - II
1) Nonlinear approaches: Basics of Information Theory
2) Maximally Informative Dimensions
3) Discrimination based approaches
4) Measuring Spike Train Distances
5) Statistical Methods in Discrimination
Week 7: Spike Trains: Encoding and Decoding - III
1) Examples-I: Encoding/Decoding in Neural Systems
2) Examples-II: Encoding/Decoding in Neural Systems
3) Neural Population Based Encoding/Decoding - I
4) Neural Population Based Encoding/Decoding - II
5) Examples: Population Based Encoding/Decoding
Week 8: Plasticity - I
1) Synaptic Transmission and Synaptic Strength
2) Ways of Modification of Synaptic Strength
3) Types of Plasticity
4) Short Term Plasticity - I
5) Short Term Plasticity - II
Week 9: Plasticity - II
1) Implications of Short Term Plasticity
2) Long Term Plasticity - I
3) Long Term Plasticity - II
4) Modeling Long Term Plasticity
5) Computational Implications
Week 10: Plasticity - III, Modeling Phenomena with Plasticity
1) Adaptation
2) Attention
3) Learning and Memory - I
4) Learning and Memory - II
5) Developmental Changes
Week 11: Plasticity - IV, Modeling Phenomena with Plasticity
1) Conditioning and Reinforcement Learning
2) Reward Prediction (Error)
3) Decision Problems
4) Learning and Memory - II
5) Developmental Changes
Week 12: Theoretical Approaches and Current Research
1) Optimal Coding Principles - I
2) Optimal Coding Principles - II
3) Theoretical Approaches to Understanding Plasticity
4) Current Topics - I
5) Current Topics - II

Books and references

1. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems,, Dayan and Abbott
2. Signal and Systems, Oppenheim and Wilsky
3. Information Theory and Coding, Cover and Thomas 4. Nonlinear Dynamics and Chaos, Strogatz
5. Methods in Neuronal Modeling, Editors: Koch and Segev
6. Ion Channels of Excitable Membranes, Hille
7. Principles of Neural Science, Kandel and Schwartz

Instructor bio

Prof. Sharba Bandyopadhyay

IIT Kharagpur
Prof. Sharba Bandyopadhyay works in the area of Auditory Neuroscience using experimental neurophysiology, computational and theoretical methods. Sharba’s primary interest is in developmental and fast time scale plasticity

Course certificate

The course is free to enroll and learn from. But if you want a certificate, you have to register and write the proctored exam conducted by us in person at any of the designated exam centres.
The exam is optional for a fee of Rs 1000/- (Rupees one thousand only).
Date and Time of Exams: 28 October 2023 Morning session 9am to 12 noon; Afternoon Session 2pm to 5pm.
Registration url: Announcements will be made when the registration form is open for registrations.
The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published. If there are any changes, it will be mentioned then.
Please check the form for more details on the cities where the exams will be held, the conditions you agree to when you fill the form etc.

CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

Certificate will have your name, photograph and the score in the final exam with the breakup.It will have the logos of NPTEL and IIT Kharagpur .It will be e-verifiable at nptel.ac.in/noc.

Only the e-certificate will be made available. Hard copies will not be dispatched.

Once again, thanks for your interest in our online courses and certification. Happy learning.

- NPTEL team


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