Experiments are complemented with computational models. It is very difficult to accurately predict the outcomes of experimental manipulations when working on a complex non-linear dynamic system like a spiking neuron with voltage-dependent conductances. Our goal is not to generate models for their own sake, although high quality computational representations of real neurons may be useful in several ways, but to use the models to provide a formal and quantitative test situation to evaluate hypotheses and determine where our understanding is weak. The comparison of these models to equivalent experiments allows us to make further predictions and develop new hypotheses; the failures of the models drive us to seek new information from experiments and lead to refinements of the models. The strength of this approach is that we can combine experimental and theoretical work in a single laboratory, which both shortens development time and allows the incorporation of many specific experimental measurements that may not necessarily be available from the literature. This approach results in fairly highly constrained models, closely tied to biophysics, and with known strengths and weaknesses with respect to the data set they represent.
Models are generated in NEURON, MATLAB, or C.
Information Processing in the Nervous System
Our fundamental interest is to understand the mechanisms used by the nervous system to process and analyze information about the sensory environment. Information processing in the nervous system operates on a number of fundamental levels: the dynamic electrical signalling of individual neurons (regulated by ion channels and their distribution within the cell membrane), the communcation between neurons that takes place largely at synapses, and the collective behavior of sets of neurons within neural circuits that are excited (or inhibited) according to external stimuli and internal conditions. Neural information processing is neither static nor uniform; rather at all levels, from molecules to networks, it depends on preceeding states, including developmental interactions with the environment, lifelong learning and training experience, and the immediate cellular and behavioral context. The sensory systems (hearing, vision, touch, smell, taste, and balance) have proven to be excellent experimental systems to study neural information processing. Sensory information processing is often easily traced from one level to the next, the external stimulus can be well controlled, and the constraints of the system and requirements for specific types of processing are often readily discernable. Previous and ongoing work in the laboratory has focussed on electrical signalling and ion channels in individual neurons, and synaptic communication between cells. A new direction that we are taking is to examine neural circuit organization and synaptic plasticity in cortex.
What do we do?
Our laboratory is interested in the mechanisms that the nervous system uses to process sensory information. For example, what makes auditory neurons uniquely suited to the tasks that they must accomplish? What kinds of synaptic and ionic mechanisms normally operate, and what kinds of plasticity are present in the system?
We recently have begun studying the effects of different forms of deafness on central auditory processing mechanisms. Cells in the nervous system sense their inputs and their own activity, and can respond by changing the proteins that they make and use. This can affect information processing.
To the immediate left is a schematic rendering of part of the circuit of the dorsal cochlear nucleus, looking en face at an isofrequency sheet (top), and looking down from the top of the nucleus at 3 such sheets (bottom). Pyramidal cells are blue, carwheel cells red, stellate cells grey, vertical cells green, and granule cells brown.
The far left image is a dorsal cochlear nucleus pyramidal cell in a brain slice, filled with AlexaFluor 488, and digitally reconstructed. Electrical activity of the cell, and currents through channels are also shown.