Professor of Neurobiology
Tel.: +44 (0)1223 336608
Our group is interested in discovering design principles that govern the structure and function of neurons and neural circuits. We record from well-defined neurons, mainly in flies’ visual systems, to measure the molecular and cellular factors that determine relevant measures of performance, such as representational capacity, dynamic range and accuracy. We combine this empirical approach with modelling to see how the basic elements of neural systems (ion channels, second messengers systems, membranes, synapses, neurons, circuits and codes) combine to determine performance. We are investigating four general problems. How are circuits designed to integrate information efficiently? How do sensory adaptation and synaptic plasticity contribute to efficiency? How do the sizes of neurons and networks relate to energy consumption and representational capacity? To what extent have energy costs shaped neurons, sense organs and brain regions during evolution? We have recently discovered how molecular noise limits the ability of axons to transmit information and ultimately the wiring density of nervous systems, how neuronal energy costs scale with performance in cells of different sizes, and a new set of interactions in fly vision that fuse sensory inputs from the ocelli and the compound eyes.
- Attwell, D. and Laughlin, S. B. (2001) An energy budget for signalling in the grey matter of the brain. Journal of Cerebral Blood Flow and Metabolism 21, 1133-1145.
- Laughlin, S. B. and Sejnowski, T. J. (2003). Communication in neuronal networks. Science 301, 1870-1874.
- Laughlin, S.B. (2004). The implications of metabolic energy requirements in the representation of information in neurons. In The Cognitive Neurosciences III, M.S. Gazzaniga, ed. (Cambridge MA: MIT Press), pp. 187-196.
- Faisal, A.A., White, J.A., and Laughlin, S.B. (2005). Ion-channel noise places limits on the miniaturization of the brain's wiring. Current Biology 15, 1143-1149.
- Parsons, M.M., Krapp, H.G., and Laughlin, S.B. (2006). A motion-sensitive neurone responds to signals from the two visual systems of the blowfly, the compound eyes and the ocelli. Journal of Experimental Biology 209, 4464-4474.
- Niven, J.E., Anderson, J.C., and Laughlin, S.B. (2007). Fly photoreceptors demonstrate energy-information trade-offs in neural coding PLoS Biology 5,
- Faisal, A.A. & Laughlin, S.B. (2007). Stochastic simulations on the Reliability of Action Potential Propagation in Thin Axons. PLoS Computational Biology. 3(5): e79.