- This event has passed.
Seminar: Professor Simon Laughlin
July 6, 2017 @ 4:00 pm - 5:30 pm
Thursday 6th of July @ 4 pm – 5:30 pm @ the Le Gros Clark Lecture Theatre
We are very honoured to be hosting Simon Laughlin from the University of Cambridge, who will be talking at Cortex Club about his research on Thursday, 6th of July.
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?
Spectacular advances in molecular neurobiology show how brain’s winning “technology”, molecular cell biology, supports prodigious competence. Indeed, the ability of neurons and glia to draw on an inventory of over 50,000 signalling molecules, to connect them in many ways into innumerable circuits, and to modify molecules and circuits according to ongoing activity, raises a question. What is it that brains cannot do? I want to discuss with you the proposition that wisdom can be gained by identifying limits to competence, working out their effects and discovering how and why they generate principles of neural design (Sterling and Laughlin, 2015). To start the ball rolling I will focus on cell biological and molecular constraints on speed, rate and accuracy, and hence representational capacity. Using some well-established examples and examples from our more recent work I will able to demonstrate how these limitations are countered by sparse distributed codes, by economizing on synapses, and by using an economical circuit motif. But can my examples help you to understand how cortical circuits process information? Please discuss. If time permits I would like to suggest that wisdom can be gained by considering a second set of limits, the techniques and ideas that drive our research. Could neuroscientists, like the brain, adapt to be more efficient?
Sterling P & Laughlin SB Principles of Neural Design. MIT Press, Cambridge Mass. 2015