Brain circuits process information through specialized neuronal subclasses interacting within a network. Revealing their interplay requires activating specific cells while monitoring others in a functioning circuit. Here we use a new platform for two-way light-based circuit interrogation in visual cortex in vivo to show the computational implications of modulating different subclasses of inhibitory neurons during sensory processing. We find that soma-targeting, parvalbumin-expressing (PV) neurons principally divide responses but preserve stimulus selectivity, whereas dendrite-targeting, somatostatin-expressing (SOM) neurons principally subtract from excitatory responses and sharpen selectivity. Visualized in vivo cell-attached recordings show that division by PV neurons alters response gain, whereas subtraction by SOM neurons shifts response levels. Finally, stimulating identified neurons while scanning many target cells reveals that single PV and SOM neurons functionally impact only specific subsets of neurons in their projection fields. These findings provide direct evidence that inhibitory neuronal subclasses have distinct and complementary roles in cortical computations.

The cortex represents information across widely varying timescales1–5. For instance, sensory cortex encodes stimuli that fluctuate over few tens of milliseconds6,7, whereas in association cortex behavioral choices can require the maintenance of information over seconds8,9. However, it remains poorly understood if diverse timescales result mostly from features intrinsic to individual neurons or from neuronal population activity. This question is unanswered because the timescales of coding in populations of neurons have not been studied extensively, and population codes have not been compared systematically across cortical regions. Here we discovered that population codes can be essential to achieve long coding timescales. Furthermore, we found that the properties of population codes differ between sensory and association cortices. We compared coding for sensory stimuli and behavioral choices in auditory cortex (AC) and posterior parietal cortex (PPC) as mice performed a sound localization task. Auditory stimulus information was stronger in AC than in PPC, and both regions contained choice information. Although AC and PPC coded information by tiling in time neurons that were transiently informative for ~200 milliseconds, the areas had major differences in functional coupling between neurons, measured as activity correlations that could not be explained by task events. Coupling among PPC neurons was strong and extended over long time lags, whereas coupling among AC neurons was weak and short-lived. Stronger coupling in PPC led to a population code with long timescales and a representation of choice that remained consistent for approximately one second. In contrast, AC had a code with rapid fluctuations in stimulus and choice information over hundreds of milliseconds. Our results reveal that population codes differ across cortex and that coupling is a variable property of cortical populations that affects the timescale of information coding and the accuracy of behavior.

Summary 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 bandpass 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.

Rett syndrome (RTT) arises from loss-of-function mutations in methyl-CpG binding protein 2 gene (Mecp2), but fundamental aspects of its physiological mechanisms are unresolved. Here, by whole-cell recording of synaptic responses in MeCP2 mutant mice in vivo, we show that visually driven excitatory and inhibitory conductances are both reduced in cortical pyramidal neurons. The excitation-to-inhibition (E/I) ratio is increased in amplitude and prolonged in time course. These changes predict circuit-wide reductions in response reliability and selectivity of pyramidal neurons to visual stimuli, as confirmed by two-photon imaging. and increases KCC2 expression to normalize the KCC2/NKCC1 ratio. Thus, loss of MeCP2 in the brain alters both excitation and inhibition in brain circuits via multiple mechanisms. Loss of MeCP2 from a specific interneuron subtype contributes crucially to the cell-specific and circuit-wide deficits of RTT. The joint restoration of inhibition and excitation in cortical circuits is pivotal for functionally correcting the disorder.MeCP2 | E/I balance | parvalbumin neurons | IGF1 | chloride transporters S ynaptic excitation (E) and inhibition (I), along with the neuronal balance of excitation and inhibition (E/I), is key to the function of brain circuits, and is often disrupted in neurodevelopmental disorders, including autism spectrum disorders (ASDs) (1-3). Rett syndrome (RTT) is a severe neurodevelopmental and adult disorder that arises from sporadic loss-of-function mutations in the X-linked (Xq28) methyl-CpG binding protein 2 gene (Mecp2) encoding the protein MeCP2 (4-7). MeCP2 is a critical regulator of brain development and adult neural function (8), and arrested brain maturation due to synaptic dysfunction is one of the hallmarks of RTT (3). However, the effects of MeCP2 on excitatory and inhibitory synaptic mechanisms in vivo, and on neuronal and circuit function underlying RTT pathophysiology, are unknown.MeCP2 is ubiquitously expressed in multiple cell types and subregions of the brain (4, 6, 9), including inhibitory interneurons, and has cell-autonomous as well as non-cell-autonomous effects (10); thus, it has been particularly challenging to identify its role in cell-specific brain circuits. Anatomically diverse inhibitory interneuron subtypes with distinct physiological signatures influence different aspects of neocortical function and behavior (11,12). Soma-targeting parvalbumin-expressing (PV + ) and dendritetargeting somatostatin-expressing (SOM + ) interneurons are the two major nonoverlapping populations of interneurons in mice that target cortical pyramidal neurons (13). Inhibition by PV + and SOM + neurons powerfully influences neuronal responses and circuit computations in visual cortex (14-17). Deletion of MeCP2 from all forebrain GABAergic interneurons recapitulates key aspects of RTT (18), demonstrating that altered inhibitory function is an important facet of RTT pathophysiology. Indeed, a major phenotype of MeCP2 reduction in individuals with RTT and in mouse models is ...

The spatiotemporal structure of activity in populations of neurons is critical for accurate perception and behavior. Experimental and theoretical studies have focused on "noise" correlations -trial-totrial covariations in neural activity for a given stimulus -as a key feature of population activity structure. Much work has shown that these correlations limit the stimulus information encoded by a population of neurons, leading to the widely-held prediction that correlations are detrimental for perceptual discrimination behaviors. However, this prediction relies on an untested assumption: that the neural mechanisms that read out sensory information to inform behavior depend only on a population's total stimulus information independently of how correlations constrain this information across neurons or time. Here we make the critical advance of simultaneously studying how correlations affect both the encoding and the readout of sensory information. We analyzed calcium imaging data from mouse posterior parietal cortex during two perceptual discrimination tasks. Correlations limited the ability to encode stimulus information, but (seemingly paradoxically) correlations were higher when mice made correct choices than when they made errors. On a singletrial basis, a mouse's behavioral choice depended not only on the stimulus information in the activity of the population as a whole, but unexpectedly also on the consistency of information across neurons and time. Because correlations increased information consistency, sensory information was more efficiently converted into a behavioral choice in the presence of correlations. Given this enhancedby-consistency readout, we estimated that correlations produced a behavioral benefit that compensated or overcame their detrimental information-limiting effects. These results call for a reevaluation of the role of correlated neural activity, and suggest that correlations in association cortex can benefit task performance even if they decrease sensory information.