View a presentation by Athanassios (Thanos) Siapas, Associate Professor of Computation and Neural Systems, on the organization of neuronal activity in the hippocampus and how these activity patterns may support learning and memory formation.
[View Presentation] 7.26.10
Erik Winfree, Associate Professor of Computer Science, Computation and Neural Systems, and Bioengineering, and colleagues from Columbia University, Arizona State University, and the University of Michigan have programmed an autonomous molecular "robot" made out of DNA to start, move, turn, and stop while following a DNA track. The development could ultimately lead to molecular systems that might one day be used for medical therapeutic devices and molecular-scale reconfigurable robots—robots made of many simple units that can reposition or even rebuild themselves to accomplish different tasks. Read More... 5.12.10
Vidhya Navalpakkam, Postdoctoral Scholar in Biology; Christof Koch, Lois and Victor Troendle Professor of Cognitive and Behavioral Biology and Professor of Computation and Neural Systems; Antonio Rangel, Associate Professor of Economics; and Pietro Perona, Allen E. Puckett Professor of Electrical Engineering, have found that the brain combines value and visual saliency information rapidly and near-optimally to perform maximal reward harvesting while seeking multiple targets in the environment. This finding has implications for how consumers may make fast choices in shopping displays. Read More... 4.2.10
A good choice involves rapidly combining both value and visual information. Here, the fly chooses the food reward offered by the salient flower, but pays a devastating price falling prey to the camouflaged crab spider. From: http://www.ojibway.ca/spider18.jpg
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How does the brain compute? Can we endow machines with brain-like computational capability? Faculty and students in the CNS program ask these questions with the goal of understanding the brain and designing systems that show the same degree of autonomy and adaptability as biological systems. Disciplines such as neurobiology, electrical engineering, computer science, physics, statistical machine learning, control and dynamical systems analysis, and psychophysics contribute to this understanding. The unifying theme is the relationship between the physical structure of a computational system (molecular, neuronal, or electronic hardware), the dynamics of its operation, and the computational problems that it can efficiently solve.
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A complete list of speakers including additional information such as talk titles and abstracts can be found on the CNS Wiki Page.