Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Course Overview
The Computational Neuroscience certification course explores fundamental queries on learning, memory, and decision-making by quantifying neuronal activity and modeling spiking. Students delve into how neurons represent information and its practical applications, culminating in computational models elucidating plasticity's role in learning, memory, and cognition.
Catering to Neural/Cognitive Sciences and AI enthusiasts, the Computational Neuroscience certification by Swayam offers a deep dive into Biological Sciences and Computational Biology. Emphasizing the industry's AI support, it equips students with insights into neural computation's real-world implications, preparing them for multifaceted challenges in understanding brain mechanisms and AI advancements.
Also Read: Online Neural Networks Certification Courses
The Highlights
- Elective Online Course
- 12 Weeks Duration
- Offered by IIT Kharagpur
Programme Offerings
- Certificate of completion
- Practical Learning
- Hands-on Experience
Courses and Certificate Fees
| Fees Informations | Certificate Availability | Certificate Providing Authority |
|---|---|---|
| INR 1000 | yes | IIT Kharagpur |
The Computational Neuroscience certification fees is free. However, if you want a certificate, you must register and take the proctored exam at designated centres, which is optional and comes with a fee of Rs 1000.
Computational Neuroscience Certification Fee Structure
Particulars | Total Fees |
Computational Neuroscience (exam) | Rs 1000/- |
Eligibility Criteria
Academic Qualifications
You need to have completed first-year college courses in Mathematics and Biology before taking the Computational Neuroscience certification course.
Certification Qualifying Details
To receive the Computational Neuroscience certification by Swayam, you need a minimum average assignment score of 10/25 and an exam score of 30/75.
What you will learn
After completing the Computational Neuroscience certification syllabus, you will be able to understand the intricate processes of information handling within the brain's neurons. You will learn how to quantify neuronal activity and model spiking in individual neurons, laying the groundwork for a nuanced comprehension of neural computation.
Upon completion of the Computational Neuroscience training, you will gain insights into the critical concept of plasticity, a cornerstone of the brain's functionality, impacting learning, memory, and cognition. You will also be equipped with computational modelling skills, allowing you to implement and analyse plasticity in the context of neural processes.
Who it is for
The Computational Neuroscience online course is designed for students interested in Neural and Cognitive Sciences and AI. This course is also beneficial for:
Admission Details
Follow these steps to join the Computational Neuroscience classes:
Step 1: Browse the URL below:
https://onlinecourses.nptel.ac.in/noc23_bt64/preview
Step 2: Click on the “Sign-in/ Register” button
Step 3: Fill out the necessary details and submit the form
The Syllabus
- Neuron structure
- Networks of Neurons and Synapses
- System of neural processing
- Basic structures in the brain
- Sensory - Executive - Behavior systems
- Membrane Potential and All or None Spike
- Patch Clamp Techniques, Membrane Potential
- Ion Channels
- Current Injection - Synapses
- Single neuron activity
- Point and Compartmental Models of Neurons
- Hodgkin Huxley Equations - I
- Hodgkin Huxley Equations - II
- Reducing the HHE and Moris-Lecar Equations (MLE) 5) Properties of MLE
- Phase Plane Analysis - I
- Phase Plane Analysis - II
- Analyzing HHE
- Bifurcations
- Other Point Models
- Random Variables and Random Processes
- Spike Train Statistics and Response Measure
- Receptive Fields and Models of Receptive Fields
- The Spike Triggered Average (Coding)
- Stimulus Reconstruction (Decoding)
- Nonlinear approaches: Basics of Information Theory
- Maximally Informative Dimensions
- Discrimination based approaches
- Measuring Spike Train Distances
- Statistical Methods in Discrimination
- Examples-I: Encoding/Decoding in Neural Systems
- Examples-II: Encoding/Decoding in Neural Systems
- Neural Population Based Encoding/Decoding - I
- Neural Population Based Encoding/Decoding - II
- Examples: Population Based Encoding/Decoding
- Synaptic Transmission and Synaptic Strength
- Ways of Modification of Synaptic Strength
- Types of Plasticity
- Short-Term Plasticity - I
- Short-Term Plasticity - II
- Implications of Short-Term Plasticity
- Long-Term Plasticity - I
- Long-Term Plasticity - II
- Modeling Long-Term Plasticity
- Computational Implications
- Adaptation
- Attention
- Learning and Memory - I
- Learning and Memory - II
- Developmental Changes
- Conditioning and Reinforcement Learning
- Reward Prediction (Error)
- Decision Problems
- Learning and Memory - II
- Developmental Changes
- Optimal Coding Principles - I
- Optimal Coding Principles - II
- Theoretical Approaches to Understanding Plasticity
- Current Topics - I
- Current Topics - II
Evaluation process
The Computational Neuroscience certification offers an optional exam. Your final score is determined by averaging the best 8 out of 12 assignments (25%) and your proctored exam score (75%).
Instructors
IIT Kharagpur Frequently Asked Questions (FAQ's)
A prerequisite for this course is the completion of first-year college-level courses in Mathematics and Biology.
The course covers fundamental aspects of computational neuroscience, including quantifying neuronal activity, modeling spiking, understanding information representation by neurons, and implementing plasticity.
The course is intended for students interested in Neural and Cognitive Sciences and AI, providing valuable insights into the computational aspects of brain function.
You will develop skills in quantifying neuronal activity, computational modeling, and plasticity analysis, enabling you to address complex questions related to learning, memory, and cognition.
The course provides a solid foundation for careers in AI by offering insights into neural computation and its applications, making graduates well-equipped for roles such as Computational Neuroscientist, Neural Data Analyst, or AI Researcher.