The Neuromorphics Lab (NL) serves graduate trainees in Boston University's Program in Cognitive and Neural Systems.

Several members of the NL were trained in the Department of Cognitive and Neural Systems (CNS). Many aspects of the CNS's graduate training mission have been taken over by the Graduate Program for Neuroscience’s Computational Neuroscience specialty. Graduate courses developed in the CNS Department and currently taught by NL faculty members include:

CAS CN510: Principles and Methods of Cognitive and Neural Modeling I: Neural modeling is an interdisciplinary paradigm for discovering the computational designs that underlie human and animal learning and performance. This graduate-level course explores elements of the psychological, biological, mathematical, and computational foundations of behavioral and brain modeling. The course integrates experimental data and theoretical concepts in an interdisciplinary format. Instructor: Anatoli Gorchetchnikov. More info

CAS CN530: Neural and Computational Models of Vision: This course explores the psychological, biological, mathematical and computational foundations of visual perception. Lectures and readings combine with simulation and essay assignments to provide an intensive and self-contained examination of core issues in early and middle visual processing. Mathematically specified neural and computational models elucidate the structure and dynamics of the mammalian visual system. Emphasis is placed on understanding the psychophysics and physiology of mammalian vision, both as a means of better understanding our own human intelligence, and as a foundation for tomorrow's machine vision architectures and algorithms. While some of the models developed in recent years at Boston University's Center for Adaptive Systems (CAS) and Department of Cognitive and Neural Systems (CNS) are covered in depth, selected models by a variety of researchers are compared and contrasted. Instructor: Ennio Mingolla. More info

CAS CN550: Neural and Computational Models of Recognition, Memory and Attention: This course develops neural network models of how internal representations of sensory events and cognitive hypotheses are learned and remembered, and of how such representations enable recognition and recall of these events to occur. Various neural and statistical pattern recognition models are analyzed. Special attention is given to stable self-organization of pattern recognition and recall codes by Adaptive Resonance Theory (ART) models. Mathematical techniques and definitions to support fluent access to the neural network and pattern recognition literature are developed throughout the course. Experimental data and theoretical predictions from cognitive psychology, neuropsychology, and neurophysiology of normal and abnormal individuals are also analyzed. Course work emphasizes skill development, including writing, computational analysis, teamwork, and verbal communication. Instructors: Heather Ames, Anatoli Gorchetchnikov. More info

CAS CN730: Models of Visual Perception, Spring 2011: This course offers an advanced survey of selected topics of current interest in the neural and computational modeling of mammalian vision. This year's topics include perceptual consequences of eye movements, visual search, object recognition, and perceptual learning. Some classes will be held at laboratories of nearby institutions. Students are expected to have a sufficient interdisciplinary grounding in the fundamentals of computational modeling of mammalian vision to read primary research sources extensively. A term project that combines a problem statement, literature review, and either (1) simulation of a model or (2) a design for a psychophysical experiment is required. Instructors: Ennio Mingolla, Arash Yazdanbakhsh. More info

CAS CN810: Topics in adaptive mobile robotics: This course offers a hands-on introduction to the challenges of implementing adaptive behavior on an iRobot Create platform. Using simple and reliable hardware, students will design and implement computational models that allow the robot to perform behavioral functions, such as learning to approach or avoid objects and locations in its environment. Students are expected to have a sufficient interdisciplinary grounding in the fundamentals of computational modeling of mammalian vision, planning, and navigation to read primary research sources. Instructor: Massimiliano Versace. More info


The Neuromorphics Lab is highly collaborative with connections across both academia and industry.