George N. Reeke Jr., Ph.D.
Signals exchanged between cells contribute to the complexity that makes the behavior of biological systems hard to predict. Reeke employs computer simulations to investigate how the components of biological systems work together to carry out complex functions. He is specifically interested in modeling neuronal systems to better understand how physical phenomena within them give rise to perception, motor control, and memory.
As the nervous system develops, it must acquire adaptive behavior through interactions with the environment. Reeke’s research simulates neural systems based on experimental neurophysiology with simulated or real sensors and locomotor organs. It then challenges those systems to perform various tasks, shedding light on sensory integration, perceptual categorization, motor control, and aspects of memory in both normal and damaged or diseased brains. These models have shown how the ability to recognize objects and events in the environment can arise in the nervous system as a result of the operation of selective processes guided by innate value systems. Reeke’s research indicates that there is no need for built-in representational codes or computational algorithms, nor for feedback of error signals from omniscient external teachers. The results call into question the popular theory that the brain is a kind of computer.
Areas of particular interest in Reeke’s lab are perception, control of locomotion, and the development of analytic tools appropriate for the characterization of these activities in space and time. He is focusing on neural mechanisms for the recognition and recall of temporal patterns, which are of fundamental importance for planning and navigation, language, and music. The lab has developed novel information-theoretic measures, based on the temporal intervals between events of interest, that help to quantitate the temporal characteristics of the signals exchanged among neurons and the spatiotemporal discharge activity of neuronal assemblies.
Reeke has shown that these same measures can be used to analyze data obtained from behavioral animal experiments. In a collaborative project with Rockefeller’s Donald W. Pfaff, Reeke helped measure the contribution of estrogens to exploratory behavior in female mice. The method could also be adapted to measure the contribution of a range of genes to behavior, as well as to understand the effects of different genotypes or pharmacological manipulations on behavior.
Reeke’s lab has also developed a composite approach to modeling neurons in which the degree of detail employed in modeling different membrane conductances is adapted to the dynamical complexity of each neuron. For example, their analysis of a model cerebellar Purkinje cell shows that when both the rate and the timing of a stimulus are varied, novel cell discharge behavior emerges. Studies of large-scale systems incorporating these techniques provide a greater understanding of how different types of neurons in both normal and diseased brains function at the integrated system level. Members of the lab have applied these methods to study non-classical responses of cells in the primary visual cortex, the process by which juvenile birds learn their songs, how general anesthetics affect the activities of neuronal clusters in the cerebral cortex, and the generation of descriptive models of sensorimotor contingencies and their relationship to reward in the cerebellum and other brain regions.