Inhibitory interneurons in the cerebral cortex include a vast array of subtypes, varying in their molecular signatures, electrophysiological properties, and connectivity patterns. This diversity suggests that individual inhibitory classes have unique roles in cortical circuits; however, their characterization to date has been limited to broad classifications including many subtypes. We used the Cre/LoxP system, specifically labeling parvalbumin(PV)-expressing interneurons in visual cortex of PV-Cre mice with red fluorescent protein (RFP), followed by targeted loose-patch recordings and two-photon imaging of calcium responses in vivo to characterize the visual receptive field properties of these cells. Despite their relative molecular and morphological homogeneity, we find that PV+ neurons have a diversity of feature-specific visual responses that include sharp orientation and direction-selectivity, small receptive fields, and band-pass spatial frequency tuning. These results suggest that subsets of parvalbumin interneurons are components of specific cortical networks and that perisomatic inhibition contributes to the generation of precise response properties.

Two-photon calcium imaging is now an important tool for in vivo imaging of biological systems. By enabling neuronal population imaging with subcellular resolution, this modality offers an approach for gaining a fundamental understanding of brain anatomy and physiology. Proper analysis of calcium imaging data requires denoising, that is separating the signal from complex physiological noise. To analyze two-photon brain imaging data, we present a signal plus colored noise model in which the signal is represented as harmonic regression and the correlated noise is represented as an order autoregressive process. We provide an efficient cyclic descent algorithm to compute approximate maximum likelihood parameter estimates by combing a weighted least-squares procedure with the Burg algorithm. We use Akaike information criterion to guide selection of the harmonic regression and the autoregressive model orders. Our flexible yet parsimonious modeling approach reliably separates stimulus-evoked fluorescence response from background activity and noise, assesses goodness of fit, and estimates confidence intervals and signal-to-noise ratio. This refined separation leads to appreciably enhanced image contrast for individual cells including clear delineation of subcellular details and network activity. The application of our approach to in vivo imaging data recorded in the ferret primary visual cortex demonstrates that our method yields substantially denoised signal estimates. We also provide a general Volterra series framework for deriving this and other signal plus correlated noise models for imaging. This approach to analyzing two-photon calcium imaging data may be readily adapted to other computational biology problems which apply correlated noise models.

Astrocytes form an intricate partnership with neural circuits to influence numerous cellular and synaptic processes. One prominent organizational feature of astrocytes is the "tiling" of the brain with non-overlapping territories. There are some documented species and brain region-specific astrocyte specializations, but the extent of astrocyte diversity and circuit specificity are still unknown. We quantitatively defined the rules that govern the spatial arrangement of astrocyte somata and territory overlap in ferret visual cortex using a combination of in vivo two-photon imaging, morphological reconstruction, immunostaining, and model simulations. We found that ferret astrocytes share, on average, half of their territory with other astrocytes. However, a specific class of astrocytes, abundant in thalamo-recipient cortical layers ("kissing" astrocytes), overlap markedly less. Together, these results demonstrate novel features of astrocyte organization indicating that different classes of astrocytes are arranged in a circuit-specific manner and that tiling does not apply universally across brain regions and species. J. Comp. Neurol. 524:3561-3576, 2016. © 2016 The Authors The Journal of Comparative Neurology Published by Wiley Periodicals, Inc.