Mathematical Sciences:Mathematical Modeling of Neural Populations

点击翻译按钮获取中文摘要

Abbott The investigator studies how the dynamic properties of large populations of neurons allow them to represent, store, recall and process information. A basic theme of the project is the study of population coding, the storage of information in large arrays of neurons, each responding fairly nonselectively to different aspects of a stimulus. The transfer of information between neuronal networks is studied using population decoding methods. Questions to be addressed include: How is the information encoded in a sensory network transferred to a motor network in a task like visually guided reaching toward an object? How do the invariant representations needed to identify and locate an object independent of translations, rotations, and scaling changes arise? It is widely believed that synaptic modification is the basic neuronal mechanism underlying memory and learning. Population decoding methods allow for an interpretation of the effects of synaptic modification with direct behavioral relevance. In preliminary decoding studies it has been found that the temporal properties of long-term potentiation naturally cause a population of neurons to predict a coded quantity after training. This suggests a novel mechanism for learning and generating sequences of motor actions that is explored. Reaching for an object, turning toward a visual or auditory cue, and a variety of other tasks require motor responses that are accurately guided by sensory information. In addition, previous experience can play an important role in shaping these responses. Experiments have provided important information about how sensory information is represented in the brain and how experience can modify neural circuits. Mathematical decoding techniques allow us to relate motor responses, such as reaching, to the activity of neurons in the motor cortex of the brain. The goal of the project is to construct a theoretical framework, based on these data, for understanding sensory-guid ed motor tasks, including mechanisms for learning and generating accurate responses and modifying them on the basis of experience. This work should provide new insights into how we generate basic motor behaviors and may have interesting applications to robotics.