Activation of specific interneurons improves V1 feature selectivity and visual perception

Single-Cell Stimulation in Barrel Cortex Influences Psychophysical Detection Performance

A single whisker stimulus elicits action potentials in a sparse subset of neurons in somatosensory cortex. The precise contribution of these neurons to the animal's perception of a whisker stimulus is unknown. Here we show that single-cell stimulation in rat barrel cortex of both sexes influences the psychophysical detection of a near-threshold whisker stimulus in a cell type-dependent manner, without affecting false alarm rate. Counterintuitively, stimulation of single fast-spiking putative inhibitory neurons increased detection performance. Single-cell stimulation of putative excitatory neurons failed to change detection performance, except for a small subset of deep-layer neurons that were highly sensitive to whisker stimulation and that had an unexpectedly strong impact on detection performance. These findings indicate that the perceptual impact of excitatory barrel cortical neurons relates to their firing response to whisker stimulation and that strong activity in a single highly sensitive neuron in barrel cortex can already enhance sensory detection. Our data suggest that sensory detection is based on a decoding mechanism that lends a disproportionally large weight to interneurons and to deep-layer neurons showing a strong response to sensory stimulation.SIGNIFICANCE STATEMENT Rat whisker somatosensory cortex contains a variety of neuronal cell types with distinct anatomical and physiological characteristics. How each of these different cell types contribute to the animal's perception of whisker stimuli is unknown. We explored this question by using a powerful electrophysiological stimulation technique that allowed us to target and stimulate single neurons with different sensory response types in whisker cortex. In awake, behaving animals, trained to detect whisker stimulation, only costimulation of single fast-spiking inhibitory neurons or single deep-layer excitatory neurons with strong responses to whisker stimulation enhanced detection performance. Our data demonstrate that single cortical neurons can have measurable impact on the detection of sensory stimuli and suggest a decoding mechanism based on select cell types.

Paradoxical response reversal of top-down modulation in cortical circuits with three interneuron types

Pyramidal cells and interneurons expressing parvalbumin (PV), somatostatin (SST), and vasoactive intestinal peptide (VIP) show cell-type-specific connectivity patterns leading to a canonical microcircuit across cortex. Experiments recording from this circuit often report counterintuitive and seemingly contradictory findings. For example, the response of SST cells in mouse V1 to top-down behavioral modulation can change its sign when the visual input changes, a phenomenon that we call response reversal. We developed a theoretical framework to explain these seemingly contradictory effects as emerging phenomena in circuits with two key features: interactions between multiple neural populations and a nonlinear neuronal input-output relationship. Furthermore, we built a cortical circuit model which reproduces counterintuitive dynamics observed in mouse V1. Our analytical calculations pinpoint connection properties critical to response reversal, and predict additional novel types of complex dynamics that could be tested in future experiments.

Progress and challenges for understanding the function of cortical microcircuits in auditory processing

An important outstanding question in auditory neuroscience is to identify the mechanisms by which specific motifs within inter-connected neural circuits affect auditory processing and, ultimately, behavior. In the auditory cortex, a combination of large-scale electrophysiological recordings and concurrent optogenetic manipulations are improving our understanding of the role of inhibitory–excitatory interactions. At the same time, computational approaches have grown to incorporate diverse neuronal types and connectivity patterns. However, we are still far from understanding how cortical microcircuits encode and transmit information about complex acoustic scenes. In this review, we focus on recent results identifying the special function of different cortical neurons in the auditory cortex and discuss a computational framework for future work that incorporates ideas from network science and network dynamics toward the coding of complex auditory scenes.

Advances in multi-neuron recordings and optogenetic manipulation have resulted in an interrogation of the function of specific cortical cell types in auditory cortex during sound processing. Here, the authors review this literature and discuss the merits of integrating computational approaches from dynamic network science.

Single-Unit Extracellular Recording from the Cerebellum During Eyeblink Conditioning in Head-Fixed Mice

This chapter presents a method for performing in vivo single-unit extracellular recordings and optogenetics during an associative, cerebellum-dependent learning task in head-fixed mice. The method uses a cylindrical treadmill system that reduces stress in the mice by allowing them to walk freely, yet it provides enough stability to maintain single-unit isolation of neurons for tens of minutes to hours. Using this system, we have investigated sensorimotor coding in the cerebellum while mice perform learned skilled movements.

Deprivation-Induced Homeostatic Spine Scaling In Vivo Is Localized to Dendritic Branches that Have Undergone Recent Spine Loss

Synaptic scaling is a key homeostatic plasticity mechanism and is thought to be involved in the regulation of cortical activity levels. Here we investigated the spatial scale of homeostatic changes in spine size following sensory deprivation in a subset of inhibitory (layer 2/3 GAD65-positive) and excitatory (layer 5 Thy1-positive) neurons in mouse visual cortex. Using repeated in vivo two-photon imaging, we find that increases in spine size are tumor necrosis factor alpha (TNF-α) dependent and thus are likely associated with synaptic scaling. Rather than occurring at all spines, the observed increases in spine size are spatially localized to a subset of dendritic branches and are correlated with the degree of recent local spine loss within that branch. Using simulations, we show that such a compartmentalized form of synaptic scaling has computational benefits over cell-wide scaling for information processing within the cell.

Synaptic integrative mechanisms for spatial cognition

Synaptic integrative mechanisms have profound effects on electrical signaling in the brain that, although largely hidden from recording methods that observe the spiking activity of neurons, may be critical for the encoding, storage and retrieval of information. Here we review roles for synaptic integrative mechanisms in the selection, generation and plasticity of place and grid fields, and in related temporal codes for the representation of space. We outline outstanding questions and challenges in the testing of hypothesized models for spatial computation and memory.

Parvalbumin-expressing interneurons can act solo while somatostatin-expressing interneurons act in chorus in most cases on cortical pyramidal cells

Neural circuits in the cerebral cortex consist primarily of excitatory pyramidal (Pyr) cells and inhibitory interneurons. Interneurons are divided into several subtypes, in which the two major groups are those expressing parvalbumin (PV) or somatostatin (SOM). These subtypes of interneurons are reported to play distinct roles in tuning and/or gain of visual response of pyramidal cells in the visual cortex. It remains unclear whether there is any quantitative and functional difference between the PV → Pyr and SOM → Pyr connections. We compared unitary inhibitory postsynaptic currents (uIPSCs) evoked by electrophysiological activation of single presynaptic interneurons with population IPSCs evoked by photo-activation of a mass of interneurons in vivo and in vitro in transgenic mice in which PV or SOM neurons expressed channelrhodopsin-2, and found that at least about 14 PV neurons made strong connections with a postsynaptic Pyr cell while a much larger number of SOM neurons made weak connections. Activation or suppression of single PV neurons modified visual responses of postsynaptic Pyr cells in 6 of 7 pairs whereas that of single SOM neurons showed no significant modification in 8 of 11 pairs, suggesting that PV neurons can act solo whereas most of SOM neurons may act in chorus on Pyr cells.

Cortical inhibitory interneurons control sensory processing

Inhibitory and excitatory neurons form intricate interconnected circuits in the mammalian sensory cortex. Whereas the function of excitatory neurons is largely to integrate and transmit information within and between brain areas, inhibitory neurons are thought to shape the way excitatory neurons integrate information, and they exhibit context-specific and behavior-specific responses. Over the last few years, work across sensory modalities has begun unraveling the function of distinct types of cortical inhibitory neurons in sensory processing, identifying their contribution to controlling stimulus selectivity of excitatory neurons and modulating information processing based on the behavioral state of the subject. Here, we review results from recent studies and discuss the implications for the contribution of inhibition to cortical circuit activity and information processing.

GABAergic Neurons in Ferret Visual Cortex Participate in Functionally Specific Networks

Functional circuits in the visual cortex require the coordinated activity of excitatory and inhibitory neurons. Molecular genetic approaches in the mouse have led to the "local non-specific pooling principle" of inhibitory connectivity, in which inhibitory neurons are untuned for stimulus features due to the random pooling of local inputs. However, it remains unclear whether this principle generalizes to species with a columnar organization of feature selectivity such as carnivores, primates, and humans. Here we use virally mediated GABAergic-specific GCaMP6f expression to demonstrate that inhibitory neurons in ferret visual cortex respond robustly and selectively to oriented stimuli. We find that the tuning of inhibitory neurons is inconsistent with the local non-specific pooling of excitatory inputs and that inhibitory neurons exhibit orientation-specific noise correlations with local and distant excitatory neurons. These findings challenge the generality of the non-specific pooling principle for inhibitory neurons, suggesting different rules for functional excitatory-inhibitory interactions in non-murine species.

Bisphenol A exposure perturbs visual function of adult cats by remodeling the neuronal activity in the primary visual pathway

Bisphenol A (BPA), a common environmental xenoestrogen, has been implicated in physiological and behavioral impairment, but the neuronal basis remains elusive. Although various synaptic mechanisms have been shown to mediate BPA-induced brain deficits, there are almost no reports addressing its underlying physiological mechanisms at the individual neuron level, particularly in the primary visual system. In the present study, using multiple-channel recording technique, we recorded the responses of single neurons in the primary visual system of cats to various direction stimuli both before and after BPA exposure. The results showed that the orientation selectivity of neurons in the primary visual cortex (area 17, A17) was obviously decreased after 2 h of intravenous BPA administration (0.2 mg/kg). Moreover, there were worse performances of information transmission of A17 neurons, presenting markedly decreased signal-to-noise ratio (SNR). To some extent, these functional decreases were attributable to the altered information inputs from lateral geniculate nucleus (LGN), which showed an increased spontaneous activity. Additionally, local injection of BPA (3.3 μg/ml) in A17 resulted in an obvious increase in orientation selectivity and a decrease in neuronal activity, involving enhanced activity of fast-spiking inhibitory interneurons. In conclusion, our results first demonstrate that acute BPA exposure can restrict the visual perception of cats, mainly depending on the alteration of the LGN projection, not the intercortical interaction. Importantly, BPA-induced-brain deficits might not only be confined to the cortical level but also occur as early as at the subcortical level.

High-Yield Methods for Accurate Two-Alternative Visual Psychophysics in Head-Fixed Mice

Research in neuroscience increasingly relies on the mouse, a mammalian species that affords unparalleled genetic tractability and brain atlases. Here, we introduce high-yield methods for probing mouse visual decisions. Mice are head-fixed, facilitating repeatable visual stimulation, eye tracking, and brain access. They turn a steering wheel to make two alternative choices, forced or unforced. Learning is rapid thanks to intuitive coupling of stimuli to wheel position. The mouse decisions deliver high-quality psychometric curves for detection and discrimination and conform to the predictions of a simple probabilistic observer model. The task is readily paired with two-photon imaging of cortical activity. Optogenetic inactivation reveals that the task requires mice to use their visual cortex. Mice are motivated to perform the task by fluid reward or optogenetic stimulation of dopamine neurons. This stimulation elicits a larger number of trials and faster learning. These methods provide a platform to accurately probe mouse vision and its neural basis.

Enhanced Control of Cortical Pyramidal Neurons With Micromagnetic Stimulation

Magnetic stimulation is less sensitive to the inflammatory reactions that plague conventional electrode-based cortical implants and therefore may be useful as a next-generation (implanted) cortical prosthetic. The fields arising from micro-coils are quite small however and thus, their ability to modulate cortical activity must first be established. Here, we show that layer V pyramidal neurons (PNs) can be strongly activated by micro-coil stimulation and further, the asymmetric fields arising from such coils do not simultaneously activate horizontally-oriented axon fibers, thus confining activation to a focal region around the coil. The spatially-narrow fields from micro-coils allowed the sensitivity of different regions within a single PN to be compared: while the proximal axon was most sensitive in naïve cells, repetitive stimulation over the apical dendrite led to a change in state of the neuron that reduced thresholds there to below those of the axon. Thus, our results raise the possibility that regardless of the mode of stimulation, penetration depths that target specific portions of the apical dendrite may actually be more effective than those that target Layer 6. Interestingly, the state change had similar properties to state changes described previously at the systems level, suggesting a possible neuronal mechanism underlying such responses.

Feedback Inhibition Shapes Emergent Computational Properties of Cortical Microcircuit Motifs

Cortical microcircuits are very complex networks, but they are composed of a relatively small number of stereotypical motifs. Hence, one strategy for throwing light on the computational function of cortical microcircuits is to analyze emergent computational properties of these stereotypical microcircuit motifs. We are addressing here the question how spike timing-dependent plasticity shapes the computational properties of one motif that has frequently been studied experimentally: interconnected populations of pyramidal cells and parvalbumin-positive inhibitory cells in layer 2/3. Experimental studies suggest that these inhibitory neurons exert some form of divisive inhibition on the pyramidal cells. We show that this data-based form of feedback inhibition, which is softer than that of winner-take-all models that are commonly considered in theoretical analyses, contributes to the emergence of an important computational function through spike timing-dependent plasticity: The capability to disentangle superimposed firing patterns in upstream networks, and to represent their information content through a sparse assembly code.SIGNIFICANCE STATEMENT We analyze emergent computational properties of a ubiquitous cortical microcircuit motif: populations of pyramidal cells that are densely interconnected with inhibitory neurons. Simulations of this model predict that sparse assembly codes emerge in this microcircuit motif under spike timing-dependent plasticity. Furthermore, we show that different assemblies will represent different hidden sources of upstream firing activity. Hence, we propose that spike timing-dependent plasticity enables this microcircuit motif to perform a fundamental computational operation on neural activity patterns.

GABAergic Interneurons in the Neocortex: From Cellular Properties to Circuits

Cortical networks are composed of glutamatergic excitatory projection neurons and local GABAergic inhibitory interneurons that gate signal flow and sculpt network dynamics. Although they represent a minority of the total neocortical neuronal population, GABAergic interneurons are highly heterogeneous, forming functional classes based on their morphological, electrophysiological, and molecular features, as well as connectivity and in vivo patterns of activity. Here we review our current understanding of neocortical interneuron diversity and the properties that distinguish cell types. We then discuss how the involvement of multiple cell types, each with a specific set of cellular properties, plays a crucial role in diversifying and increasing the computational power of a relatively small number of simple circuit motifs forming cortical networks. We illustrate how recent advances in the field have shed light onto the mechanisms by which GABAergic inhibition contributes to network operations.

Synaptic integration in cortical inhibitory neuron dendrites

Cortical inhibitory interneurons have a wide range of important functions, including balancing network excitation, enhancing spike-time precision of principal neurons, and synchronizing neural activity within and across brain regions. All these functions critically depend on the integration of synaptic inputs in their dendrites. But the sparse number of inhibitory cells, their small caliber dendrites, and the problem of cell-type identification, have prevented fast progress in analyzing their dendritic properties. Despite these challenges, recent advancements in electrophysiological, optical and molecular tools have opened the door for studying synaptic integration and dendritic computations in molecularly defined inhibitory interneurons. Accumulating evidence indicates that the biophysical properties of inhibitory neuron dendrites differ substantially from those of pyramidal neurons. In addition to the supralinear dendritic integration commonly observed in pyramidal neurons, interneuron dendrites can also integrate synaptic inputs in a linear or sublinear fashion. In this comprehensive review, we compare the dendritic biophysical properties of the three major classes of cortical inhibitory neurons and discuss how these cell type-specific properties may support their functions in the cortex.

Circuit Mechanisms of Sensorimotor Learning

The relationship between the brain and the environment is flexible, forming the foundation for our ability to learn. Here we review the current state of our understanding of the modifications in the sensorimotor pathway related to sensorimotor learning. We divide the process into three hierarchical levels with distinct goals: (1) sensory perceptual learning, (2) sensorimotor associative learning, and (3) motor skill learning. Perceptual learning optimizes the representations of important sensory stimuli. Associative learning and the initial phase of motor skill learning are ensured by feedback-based mechanisms that permit trial-and-error learning. The later phase of motor skill learning may primarily involve feedback-independent mechanisms operating under the classic Hebbian rule. With these changes under distinct constraints and mechanisms, sensorimotor learning establishes dedicated circuitry for the reproduction of stereotyped neural activity patterns and behavior.

Altered visual cortical processing in a mouse model of MECP2 duplication syndrome

As an epigenetic modulator of gene expression, Methyl-CpG binding protein 2 (MeCP2) is essential for normal neurological function. Dysfunction of MeCP2 is associated with a variety of neurological disorders. MECP2 gene duplication in human causes neuropsychiatric symptoms such as mental retardation and autism. MeCP2 overexpression in mice results in neurobehavioural disorders, dendritic abnormalities, and synaptic defects. However, how gain of MeCP2 function influences cortical processing of sensory information remains unclear. In this study, we examined visual processing in a mouse model of MECP2 duplication syndrome (MECP2 Tg1 mouse) at 8 and 14 weeks, which were before and after the onset of behavioural symptoms, respectively. In vivo extracellular recordings from primary visual cortex (V1) showed that neurons in Tg1 mice at both adult ages preferred higher spatial frequencies (SFs) than those in wild-type (WT) littermate controls, and the semi-saturation contrasts of neurons were lower in Tg1 mice at 8 weeks but not at 14 weeks. Behavioural experiments showed that the performance for visual detection at high SFs and low contrasts was higher in MECP2 Tg1 mice. Thus, MeCP2 gain-of-function in mice leads to higher visual acuity and contrast sensitivity, both at the levels of cortical response and behavioural performance.

Cortical Control of Spatial Resolution by VIP+ Interneurons

Neuronal tuning, defined by the degree of selectivity to a specific stimulus, is a hallmark of cortical computation. Understanding the role of GABAergic interneurons in shaping cortical tuning is now possible with the ability to manipulate interneuron classes selectively. Here, we show that interneurons expressing vasoactive intestinal polypeptide (VIP⁺) regulate the spatial frequency (SF) tuning of pyramidal neurons in mouse visual cortex. Using two-photon calcium imaging and optogenetic manipulations of VIP⁺ cell activity, we found that activating VIP⁺ cells elicited a stronger network response to stimuli of higher SFs, whereas suppressing VIP⁺ cells resulted in a network response shift toward lower SFs. These results establish that cortical inhibition modulates the spatial resolution of visual processing and add further evidence demonstrating that feature selectivity depends, not only on the feedforward excitatory projections into the cortex, but also on dynamic intracortical modulations by specific forms of inhibition.

SIGNIFICANCE STATEMENT

We demonstrate that interneurons expressing vasoactive intestinal polypeptide (VIP⁺) play a causal role in regulating the spatial frequency (SF) tuning of neurons in mouse visual cortex. We show that optogenetic activation of VIP⁺ cells results in a shift in network preference toward higher SFs, whereas suppressing them shifts the network toward lower SFs. Several studies have shown that VIP⁺ cells are sensitive to neuromodulation and increase their firing during locomotion, whisking, and pupil dilation and are involved in spatially specific top-down modulation, reminiscent of the effects of top-down attention, and also that attention enhances spatial resolution. Our findings provide a bridge between these studies by establishing the inhibitory circuitry that regulates these fundamental modulations of SF in the cortex.

An Optogenetic Approach for Investigation of Excitatory and Inhibitory Network GABA Actions in Mice Expressing Channelrhodopsin-2 in GABAergic Neurons

To investigate excitatory and inhibitory GABA actions in cortical neuronal networks, we present a novel optogenetic approach using a mouse knock-in line with conditional expression of channelrhodopsin-2 (ChR2) in GABAergic interneurons. During whole-cell recordings from hippocampal and neocortical slices from postnatal day (P) 2-P15 mice, photostimulation caused depolarization and excitation of interneurons and evoked barrages of postsynaptic GABAergic currents. Excitatory/inhibitory GABA actions on pyramidal cells were assessed by monitoring the alteration in the frequency of EPSCs during photostimulation of interneurons. We found that in slices from P2-P8 mice, photostimulation evoked an increase in EPSC frequency, whereas in P9-P15 mice the response switched to a reduction in EPSC frequency, indicating a developmental excitatory-to-inhibitory switch in GABA actions on glutamatergic neurons. Using a similar approach in urethane-anesthetized animals in vivo, we found that photostimulation of interneurons reduces EPSC frequency at ages P3-P9. Thus, expression of ChR2 in GABAergic interneurons of mice enables selective photostimulation of interneurons during the early postnatal period, and these mice display a developmental excitatory-to-inhibitory switch in GABA action in cortical slices in vitro, but so far show mainly inhibitory GABA actions on spontaneous EPSCs in the immature hippocampus and neocortex in vivo

SIGNIFICANCE STATEMENT

We report a novel optogenetic approach for investigating excitatory and inhibitory GABA actions in mice with conditional expression of channelrhodopsin-2 in GABAergic interneurons. This approach shows a developmental excitatory-to-inhibitory switch in the actions of GABA on glutamatergic neurons in neocortical and hippocampal slices from neonatal mouse pups in vitro, but also reveals inhibitory GABA actions in the neonatal mouse neocortex and hippocampus in vivo.

Sensory noise predicts divisive reshaping of receptive fields

In order to respond reliably to specific features of their environment, sensory neurons need to integrate multiple incoming noisy signals. Crucially, they also need to compete for the interpretation of those signals with other neurons representing similar features. The form that this competition should take depends critically on the noise corrupting these signals. In this study we show that for the type of noise commonly observed in sensory systems, whose variance scales with the mean signal, sensory neurons should selectively divide their input signals by their predictions, suppressing ambiguous cues while amplifying others. Any change in the stimulus context alters which inputs are suppressed, leading to a deep dynamic reshaping of neural receptive fields going far beyond simple surround suppression. Paradoxically, these highly variable receptive fields go alongside and are in fact required for an invariant representation of external sensory features. In addition to offering a normative account of context-dependent changes in sensory responses, perceptual inference in the presence of signal-dependent noise accounts for ubiquitous features of sensory neurons such as divisive normalization, gain control and contrast dependent temporal dynamics.

An inhibitory gate for state transition in cortex

Large scale transitions between active (up) and silent (down) states during quiet wakefulness or NREM sleep regulate fundamental cortical functions and are known to involve both excitatory and inhibitory cells. However, if and how inhibition regulates these activity transitions is unclear. Using fluorescence-targeted electrophysiological recording and cell-specific optogenetic manipulation in both anesthetized and non-anesthetized mice, we found that two major classes of interneurons, the parvalbumin and the somatostatin positive cells, tightly control both up-to-down and down-to-up state transitions. Inhibitory regulation of state transition was observed under both natural and optogenetically-evoked conditions. Moreover, perturbative optogenetic experiments revealed that the inhibitory control of state transition was interneuron-type specific. Finally, local manipulation of small ensembles of interneurons affected cortical populations millimetres away from the modulated region. Together, these results demonstrate that inhibition potently gates transitions between cortical activity states, and reveal the cellular mechanisms by which local inhibitory microcircuits regulate state transitions at the mesoscale.

DOI:http://dx.doi.org/10.7554/eLife.26177.001

Vision as a Beachhead

When neural circuits develop abnormally due to different genetic deficits and/or environmental insults, neural computations and the behaviors that rely on them are altered. Computational theories that relate neural circuits with specific quantifiable behavioral and physiological phenomena, therefore, serve as extremely useful tools for elucidating the neuropathological mechanisms that underlie different disorders. The visual system is particularly well suited for characterizing differences in neural computations; computational theories of vision are well established, and empirical protocols for measuring the parameters of those theories are well developed. In this article, we examine how psychophysical and neuroimaging measurements from human subjects are being used to test hypotheses about abnormal neural computations in autism, with an emphasis on hypotheses regarding potential excitation/inhibition imbalances. We discuss the complexity of relating specific computational abnormalities to particular underlying mechanisms given the diversity of neural circuits that can generate the same computation, and we discuss areas of research in which computational theories need to be further developed to provide useful frameworks for interpreting existing results. A final emphasis is placed on the need to extend existing ideas into developmental frameworks that take into account the dramatic developmental changes in neurophysiology (e.g., changes in excitation/inhibition balance) that take place during the first years of life, when autism initially emerges.

Feature Detection in Visual Cortex during Different Functional States

Cortical activity exhibits distinct characteristics in different functional states. In awake behaving animals it shows less synchrony, while in rest or sleeping state cortical activity is most synchronous. Previous studies showed that switching between functional states can change the efficiency of flowing sensory information. Switching between functional states can be triggered by releasing neuromodulators which affect neurotransmitter release probability and depolarization of cortical neurons. In this work we focus on studying primary visual area V1, by using firing rate ring model with short-term synaptic depression (STD). We show that reconstruction of visual features from V1 activity depends on the functional state, with best precision achieved at the state with intermediate release probability. We suggest that this regime corresponds to the state of maximal visual attention.

Circuits and Mechanisms for Surround Modulation in Visual Cortex

Surround modulation (SM) is a fundamental property of sensory neurons in many species and sensory modalities. SM is the ability of stimuli in the surround of a neuron's receptive field (RF) to modulate (typically suppress) the neuron's response to stimuli simultaneously presented inside the RF, a property thought to underlie optimal coding of sensory information and important perceptual functions. Understanding the circuit and mechanisms for SM can reveal fundamental principles of computations in sensory cortices, from mouse to human. Current debate is centered over whether feedforward or intracortical circuits generate SM, and whether this results from increased inhibition or reduced excitation. Here we present a working hypothesis, based on theoretical and experimental evidence, that SM results from feedforward, horizontal, and feedback interactions with local recurrent connections, via synaptic mechanisms involving both increased inhibition and reduced recurrent excitation. In particular, strong and balanced recurrent excitatory and inhibitory circuits play a crucial role in the computation of SM.

Ube3a loss increases excitability and blunts orientation tuning in the visual cortex of Angelman syndrome model mice

Angelman syndrome (AS) is a neurodevelopmental disorder caused by loss of the maternally inherited allele of UBE3AUbe3a^STOP/p+ mice recapitulate major features of AS in humans and allow conditional reinstatement of maternal Ube3a with the expression of Cre recombinase. We have recently shown that AS model mice exhibit reduced inhibitory drive onto layer (L)2/3 pyramidal neurons of visual cortex, which contributes to a synaptic excitatory/inhibitory imbalance. However, it remains unclear how this loss of inhibitory drive affects neural circuits in vivo. Here we examined visual cortical response properties in individual neurons to explore the consequences of Ube3a loss on intact cortical circuits and processing. Using in vivo patch-clamp electrophysiology, we measured the visually evoked responses to square-wave drifting gratings in L2/3 regular-spiking (RS) neurons in control mice, Ube3a-deficient mice, and mice in which Ube3a was conditionally reinstated in GABAergic neurons. We found that Ube3a-deficient mice exhibited enhanced pyramidal neuron excitability in vivo as well as weaker orientation tuning. These observations are the first to show alterations in cortical computation in an AS model, and they suggest a basis for cortical dysfunction in AS.NEW & NOTEWORTHY Angelman syndrome (AS) is a severe neurodevelopmental disorder caused by the loss of the gene UBE3A Using electrophysiological recording in vivo, we describe visual cortical dysfunctions in a mouse model of AS. Aberrant cellular properties in AS model mice could be improved by reinstating Ube3a in inhibitory neurons. These findings suggest that inhibitory neurons play a substantial role in the pathogenesis of AS.

Monitoring the Right Collection: The Central Cholinergic Neurons as an Instructive Example

Some neurons are more equal than others: neuroscience relies heavily on the notion that there is a division of labor among different subtypes of brain cells. Therefore, it is important to recognize groups of neurons that participate in the same computation or share similar tasks. However, what the best ways are to identify such collections is not yet clear. Here, we argue that monitoring the activity of genetically defined cell types will lead to new insights about neural mechanisms and improve our understanding of disease vulnerability. Through highlighting how central cholinergic neurons encode reward and punishment that can be captured by a unified framework of reinforcement surprise, we hope to provide an instructive example of how studying a genetically defined cell type may further our understanding of neural function.

A Computational Analysis of the Function of Three Inhibitory Cell Types in Contextual Visual Processing

Most cortical inhibitory cell types exclusively express one of three genes, parvalbumin, somatostatin and 5HT3a. We conjecture that these three inhibitory neuron types possess distinct roles in visual contextual processing based on two observations. First, they have distinctive synaptic sources and targets over different spatial extents and from different areas. Second, the visual responses of cortical neurons are affected not only by local cues, but also by visual context. We use modeling to relate structural information to function in primary visual cortex (V1) of the mouse, and investigate their role in contextual visual processing. Our findings are three-fold. First, the inhibition mediated by parvalbumin positive (PV) cells mediates local processing and could underlie their role in boundary detection. Second, the inhibition mediated by somatostatin-positive (SST) cells facilitates longer range spatial competition among receptive fields. Third, non-specific top-down modulation to interneurons expressing vasoactive intestinal polypeptide (VIP), a subclass of 5HT3a neurons, can selectively enhance V1 responses.

Delay activity of specific prefrontal interneuron subtypes modulates memory-guided behavior

Memory-guided behavior requires maintenance of task-relevant information without sensory input, but the underlying circuit mechanism remains unclear. Calcium imaging in mice performing a delayed Go/No-Go task revealed robust delay activity in dorsomedial prefrontal cortex (dmPFC), with different pyramidal neurons signaling Go and No-Go action plans. Inhibiting pyramidal neurons by optogenetically activating somatostatin (SST)- or parvalbumin (PV)-positive interneurons, even transiently during an early delay period, impaired task performance primarily by increasing inappropriate Go responses. In contrast, activating vasoactive intestinal peptide (VIP)-positive interneurons enhanced the behavioral performance and neuronal representation of action plans. Furthermore, while the natural activity of SST and PV neurons was strongly biased toward Go trials, VIP neurons were similarly active in Go and No-Go trials. SST/VIP neuron activation also impaired/enhanced performance of a delayed two-alternative forced choice task. Thus, dmPFC is a crucial component of the short-term memory network, and activation of its VIP neurons improves memory retention.

Neural Circuitry of Reward Prediction Error

Dopamine neurons facilitate learning by calculating reward prediction error, or the difference between expected and actual reward. Despite two decades of research, it remains unclear how dopamine neurons make this calculation. Here we review studies that tackle this problem from a diverse set of approaches, from anatomy to electrophysiology to computational modeling and behavior. Several patterns emerge from this synthesis: that dopamine neurons themselves calculate reward prediction error, rather than inherit it passively from upstream regions; that they combine multiple separate and redundant inputs, which are themselves interconnected in a dense recurrent network; and that despite the complexity of inputs, the output from dopamine neurons is remarkably homogeneous and robust. The more we study this simple arithmetic computation, the knottier it appears to be, suggesting a daunting (but stimulating) path ahead for neuroscience more generally.

Interplay between periodic stimulation and GABAergic inhibition in striatal network oscillations

Network oscillations are ubiquitous across many brain regions. In the basal ganglia, oscillations are also present at many levels and a wide range of characteristic frequencies have been reported to occur during both health and disease. The striatum, the main input nucleus of the basal ganglia, receives massive glutamatergic inputs from the cortex and is highly susceptible to external oscillations. However, there is limited knowledge about the exact nature of this routing process and therefore, it is of key importance to understand how time-dependent, external stimuli propagate through the striatal circuitry. Using a network model of the striatum and corticostriatal projections, we try to elucidate the importance of specific GABAergic neurons and their interactions in shaping striatal oscillatory activity. Here, we propose that fast-spiking interneurons can perform an important role in transferring cortical oscillations to the striatum especially to those medium spiny neurons that are not directly driven by the cortical oscillations. We show how the activity levels of different populations, the strengths of different inhibitory synapses, degree of outgoing projections of striatal cells, ongoing activity and synchronicity of inputs can influence network activity. These results suggest that the propagation of oscillatory inputs into the medium spiny neuron population is most efficient, if conveyed via the fast-spiking interneurons. Therefore, pharmaceuticals that target fast-spiking interneurons may provide a novel treatment for regaining the spectral characteristics of striatal activity that correspond to the healthy state.

Interactions between synaptic homeostatic mechanisms: an attempt to reconcile BCM theory, synaptic scaling, and changing excitation/inhibition balance

Homeostatic plasticity is proposed to be mediated by synaptic changes, such as synaptic scaling and shifts in the excitation/inhibition balance. These mechanisms are thought to be separate from the Bienenstock, Cooper, Munro (BCM) learning rule, where the threshold for the induction of long-term potentiation and long-term depression slides in response to changes in activity levels. Yet, both sets of mechanisms produce a homeostatic response of a relative increase (or decrease) in strength of excitatory synapses in response to overall activity-level changes. Here we review recent studies, with a focus on in vivo experiments, to re-examine the overlap and differences between these two mechanisms and we suggest how they may interact to facilitate firing-rate homeostasis, while maintaining functional properties of neurons.

FlpStop, a tool for conditional gene control in Drosophila

Manipulating gene function cell type-specifically is a common experimental goal in Drosophila research and has been central to studies of neural development, circuit computation, and behavior. However, current cell type-specific gene disruption techniques in flies often reduce gene activity incompletely or rely on cell division. Here we describe FlpStop, a generalizable tool for conditional gene disruption and rescue in post-mitotic cells. In proof-of-principle experiments, we manipulated apterous, a regulator of wing development. Next, we produced conditional null alleles of Glutamic acid decarboxylase 1 (Gad1) and Resistant to dieldrin (Rdl), genes vital for GABAergic neurotransmission, as well as cacophony (cac) and paralytic (para), voltage-gated ion channels central to neuronal excitability. To demonstrate the utility of this approach, we manipulated cac in a specific visual interneuron type and discovered differential regulation of calcium signals across subcellular compartments. Thus, FlpStop will facilitate investigations into the interactions between genes, circuits, and computation.

A Principle for Describing and Verifying Brain Mechanisms Using Ongoing Activity

Not even the most informed scientist can setup a theory that takes all brain signals into account. A neuron not only receives neuronal short range and long range input from all over the brain but a neuron also receives input from the extracellular space, astrocytes and vasculature. Given this complexity, how does one describe and verify a typical brain mechanism in vivo? Common to most described mechanisms is that one focuses on how one specific input signal gives rise to the activity in a population of neurons. This can be an input from a brain area, a population of neurons or a specific cell type. All remaining inputs originating from all over the brain are lumped together into one background input. The division into two inputs is attractive since it can be used to quantify the relative importance of either input. Here we have chosen to extract the specific and the background input by means of recording and inhibiting the specific input. We summarize what it takes to estimate the two inputs on a single trial level. The inhibition should not only be strong but also fast and the specific input measurement has to be tailor-made to the inhibition. In essence, we suggest ways to control electrophysiological experiments in vivo. By applying those controls it may become possible to describe and verify many brain mechanisms, and it may also allow the study of the integration of spontaneous and ongoing activity, which in turn governs cognition and behavior.

GABA and GABA receptors alterations in the primary visual cortex of concave lens-induced myopic model

Until recently most researches on myopia mechanisms have mainly been focused on the eye ball and few investigations were explored on the upper visual pathway, such as the visual cortex. The roles of gamma-aminobutyric acid (GABA) in the retinal and in the upper visual pathway are inter-correlated. As the retinal glutamate decarboxylase (GAD), GABA, and the mRNA levels of GABA receptors increased during the concave lens induced myopia formation, however, whether GABA alterations also occurred in the visual cortex during the concave lens induction is still unknown. In the present study, using HPLC, Enzyme-Linked Immunosorbent Assay (ELISA) and Real-Time Quantitative-PCR (RT-PCR) methods, we observed the changing trends of GABA, glutamate decarboxylase (GAD), and GABA receptors in the visual cortex of concave lens-induced myopic guinea pigs. Similar to the changing patterns of retinal GABA, the concentrations of GAD, GABA and the mRNA levels of GABA receptors in the visual cortex also increased. These results indicate that the exploration on myopia mechanisms should possibly be investigated on the whole visual pathway and the detailed significance of cortical GABA alterations needs further investigation.

Activation of Parvalbumin-Positive Neurons in Both Retina and Primary Visual Cortex Improves the Feature-Selectivity of Primary Visual Cortex Neurons

Several recent studies using either viral or transgenic mouse models have shown different results on whether the activation of parvalbumin-positive (PV⁺) neurons expressing channelrhodopsin-2 (ChR2) in the primary visual cortex (V1) improves the orientation- and direction-selectivity of V1 neurons. Although this discrepancy was thoroughly discussed in a follow-up communication, the issue of using different models to express ChR2 in V1 was not mentioned. We found that ChR2 was expressed in retinal ganglion cells (RGCs) and V1 neurons in ChR2^fl/+; PV-Cre mice. Our results showed that the activation of PV⁺ RGCs using white drifting gratings alone significantly decreased the firing rates of V1 neurons and improved their direction- and orientation-selectivity. Long-duration activation of PV⁺ interneurons in V1 further enhanced the feature-selectivity of V1 neurons in anesthetized mice, confirming the conclusions from previous findings. These results suggest that the activation of both PV⁺ RGCs and V1 neurons improves feature-selectivity in mice.

Fifty shades of inhibition

Inhibitory circuits are essential for brain function. Our understanding of their synaptic organization has advanced extensively with the identification and classification of an impressive variety of neuron groups, receptor types, and patterns of connectivity. However, the conceptual discussion regarding the role of in neural circuits still revolves around the idea that its primary role is to regulate circuit excitability. Here, I will focus on recent findings from cortical circuits and argue that inhibitory circuits are central to the integration of incoming inputs and can promote sophisticated fine-scale control of local circuits. I propose that inhibitory circuits should not be viewed so much as brakes on principal neurons activity, but as primary contributors to a variety of neural network functions.

Differential Inputs to the Perisomatic and Distal-Dendritic Compartments of VIP-Positive Neurons in Layer 2/3 of the Mouse Barrel Cortex

The recurrent network composed of excitatory and inhibitory neurons is fundamental to neocortical function. Inhibitory neurons in the mammalian neocortex are molecularly diverse, and individual cell types play unique functional roles in the neocortical microcircuit. Recently, vasoactive intestinal polypeptide-positive (VIP+) neurons, comprising a subclass of inhibitory neurons, have attracted particular attention because they can disinhibit pyramidal cells through inhibition of other types of inhibitory neurons, such as parvalbumin- (PV+) and somatostatin-positive (SOM+) inhibitory neurons, promoting sensory information processing. Although VIP+ neurons have been reported to receive synaptic inputs from PV+ and SOM+ inhibitory neurons as well as from cortical and thalamic excitatory neurons, the somatodendritic localization of these synaptic inputs has yet to be elucidated at subcellular spatial resolution. In the present study, we visualized the somatodendritic membranes of layer (L) 2/3 VIP+ neurons by injecting a newly developed adeno-associated virus (AAV) vector into the barrel cortex of VIP-Cre knock-in mice, and we determined the extensive ramification of VIP+ neuron dendrites in the vertical orientation. After immunohistochemical labeling of presynaptic boutons and postsynaptic structures, confocal laser scanning microscopy revealed that the synaptic contacts were unevenly distributed throughout the perisomatic (<100 μm from the somata) and distal-dendritic compartments (≥100 μm) of VIP+ neurons. Both corticocortical and thalamocortical excitatory neurons preferentially targeted the distal-dendritic compartment of VIP+ neurons. On the other hand, SOM+ and PV+ inhibitory neurons preferentially targeted the distal-dendritic and perisomatic compartments of VIP+ neurons, respectively. Notably, VIP+ neurons had few reciprocal connections. These observations suggest different inhibitory effects of SOM+ and PV+ neuronal inputs on VIP+ neuron activity; inhibitory inputs from SOM+ neurons likely modulate excitatory inputs locally in dendrites, while PV+ neurons could efficiently interfere with action potential generation through innervation of the perisomatic domain of VIP+ neurons. The present study, which shows a precise configuration of site-specific inputs, provides a structural basis for the integration mechanism of synaptic inputs to VIP+ neurons.

Inhibitory control of correlated intrinsic variability in cortical networks

Cortical networks exhibit intrinsic dynamics that drive coordinated, large-scale fluctuations across neuronal populations and create noise correlations that impact sensory coding. To investigate the network-level mechanisms that underlie these dynamics, we developed novel computational techniques to fit a deterministic spiking network model directly to multi-neuron recordings from different rodent species, sensory modalities, and behavioral states. The model generated correlated variability without external noise and accurately reproduced the diverse activity patterns in our recordings. Analysis of the model parameters suggested that differences in noise correlations across recordings were due primarily to differences in the strength of feedback inhibition. Further analysis of our recordings confirmed that putative inhibitory neurons were indeed more active during desynchronized cortical states with weak noise correlations. Our results demonstrate that network models with intrinsically-generated variability can accurately reproduce the activity patterns observed in multi-neuron recordings and suggest that inhibition modulates the interactions between intrinsic dynamics and sensory inputs to control the strength of noise correlations.

DOI:http://dx.doi.org/10.7554/eLife.19695.001

Our brains contain billions of neurons, which are continually producing electrical signals to relay information around the brain. Yet most of our knowledge of how the brain works comes from studying the activity of one neuron at a time. Recently, studies of multiple neurons have shown that they tend to be active together in short bursts called “up” states, which are followed by periods in which they are less active called “down” states. When we are sleeping or under a general anesthetic, the neurons may be completely silent during down states, but when we are awake the difference in activity between the two states is usually less extreme. However, it is still not clear how the neurons generate these patterns of activity.

To address this question, Stringer et al. studied the activity of neurons in the brains of awake and anesthetized rats, mice and gerbils. The experiments recorded electrical activity from many neurons at the same time and found a wide range of different activity patterns. A computational model based on these data suggests that differences in the degree to which some neurons suppress the activity of other neurons may account for this variety. Increasing the strength of these inhibitory signals in the model decreased the fluctuations in electrical activity across entire areas of the brain. Further analysis of the experimental data supported the model’s predictions by showing that inhibitory neurons – which act to reduce electrical activity in other neurons – were more active when there were fewer fluctuations in activity across the brain.

The next step following on from this work would be to develop ways to build computer models that can mimic the activity of many more neurons at the same time. The models could then be used to interpret the electrical activity produced by many different kinds of neuron. This will enable researchers to test more sophisticated hypotheses about how the brain works.

DOI:http://dx.doi.org/10.7554/eLife.19695.002

Lack of Cdkl5 Disrupts the Organization of Excitatory and Inhibitory Synapses and Parvalbumin Interneurons in the Primary Visual Cortex

Cyclin-dependent kinase-like 5 (CDKL5) mutations are found in severe neurodevelopmental disorders, including the Hanefeld variant of Rett syndrome (RTT; CDKL5 disorder). CDKL5 loss-of-function murine models recapitulate pathological signs of the human disease, such as visual attention deficits and reduced visual acuity. Here we investigated the cellular and synaptic substrates of visual defects by studying the organization of the primary visual cortex (V1) of Cdkl5^−/y mice. We found a severe reduction of c-Fos expression in V1 of Cdkl5^−/y mutants, suggesting circuit hypoactivity. Glutamatergic presynaptic structures were increased, but postsynaptic PSD-95 and Homer were significantly downregulated in CDKL5 mutants. Interneurons expressing parvalbumin, but not other types of interneuron, had a higher density in mutant V1, and were hyperconnected with pyramidal neurons. Finally, the developmental trajectory of pavalbumin-containing cells was also affected in Cdkl5^−/y mice, as revealed by fainter appearance perineuronal nets at the closure of the critical period (CP). The present data reveal an overall disruption of V1 cellular and synaptic organization that may cause a shift in the excitation/inhibition balance likely to underlie the visual deficits characteristic of CDKL5 disorder. Moreover, ablation of CDKL5 is likely to tamper with the mechanisms underlying experience-dependent refinement of cortical circuits during the CP of development.

Neural Elements for Predictive Coding

Predictive coding theories of sensory brain function interpret the hierarchical construction of the cerebral cortex as a Bayesian, generative model capable of predicting the sensory data consistent with any given percept. Predictions are fed backward in the hierarchy and reciprocated by prediction error in the forward direction, acting to modify the representation of the outside world at increasing levels of abstraction, and so to optimize the nature of perception over a series of iterations. This accounts for many ‘illusory’ instances of perception where what is seen (heard, etc.) is unduly influenced by what is expected, based on past experience. This simple conception, the hierarchical exchange of prediction and prediction error, confronts a rich cortical microcircuitry that is yet to be fully documented. This article presents the view that, in the current state of theory and practice, it is profitable to begin a two-way exchange: that predictive coding theory can support an understanding of cortical microcircuit function, and prompt particular aspects of future investigation, whilst existing knowledge of microcircuitry can, in return, influence theoretical development. As an example, a neural inference arising from the earliest formulations of predictive coding is that the source populations of forward and backward pathways should be completely separate, given their functional distinction; this aspect of circuitry – that neurons with extrinsically bifurcating axons do not project in both directions – has only recently been confirmed. Here, the computational architecture prescribed by a generalized (free-energy) formulation of predictive coding is combined with the classic ‘canonical microcircuit’ and the laminar architecture of hierarchical extrinsic connectivity to produce a template schematic, that is further examined in the light of (a) updates in the microcircuitry of primate visual cortex, and (b) rapid technical advances made possible by transgenic neural engineering in the mouse. The exercise highlights a number of recurring themes, amongst them the consideration of interneuron diversity as a spur to theoretical development and the potential for specifying a pyramidal neuron’s function by its individual ‘connectome,’ combining its extrinsic projection (forward, backward or subcortical) with evaluation of its intrinsic network (e.g., unidirectional versus bidirectional connections with other pyramidal neurons).

Jointly reduced inhibition and excitation underlies circuit-wide changes in cortical processing in Rett syndrome

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. Targeted recordings reveal that parvalbumin-expressing (PV⁺) interneurons in mutant mice have reduced responses. PV-specific MeCP2 deletion alone recapitulates effects of global MeCP2 deletion on cortical circuits, including reduced pyramidal neuron responses and reduced response reliability and selectivity. Furthermore, MeCP2 mutant mice show reduced expression of the cation-chloride cotransporter KCC2 (K⁺/Cl^- exporter) and a reduced KCC2/NKCC1 (Na⁺/K⁺/Cl^- importer) ratio. Perforated patch recordings demonstrate that the reversal potential for GABA is more depolarized in mutant mice, but is restored by application of the NKCC1 inhibitor bumetanide. Treatment with recombinant human insulin-like growth factor-1 restores responses of PV⁺ and pyramidal neurons 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.

Cortical Neural Computation by Discrete Results Hypothesis

One of the most challenging problems we face in neuroscience is to understand how the cortex performs computations. There is increasing evidence that the power of the cortical processing is produced by populations of neurons forming dynamic neuronal ensembles. Theoretical proposals and multineuronal experimental studies have revealed that ensembles of neurons can form emergent functional units. However, how these ensembles are implicated in cortical computations is still a mystery. Although cell ensembles have been associated with brain rhythms, the functional interaction remains largely unclear. It is still unknown how spatially distributed neuronal activity can be temporally integrated to contribute to cortical computations. A theoretical explanation integrating spatial and temporal aspects of cortical processing is still lacking. In this Hypothesis and Theory article, we propose a new functional theoretical framework to explain the computational roles of these ensembles in cortical processing. We suggest that complex neural computations underlying cortical processing could be temporally discrete and that sensory information would need to be quantized to be computed by the cerebral cortex. Accordingly, we propose that cortical processing is produced by the computation of discrete spatio-temporal functional units that we have called "Discrete Results" (Discrete Results Hypothesis). This hypothesis represents a novel functional mechanism by which information processing is computed in the cortex. Furthermore, we propose that precise dynamic sequences of "Discrete Results" is the mechanism used by the cortex to extract, code, memorize and transmit neural information. The novel "Discrete Results" concept has the ability to match the spatial and temporal aspects of cortical processing. We discuss the possible neural underpinnings of these functional computational units and describe the empirical evidence supporting our hypothesis. We propose that fast-spiking (FS) interneuron may be a key element in our hypothesis providing the basis for this computation.

Distinct Roles of Parvalbumin- and Somatostatin-Expressing Interneurons in Working Memory

Inhibitory interneurons are thought to play crucial roles in diverse brain functions. However, roles of different inhibitory interneuron subtypes in working memory remain unclear. We found distinct activity patterns and stimulation effects of two major interneuron subtypes, parvalbumin (PV)- and somatostatin (SOM)-expressing interneurons, in the medial prefrontal cortex of mice performing a spatial working memory task. PV interneurons showed weak target-dependent delay-period activity and were strongly inhibited by reward. By contrast, SOM interneurons showed strong target-dependent delay-period activity, and only a subtype of them was inhibited by reward. Furthermore, optogenetic stimulation of PV and SOM interneurons preferentially suppressed discharges of putative pyramidal cells and interneurons, respectively. These results indicate different contributions of PV and SOM interneurons to prefrontal cortical circuit dynamics underlying working memory.

Snapshots of the Brain in Action: Local Circuit Operations through the Lens of γ Oscillations

γ oscillations (20-80 Hz) are associated with sensory processing, cognition, and memory, and focused attention in animals and humans. γ activity can arise from several neural mechanisms in the cortex and hippocampus and can vary across circuits, behavioral states, and developmental stages. γ oscillations are nonstationary, typically occurring in short bouts, and the peak frequency of this rhythm is modulated by stimulus parameters. In addition, the participation of excitatory and inhibitory neurons in the γ rhythm varies across local circuits and conditions, particularly in the cortex. Although these dynamics present a challenge to interpreting the functional role of γ oscillations, these patterns of activity emerge from synaptic interactions among excitatory and inhibitory neurons and thus provide important insight into local circuit operations.

Asymmetric effects of activating and inactivating cortical interneurons

Bidirectional manipulations – activation and inactivation – are widely used to identify the functions supported by specific cortical interneuron types. Implicit in much of this work is the notion that tonic activation and inactivation will both produce valid, internally consistent insights into interneurons’ computational roles. Here, using single-unit recordings in auditory cortex of awake mice, we show that this may not generally hold true. Optogenetically manipulating somatostatin-positive (Sst+) or parvalbumin-positive (Pvalb+) interneurons while recording tone-responses showed that Sst+ inactivation increased response gain, while Pvalb+ inactivation weakened tuning and decreased information transfer, implying that these neurons support delineable computational functions. But activating Sst+ and Pvalb+ interneurons revealed no such differences. We used a simple network model to understand this asymmetry, and showed how relatively small changes in key parameters, such as spontaneous activity or strength of the light manipulation, determined whether activation and inactivation would produce consistent or paradoxical conclusions regarding interneurons’ computational functions.

DOI:http://dx.doi.org/10.7554/eLife.18383.001

The brain processes information through the activity of many different types of neurons. A common assumption is that we can work out the role of each type of neuron by increasing or decreasing its activity experimentally and observing the consequences. Imagine, for example, that increasing the activity of a group of neurons boosts the formation of memories, whereas decreasing their activity has the opposite effect. Can we conclude that the natural role of those neurons is to support memory formation?

This question has become increasingly pertinent in recent years, as advances in molecular genetic techniques such as optogenetics have made it easier to experimentally manipulate the activity of neurons. However, the ease with which these experiments can be done masks how difficult it can be to interpret the results.

To test the assumption that dialing a neuron’s activity up or down provides a straightforward readout of its role in the brain, Phillips and Hasenstaub used optogenetics to inactivate two types of neurons in an area of the mouse brain responsible for processing sound. Inactivating each cell type produced different outcomes. The standard way of interpreting these results is to conclude that the neurons must therefore have different, separable roles. However, using optogenetics to increase the activity of the neurons revealed no such differences in the neurons’ roles.

Using a computer model, Phillips and Hasenstaub showed that relatively small changes in factors such as the spontaneous activity of the neurons can affect the outcome of the experiments. Under certain circumstances, the two types of neurons respond in the same way to activation and inactivation; under other circumstances, they respond differently.

The results presented by Phillips and Hasenstaub remind us that the conclusions we draw about a neuron’s role can be sensitive to how the cell was manipulated. Future studies should address how other aspects of experimental manipulations, such as their timing or pattern, affect the apparent behavior of neurons.

DOI:http://dx.doi.org/10.7554/eLife.18383.002