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Brain-Inspired Computing


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.

B (Ph.D.)
* 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.

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. 

Teaching Professors Names: 
Konstantinos Michmizos