Searching for optimal sensory signals: iterative stimulus reconstruction in closed-loop experiments
Frederik Edin, Christian K. Machens, Hartmut Schütze & Andreas V. M. Herz
Journal of Computational Neuroscience 17(1): 47-56 (2004)
Abstract
Shaped by evolutionary processes, sensory systems often represent
behaviorally relevant stimuli with higher fidelity than other
stimuli. The stimulus dependence of neural reliability could
therefore provide an important clue in a search for relevant
sensory signals. We explore this relation and introduce a novel
iterative algorithm that allows one to find stimuli that are
reliably represented by the sensory system under study. To assess
the quality of a neural representation, we use stimulus
reconstruction methods. The algorithm starts with the presentation
of an initial stimulus (e.g. white noise). The evoked spike train
is recorded and used to reconstruct the stimulus online. Within a
closed-loop setup, this reconstruction is then played back to the
sensory system. Iterating this procedure, the newly generated
stimuli can be better and better reconstructed. We demonstrate the
feasibility of this method by applying it to auditory receptor
neurons in locusts. Our data show that the optimal stimuli often
exhibit pronounced sub-threshold periods that are interrupted by
short, yet intense pulses. Similar results are obtained for simple
model neurons and suggest that these stimuli are encoded with high
reliability by a large class of neurons.
Last modified: Fri Nov 28 11:22:42 CET 2008