The sensory areas of the cerebral cortex possess multiple topographic representations of sensory dimensions. Gradient of frequency selectivity (tonotopy) is the dominant organizational feature in the primary auditory cortex, while other feature-based organizations are less well established. We probed the topographic organization of the mouse auditory cortex at the single cell level using in vivo two-photon Ca2+ imaging. Tonotopy was present on a large scale but was fractured on a fine scale. Intensity tuning, important in level-invariant representation, was observed in individual cells but was not topographically organized. The presence or near-absence of putative sub-threshold responses revealed a dichotomy in topographic organization. Inclusion of sub-threshold responses revealed a topographic clustering of neurons with similar response properties, while such clustering was absent in supra-threshold responses. This dichotomy indicates that groups of nearby neurons with locally shared inputs can perform independent parallel computations in ACX.

Commonly used dependence measures, such as linear correlation, cross-correlogram, or Kendall's , cannot capture the complete dependence structure in data unless the structure is restricted to linear, periodic, or monotonic. Mutual information ͑MI͒ has been frequently utilized for capturing the complete dependence structure including nonlinear dependence. Recently, several methods have been proposed for the MI estimation, such as kernel density estimators ͑KDEs͒, k-nearest neighbors ͑KNNs͒, Edgeworth approximation of differential entropy, and adaptive partitioning of the XY plane. However, outstanding gaps in the current literature have precluded the ability to effectively automate these methods, which, in turn, have caused limited adoptions by the application communities. This study attempts to address a key gap in the literaturespecifically, the evaluation of the above methods to choose the best method, particularly in terms of their robustness for short and noisy data, based on comparisons with the theoretical MI estimates, which can be computed analytically, as well with linear correlation and Kendall's . Here we consider smaller data sizes, such as 50, 100, and 1000, and within this study we characterize 50 and 100 data points as very short and 1000 as short. We consider a broader class of functions, specifically linear, quadratic, periodic, and chaotic, contaminated with artificial noise with varying noise-to-signal ratios. Our results indicate KDEs as the best choice for very short data at relatively high noise-to-signal levels whereas the performance of KNNs is the best for very short data at relatively low noise levels as well as for short data consistently across noise levels. In addition, the optimal smoothing parameter of a Gaussian kernel appears to be the best choice for KDEs while three nearest neighbors appear optimal for KNNs. Thus, in situations where the approximate data sizes are known in advance and exploratory data analysis and/or domain knowledge can be used to provide a priori insights into the noise-to-signal ratios, the results in the paper point to a way forward for automating the process of MI estimation.

Subplate neurons (SPNs) are a population of neurons in the mammalian cerebral cortex that exist predominantly in the prenatal and early postnatal period. Loss of SPNs prevents the functional maturation of the cerebral cortex. SPNs receive subcortical input from the thalamus and relay this information to the developing cortical plate and thereby can influence cortical activity in a feed-forward manner. Little is known about potential feedback projections from the cortical plate to SPNs. Thus, we investigated the spatial distribution of intracortical synaptic inputs to SPNs in vitro in mouse auditory cortex by photostimulation. We find that SPNs fell into two broad classes based on their distinct spatial patterns of synaptic inputs. The first class of SPNs receives inputs from only deep cortical layers while the second class of SPNs receives inputs from deep as well as superficial layers including layer 4. We find that superficial cortical inputs to SPNs emerge in the 2nd postnatal week and that SPNs that receive superficial cortical input are located more superficially than those that do not. Our data thus suggests that distinct circuits are present in the subplate and that while SPNs participate in an early feed-forward circuit they are also involved in a feedback circuit at older ages. Together our results show that SPNs are tightly integrated into the developing thalamocortical and intracortical circuit. The feedback projections from the cortical plate might enable SPNs to amplify thalamic inputs to SPNs.

Neurons in the primary auditory cortex (A1) can show rapid changes in receptive fields when animals are engaged in sound detection and discrimination tasks. The source of a signal to A1 that triggers these changes is suspected to be in frontal cortical areas. How or whether activity in frontal areas can influence activity and sensory processing in A1 and the detailed changes occurring in A1 on the level of single neurons and in neuronal populations remain uncertain. Using electrophysiological techniques in mice, we found that pairing orbitofrontal cortex (OFC) stimulation with sound stimuli caused rapid changes in the sound-driven activity within A1 that are largely mediated by noncholinergic mechanisms. By integrating in vivo two-photon Ca 2ϩ imaging of A1 with OFC stimulation, we found that pairing OFC activity with sounds caused dynamic and selective changes in sensory responses of neural populations in A1. Further, analysis of changes in signal and noise correlation after OFC pairing revealed improvement in neural population-based discrimination performance within A1. This improvement was frequency specific and dependent on correlation changes. These OFC-induced influences on auditory responses resemble behavior-induced influences on auditory responses and demonstrate that OFC activity could underlie the coordination of rapid, dynamic changes in A1 to dynamic sensory environments.

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