Overview

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 AIPREREQUISITES: 1st year college Mathematics and BiologyINDUSTRY SUPPORT: AI related industry

Syllabus

Week 1: Introduction to Neurons1) Neuron structure2) Networks of Neurons and Synapses3) System of neural processing4) Basic structures in the brain5) Sensory - Executive - Behavior systemsWeek 2: Excitable Membranes and Neural Activity1) Membrane Potential and All or None Spike2) Patch Clamp Techniques, Membrane Potential3) Ion Channels4) Current Injection - Synapses5) Single neuron activityWeek 3: Point models: Hodgkin Huxley Equations (HHE)1) Point and Compartmental Models of Neurons2) Hodgkin Huxley Equations - I3) Hodgkin Huxley Equations - II4) Reducing the HHE and Moris-Lecar Equations (MLE) 5) Properties of MLEWeek 4: Analysis of Neural Models1) Phase Plane Analysis - I2) Phase Plane Analysis - II3) Analyzing HHE4) Bifurcations5) Other Point ModelsWeek 5: Spike Trains: Encoding and Decoding - I1) Random Variables and Random Processes2) Spike Train Statistics and Response Measure3) Receptive fields and Models of Receptive Fields4) The Spike Triggered Average (Coding)5) Stimulus Reconstruction (Decoding)Week 6: Spike Trains: Encoding and Decoding - II1) Nonlinear approaches: Basics of Information Theory2) Maximally Informative Dimensions3) Discrimination based approaches4) Measuring Spike Train Distances5) Statistical Methods in DiscriminationWeek 7: Spike Trains: Encoding and Decoding - III1) Examples-I: Encoding/Decoding in Neural Systems2) Examples-II: Encoding/Decoding in Neural Systems3) Neural Population Based Encoding/Decoding - I4) Neural Population Based Encoding/Decoding - II5) Examples: Population Based Encoding/DecodingWeek 8: Plasticity - I1) Synaptic Transmission and Synaptic Strength2) Ways of Modification of Synaptic Strength3) Types of Plasticity4) Short Term Plasticity - I5) Short Term Plasticity - IIWeek 9: Plasticity - II1) Implications of Short Term Plasticity2) Long Term Plasticity - I3) Long Term Plasticity - II4) Modeling Long Term Plasticity5) Computational ImplicationsWeek 10: Plasticity - III, Modeling Phenomena with Plasticity1) Adaptation2) Attention3) Learning and Memory - I4) Learning and Memory - II5) Developmental ChangesWeek 11: Plasticity - IV, Modeling Phenomena with Plasticity1) Conditioning and Reinforcement Learning2) Reward Prediction (Error)3) Decision Problems4) Learning and Memory - II5) Developmental ChangesWeek 12: Theoretical Approaches and Current Research1) Optimal Coding Principles - I2) Optimal Coding Principles - II3) Theoretical Approaches to Understanding Plasticity4) Current Topics - I5) Current Topics - II