The Statistical Structure of the Hippocampal Code for Space as a Function of Time, Context, and Value.

Hippocampal activity represents many behaviorally important variables, including context, an animal's location within a given environmental context, time, and reward. Using longitudinal calcium imaging in mice, multiple large virtual environments, and differing reward contingencies, we derived a unified probabilistic model of CA1 representations centered on a single feature-the field propensity. Each cell's propensity governs how many place fields it has per unit space, predicts its reward-related activity, and is preserved across distinct environments and over months. Propensity is broadly distributed-with many low, and some very high, propensity cells-and thus strongly shapes hippocampal representations. This results in a range of spatial codes, from sparse to dense. Propensity varied ∼10-fold between adjacent cells in salt-and-pepper fashion, indicating substantial functional differences within a presumed cell type. Intracellular recordings linked propensity to cell excitability. The stability of each cell's propensity across conditions suggests this fundamental property has anatomical, transcriptional, and/or developmental origins.

Cortical Neural Activity Predicts Sensory Acuity Under Optogenetic Manipulation.

Excitatory and inhibitory neurons in the mammalian sensory cortex form interconnected circuits that control cortical stimulus selectivity and sensory acuity. Theoretical studies have predicted that suppression of inhibition in such excitatory-inhibitory networks can lead to either an increase or, paradoxically, a decrease in excitatory neuronal firing, with consequent effects on stimulus selectivity. We tested whether modulation of inhibition or excitation in the auditory cortex of male mice could evoke such a variety of effects in tone-evoked responses and in behavioral frequency discrimination acuity. We found that, indeed, the effects of optogenetic manipulation on stimulus selectivity and behavior varied in both magnitude and sign across subjects, possibly reflecting differences in circuitry or expression of optogenetic factors. Changes in neural population responses consistently predicted behavioral changes for individuals separately, including improvement and impairment in acuity. This correlation between cortical and behavioral change demonstrates that, despite the complex and varied effects that these manipulations can have on neuronal dynamics, the resulting changes in cortical activity account for accompanying changes in behavioral acuity.SIGNIFICANCE STATEMENT Excitatory and inhibitory interactions determine stimulus specificity and tuning in sensory cortex, thereby controlling perceptual discrimination acuity. Modeling has predicted that suppressing the activity of inhibitory neurons can lead to increased or, paradoxically, decreased excitatory activity depending on the architecture of the network. Here, we capitalized on differences between subjects to test whether suppressing/activating inhibition and excitation can in fact exhibit such paradoxical effects for both stimulus sensitivity and behavioral discriminability. Indeed, the same optogenetic manipulation in the auditory cortex of different mice could improve or impair frequency discrimination acuity, predictable from the effects on cortical responses to tones. The same manipulations sometimes produced opposite changes in the behavior of different individuals, supporting theoretical predictions for inhibition-stabilized networks.

Bidirectional Regulation of Innate and Learned Behaviors That Rely on Frequency Discrimination by Cortical Inhibitory Neurons.

The ability to discriminate tones of different frequencies is fundamentally important for everyday hearing. While neurons in the primary auditory cortex (AC) respond differentially to tones of different frequencies, whether and how AC regulates auditory behaviors that rely on frequency discrimination remains poorly understood. Here, we find that the level of activity of inhibitory neurons in AC controls frequency specificity in innate and learned auditory behaviors that rely on frequency discrimination. Photoactivation of parvalbumin-positive interneurons (PVs) improved the ability of the mouse to detect a shift in tone frequency, whereas photosuppression of PVs impaired the performance. Furthermore, photosuppression of PVs during discriminative auditory fear conditioning increased generalization of conditioned response across tone frequencies, whereas PV photoactivation preserved normal specificity of learning. The observed changes in behavioral performance were correlated with bidirectional changes in the magnitude of tone-evoked responses, consistent with predictions of a model of a coupled excitatory-inhibitory cortical network. Direct photoactivation of excitatory neurons, which did not change tone-evoked response magnitude, did not affect behavioral performance in either task. Our results identify a new function for inhibition in the auditory cortex, demonstrating that it can improve or impair acuity of innate and learned auditory behaviors that rely on frequency discrimination.

Emergence of invariant representation of vocalizations in the auditory cortex.

An essential task of the auditory system is to discriminate between different communication signals, such as vocalizations. In everyday acoustic environments, the auditory system needs to be capable of performing the discrimination under different acoustic distortions of vocalizations. To achieve this, the auditory system is thought to build a representation of vocalizations that is invariant to their basic acoustic transformations. The mechanism by which neuronal populations create such an invariant representation within the auditory cortex is only beginning to be understood. We recorded the responses of populations of neurons in the primary and nonprimary auditory cortex of rats to original and acoustically distorted vocalizations. We found that populations of neurons in the nonprimary auditory cortex exhibited greater invariance in encoding vocalizations over acoustic transformations than neuronal populations in the primary auditory cortex. These findings are consistent with the hypothesis that invariant representations are created gradually through hierarchical transformation within the auditory pathway.

Determining the Gaussian curvature modulus of lipid membranes in simulations.

The Gaussian curvature modulus κ¯ of lipid bilayers likely contributes more than 100 kcal/mol to every cellular fission or fusion event. This huge impact on membrane remodeling energetics might be a factor that codetermines the complex lipid composition of biomembranes through tuning of κ¯. Yet, its value has been measured only for a handful of simple lipids, and no simulation has so far determined it better than a factor of two, rendering a systematic investigation of such enticing speculations impossible. Here we propose a highly accurate method to determine κ¯ in computer simulations. It relies on the interplay between curvature stress and edge tension of partially curved axisymmetric membrane disks and requires determining their closing probability. For a simplified lipid model we obtain κ¯ and its relation to the normal bending modulus κ for membranes differing both in stiffness and spontaneous lipid curvature. The elastic ratio κ¯/κ can be determined with a few percent statistical accuracy. Its value agrees with the scarce experimental data, and its change with spontaneous lipid curvature is compatible with theoretical expectations, thereby granting additional information on monolayer properties. We also show that an alternative determination of these elastic parameters based on moments of the lateral stress profile gives markedly different and unphysical values.

Efficient coding of natural scene statistics predicts discrimination thresholds for grayscale textures.

Previously, in Hermundstad et al., 2014, we showed that when sampling is limiting, the efficient coding principle leads to a 'variance is salience' hypothesis, and that this hypothesis accounts for visual sensitivity to binary image statistics. Here, using extensive new psychophysical data and image analysis, we show that this hypothesis accounts for visual sensitivity to a large set of grayscale image statistics at a striking level of detail, and also identify the limits of the prediction. We define a 66-dimensional space of local grayscale light-intensity correlations, and measure the relevance of each direction to natural scenes. The 'variance is salience' hypothesis predicts that two-point correlations are most salient, and predicts their relative salience. We tested these predictions in a texture-segregation task using un-natural, synthetic textures. As predicted, correlations beyond second order are not salient, and predicted thresholds for over 300 second-order correlations match psychophysical thresholds closely (median fractional error <0.13).

Complementary control of sensory adaptation by two types of cortical interneurons.

Reliably detecting unexpected sounds is important for environmental awareness and survival. By selectively reducing responses to frequently, but not rarely, occurring sounds, auditory cortical neurons are thought to enhance the brain's ability to detect unexpected events through stimulus-specific adaptation (SSA). The majority of neurons in the primary auditory cortex exhibit SSA, yet little is known about the underlying cortical circuits. We found that two types of cortical interneurons differentially amplify SSA in putative excitatory neurons. Parvalbumin-positive interneurons (PVs) amplify SSA by providing non-specific inhibition: optogenetic suppression of PVs led to an equal increase in responses to frequent and rare tones. In contrast, somatostatin-positive interneurons (SOMs) selectively reduce excitatory responses to frequent tones: suppression of SOMs led to an increase in responses to frequent, but not to rare tones. A mutually coupled excitatory-inhibitory network model accounts for distinct mechanisms by which cortical inhibitory neurons enhance the brain's sensitivity to unexpected sounds.

Variance predicts salience in central sensory processing.

Information processing in the sensory periphery is shaped by natural stimulus statistics. In the periphery, a transmission bottleneck constrains performance; thus efficient coding implies that natural signal components with a predictably wider range should be compressed. In a different regime--when sampling limitations constrain performance--efficient coding implies that more resources should be allocated to informative features that are more variable. We propose that this regime is relevant for sensory cortex when it extracts complex features from limited numbers of sensory samples. To test this prediction, we use central visual processing as a model: we show that visual sensitivity for local multi-point spatial correlations, described by dozens of independently-measured parameters, can be quantitatively predicted from the structure of natural images. This suggests that efficient coding applies centrally, where it extends to higher-order sensory features and operates in a regime in which sensitivity increases with feature variability.