title: Population decoding in rat barrel cortex: optimizing the linear readout of correlated population responses.
abstract: Sensory information is encoded in the response of neuronal populations. How might this information be decoded by downstream neurons? Here we analyzed the responses of simultaneously recorded barrel cortex neurons to sinusoidal vibrations of varying amplitudes preceded by three adapting stimuli of 0, 6 and 12 µm in amplitude. Using the framework of signal detection theory, we quantified the performance of a linear decoder which sums the responses of neurons after applying an optimum set of weights. Optimum weights were found by the analytical solution that maximized the average signal-to-noise ratio based on Fisher linear discriminant analysis. This provided a biologically plausible decoder that took into account the neuronal variability, covariability, and signal correlations. The optimal decoder achieved consistent improvement in discrimination performance over simple pooling. Decorrelating neuronal responses by trial shuffling revealed that, unlike pooling, the performance of the optimal decoder was minimally affected by noise correlation. In the non-adapted state, noise correlation enhanced the performance of the optimal decoder for some populations. Under adaptation, however, noise correlation always degraded the performance of the optimal decoder. Nonetheless, sensory adaptation improved the performance of the optimal decoder mainly by increasing signal correlation more than noise correlation. Adaptation induced little systematic change in the relative direction of signal and noise. Thus, a decoder which was optimized under the non-adapted state generalized well across states of adaptation.
authors.affiliation: School of Psychology, University of New South Wales, Sydney, New South Wales, Australia ; Eccles Institute of Neuroscience, John Curtin School of Medical Research, The Australian National University, Canberra, Australian Capital Territory, Australia.
authors.forename: Mehdi
authors.initials: M
authors.lastname: Adibi
authors.affiliation: School of Psychology, University of New South Wales, Sydney, New South Wales, Australia.
authors.forename: James S
authors.initials: JS
authors.lastname: McDonald
authors.affiliation: School of Psychology & Australian Centre of Excellence in Vision Science, University of Sydney, Sydney, New South Wales, Australia.
authors.forename: Colin W G
authors.initials: CW
authors.lastname: Clifford
authors.affiliation: School of Psychology, University of New South Wales, Sydney, New South Wales, Australia ; Eccles Institute of Neuroscience, John Curtin School of Medical Research, The Australian National University, Canberra, Australian Capital Territory, Australia.
authors.forename: Ehsan
authors.initials: E
authors.lastname: Arabzadeh
mesh_terms: D000222:Adaptation, Physiological / Q000502:physiology; D000465:Algorithms; D000818:Animals; D002540:Cerebral Cortex / Q000473:pathology*; D055724:Electrophysiological Phenomena; D016014:Linear Models; D008297:Male; D009474:Neurons / Q000502:physiology; D012372:ROC Curve; D051381:Rats; D017208:Rats, Wistar; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio; D013003:Somatosensory Cortex / Q000502:physiology