Course Identification

Theoretical Neuroscience

Lecturers and Teaching Assistants

Prof. Elad Schneidman
Dr. Tal Tamir

Course Schedule and Location

Second Semester
Sunday, 16:15 - 18:00, FGS, Rm C

Field of Study, Course Type and Credit Points

Life Sciences: Lecture; Elective; 2.00 points
Physical Sciences: Lecture; 2.00 points
Life Sciences (Systems Biology Track): Lecture; 2.00 points
Life Sciences (Molecular and Cellular Neuroscience Track): Lecture; 2.00 points
Life Sciences (Brain Sciences: Systems, Computational and Cognitive Neuroscience Track): Lecture; Core; 2.00 points
Mathematics and Computer Science: Lecture; 2.00 points


No lecture on 22/4.


Background in linear algebra, probability theory, programming.



Language of Instruction


Attendance and participation

Expected and Recommended

Grade Type

Numerical (out of 100)

Grade Breakdown (in %)


Evaluation Type

Final assignment

Scheduled date 1

Final project submission- Aug. 15

Estimated Weekly Independent Workload (in hours)



The course will introduce students to theoretical models and computational approaches in neuroscience. Topics to be covered include neuronal encoding and decoding, analysis of dynamical neural systems, biophysical modeling of single neurons and networks, learning paradigms in neural network theory and contemporary applications in brain research.
Part A: The Neural Code:
  1. Introduction to the neural code
  2. Neuronal encoding and decoding
  3. Noise, information, and population of neurons
  4. Stimulus statistics, neural adaptation, and optimal coding
Part B: From single Neurons to Network models
  1. Integrate and fire models, neural excitability, and conductance-based models
  2. Basic concepts in dynamical systems and single neuron dynamics
  3. Network models
Part C: Learning and Memory in Simplified Neural Models
  1. The linear perceptron and basic concepts in learning theory
  2. The Hopfield model for associative memory
  3. Multilayered networks, back-propagation, Boltzmann machines, and beyond
  4. Classical conditioning and reinforcement learning
Part D: Modeling Brain Function
  1. Sensory perception and decision theory
  2. Examples of neuronal network models in brain research (e.g., the ring model for orientation selectivity, attractor neural network models of place cells)

Learning Outcomes

Upon successful completion of this course students should be able to:

  1. Apply central theoretical models and computational approaches in neuroscience to various computational problems and experimental data. 

Reading List

  • Theoretical Neuroscience [P. Dayan and L.F. Abbott] 
  • Biophysics: Searching for Principles [W. Bialek]
  • Modeling Brain Function [DJ Amit]