In 1988, co-author Apostolos Georgopoulos and his colleagues showed in monkeys that from the activity of neurons in the motor cortex, the population vector algorithm can predict the monkey's upcoming arm movement. However, to achieve a more accurate prediction with this algorithm, upwards of 200 neurons were needed. Thus, the present discovery that a highly accurate neural code carrying information about target direction can be achieved with just 16 neurons is enlightening, and could have applications in the development of bioinspired robots. (Georgopolos is an MD-PhD at the University of Minnesota/Veterans Administration Medical Center who is interested in the development of prosthetics.)
Randy Schekman, PhD, editor-in-chief of PNAS, describes the papers chosen for the Cozzarelli Prize as being "of exceptional interest These papers are not merely technically superior but have had special impact and maybe novel techniques or novel applications of techniques, or very important discoveries."
For this study, Gonzalez-Bellido and Trever Wardill (then at HHMI) developed a new protocol for labeling and confocal imaging of neurons in thick invertebrate tissue samples. In addition, her co-authors and former HHMI colleagues Hanchuan Peng and Jinzhu Yang developed a method for automatic 3D digital reconstruction (tracing) of neurons in microscopic images.
Gonzalez-Bellido sees the dragonfly as a promising model for understanding the evolution of neural systems. "It's exciting that the same computation [the population vector algorithm] is used by monkeys and dragonflies for this task. Dragonflies belong to the most ancient groups of flying insects on earth, and they have changed little in 250 million years" she says.
The Cozzarelli Award was established in 2005 and named in 2007 to honor l
|Contact: Diana Kenney|
Marine Biological Laboratory