Course Details

  • Course Number: 16:198:525
  • Course Type: Graduate
  • Semester 1: Fall
  • Credits: 3
  • Description:

    The course explores how computation in the human brain can be effectively modeled across the main levels of abstraction (from a single neuron to biological neural networks and system; introduces a time-sensitive computational formalization of brain function based on the model of neuron as a Spike Processing Machine – Spike Neural Networks (SNNs); and employs neuro-mimetic or neuro-inspired SNNs to tackle a problem in a term-wide project. The aim of this course is to provide the student with a solid foundation in the field.

  • Category: B (M.S.), B (Ph.D.)
  • Prerequisite Information:

    01:198:205 or 01:198:440

  • Course Links: 01:198:205 - Introduction to Discrete Structures I, 01:198:440 - Introduction to Artificial Intelligence
  • Topics:
    * Elements of Neuronal Dynamics in Biophysically realistic Neuron Models
    * Dimensionality reduction and phase plane analysis: Integrate & Fire, Izhikevich, Spike Response Models, Nonlinear Neuron Models
    * Parameter Optimization in linear and non-linear models
    * Evolving Neuronal Populations
    * The Brain as an optimization machine
    * Learning via synaptic tuning
    * Memory and Attractor Dynamics
    * Synaptic Plasticity and Learning in Spiking Neural Networks
    * Computational elements of decision making, emotions and consciousness

    Course Material: 

    Suggested Textbooks: (1) Keith L. Downing “Intelligence Emerging: Adaptivity and Search in Evolving Neural Systems” MIT Press | May 2015 (2) Peter Sterling and Simon Laughlin “Principles of Neural Design” MIT Press | March 2015 (3) Dana H. Ballard “Brain Computation as Hierarchical Abstraction” MIT Press | February 2015

  • Expected Work: A term project on Spiking Neural Networks; 2 assignments - to prepare the students for the term project; Paper presentation.
  • Exams: The usual practice is a term-project.
  • Learning Goals:

    The course provides an overview of the fundamental concepts and current trends in Neuro-morphic Computing with a focus on designing spiking neural networks for robotic vision and movement.