The Center for Adaptive Systems (CAS) is an interdisciplinary research and training center whose interests intersect the areas of biology, computer science, engineering, mathematics, and psychology. The Center performs interdisciplinary research aimed at discovering and developing principled theories of brain and behavior, notably concerning how individual humans and animals adapt so well on their own to rapidly changing environments that may include rare, ambiguous, and unexpected events. The Center also develops technological applications that are inspired by its biological models. Research and training are carried out both individually and through close collaborative relationships between faculty, students, and postdoctoral fellows. Research projects encompass a broad range of areas concerning cognitive and neural systems, including vision and image processing; audition, speech and language understanding; adaptive pattern recognition; cognitive information processing; self-organization and development; associative learning and long-term memory; reinforcement and motivation; attention; adaptive sensory-motor planning, control and robotics; navigation and spatial orientation; biological rhythms; consciousness; and the mathematical and computational methods needed to support advanced modeling research and applications. Both normal and abnormal behaviors are analyzed, including Parkinson’s disease, attention deficit disorder, schizophrenia, and depression.

These investigations lead to neural network models that clarify the functional architecture of different brain regions. Recent models characterize the functional organization of such brain areas as the visual cortex, auditory cortex, temporal cortex, parietal cortex, motor cortex, prefrontal cortex, hippocampus, hypothalamus, cerebellum, superior colliculus, basal ganglia, reticular formation, thalamus, retina, and spinal cord.

General neural designs that realize specialized functional roles in distinct brain regions are clarified through such models. Different levels of organization are analyzed, ranging from neural systems and architectures to neural modules, local circuits, and cellular, biophysical, and biochemical mechanisms. For example, CAS and CNS have led the way in modeling how and why the architecture of all sensory and cognitive neocortex is organized into layered circuits. This research clarifies how “laminar computing” contributes to biological intelligence. Such cortical laminar cortical architectures are under investigation in vision, recognition learning and categorization, short-term memory, cognitive information processing, and sensory-motor planning. A typical example on the module level is opponent processing circuits by on-cells and off-cells. Specialized versions of this module play a key role in vision, biological rhythms, reinforcement learning, motor control, and cognitive information processing. Such a comparative analysis clarifies how a single modular design may be adapted to many different behavioral functions. A typical example on the mechanism level is associative learning, which plays a key role in such varied behaviors as recognition, spatial orientation, and sensory-motor control. Contributions of the specialized electrical and chemical dynamics of individual cells are analyzed in every model. The models also provide explanations and predictions of data that link the several levels of behavior, evoked potentials, neurophysiology, anatomy, biophysics, and biochemistry.

These neural models are typically naturally expressed as nonlinear dynamical systems. Numerical and analytical investigations of these systems lead to new mathematical results and problems, as well as to formal bridges to other biological and physical systems, notably dissipative systems that describe aspects of self-organization and nonequilibrium behavior. These formal investigations suggest new designs for computer vision, adaptive pattern recognition machines, autonomous robots, and massively parallel computers, thereby integrating basic science with the design of novel technologies. Faculty and students also interact with working engineers in companies and government laboratories to implement neural network designs in new hardware for technological applications.

As a part of Boston’s large academic community, the Center has facilitated active collaborations among scientists at neighboring universities and research laboratories. In addition, Boston’s prime location leads to a steady stream of national and international visitors.

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