Fast and robust estimation of spectro-temporal receptive fields using stochastic approximations.
BACKGROUND
The receptive field (RF) represents the signal preferences of sensory neurons and is the primary analysis method for understanding sensory coding. While it is essential to estimate a neuron's RF, finding numerical solutions to increasingly complex RF models can become computationally intensive, in particular for high-dimensional stimuli or when many neurons are involved.


METHODS
Here we propose an optimization scheme based on stochastic approximations that facilitate this task. The basic idea is to derive solutions on a random subset rather than computing the full solution on the available data set. To test this, we applied different optimization schemes based on stochastic gradient descent (SGD) to both the generalized linear model (GLM) and a recently developed classification-based RF estimation approach.


RESULTS
Using simulated and recorded responses, we demonstrate that RF parameter optimization based on state-of-the-art SGD algorithms produces robust estimates of the spectro-temporal receptive field (STRF). Results on recordings from the auditory midbrain demonstrate that stochastic approximations preserve both predictive power and tuning properties of STRFs. A correlation of 0.93 with the STRF derived from the full solution may be obtained in less than 10% of the full solution's estimation time. We also present an on-line algorithm that allows simultaneous monitoring of STRF properties of more than 30 neurons on a single computer.


CONCLUSIONS
The proposed approach may not only prove helpful for large-scale recordings but also provides a more comprehensive characterization of neural tuning in experiments than standard tuning curves.
Medizinische Physik and Cluster of Excellence Hearing4all, Carl von Ossietzky University, D-26111 Oldenburg, Germany. Electronic address: arne.f.meyer@uni-oldenburg.de.
Arne F
AF
Meyer
Leibniz Institute for Neurobiology, D-39118 Magdeburg, Germany. Electronic address: jan-philipp.diepenbrock@lin-magdeburg.de.
Jan-Philipp
JP
Diepenbrock
Leibniz Institute for Neurobiology, D-39118 Magdeburg, Germany; Institute of Biology, Otto-von-Guericke University, D-39118 Magdeburg, Germany. Electronic address: frank.ohl@lin-magdeburg.de.
Frank W
FW
Ohl
Medizinische Physik and Cluster of Excellence Hearing4all, Carl von Ossietzky University, D-26111 Oldenburg, Germany. Electronic address: joern.anemueller@uni-oldenburg.de.
Jörn
J
Anemüller
D000161:Acoustic Stimulation; D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D001307:Auditory Perception / Q000502:physiology; D003198:Computer Simulation; D005849:Gerbillinae; D007245:Inferior Colliculi / Q000166:cytology*; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D013269:Stochastic Processes
Auditory system; Generalized linear model; On-line learning; Receptive field; Sensory coding; Stochastic gradient descent