Active cortical dendrites modulate perception

Synergistic Positive Feedback Mechanisms Underlying Seizure Initiation

Investigations into seizure initiation, in recent years, have focused almost entirely upon alterations of interneuronal function, chloride homeostasis, and extracellular potassium levels. In contrast, little attention has been directed toward a possible role of dendritic plateau potentials in the actual ictogenic transition, despite a substantial literature dating back 40 years regarding its importance generally in epilepsy. Here, we argue that an increase in dendritic excitability, coordinated across the population of pyramidal cells, is a key stage in ictogenesis.

Cortical sensory processing across motivational states during goal-directed behavior

Behavioral states can influence performance of goal-directed sensorimotor tasks. Yet, it is unclear how altered neuronal sensory representations in these states relate to task performance and learning. We trained water-restricted mice in a two-whisker discrimination task to study cortical circuits underlying perceptual decision-making under different levels of thirst. We identified somatosensory cortices as well as the premotor cortex as part of the circuit necessary for task execution. Two-photon calcium imaging in these areas identified populations selective to sensory or motor events. Analysis of task performance during individual sessions revealed distinct behavioral states induced by decreasing levels of thirst-related motivation. Learning was better explained by improvements in motivational state control rather than sensorimotor association. Whisker sensory representations in the cortex were altered across behavioral states. In particular, whisker stimuli could be better decoded from neuronal activity during high task performance states, suggesting that state-dependent changes of sensory processing influence decision-making.

High synaptic threshold for dendritic NMDA spike generation in human layer 2/3 pyramidal neurons

Neurons receive synaptic input primarily onto their dendrites. While we know much about the electrical properties of dendrites in rodents, we have only just started to describe their properties in the human brain. Here, we investigate the capacity of human dendrites to generate NMDA-receptor-dependent spikes (NMDA spikes). Using dendritic glutamate iontophoresis, as well as local dendritic synaptic stimulation, we find that human layer 2/3 pyramidal neurons can generate dendritic NMDA spikes. The capacity to evoke NMDA spikes in human neurons, however, was significantly reduced compared with that in rodents. Simulations in morphologically realistic and simplified models indicated that human neurons have a higher synaptic threshold for NMDA spike generation primarily due to the wider diameter of their dendrites. In summary, we find reduced NMDA spike generation in human compared with rodent layer 2/3 pyramidal neurons and provide evidence that this is due to the wider diameter of human dendrites.

Regulation of circuit organization and function through inhibitory synaptic plasticity

Diverse inhibitory neurons in the mammalian brain shape circuit connectivity and dynamics through mechanisms of synaptic plasticity. Inhibitory plasticity can establish excitation/inhibition (E/I) balance, control neuronal firing, and affect local calcium concentration, hence regulating neuronal activity at the network, single neuron, and dendritic level. Computational models can synthesize multiple experimental results and provide insight into how inhibitory plasticity controls circuit dynamics and sculpts connectivity by identifying phenomenological learning rules amenable to mathematical analysis. We highlight recent studies on the role of inhibitory plasticity in modulating excitatory plasticity, forming structured networks underlying memory formation and recall, and implementing adaptive phenomena and novelty detection. We conclude with experimental and modeling progress on the role of interneuron-specific plasticity in circuit computation and context-dependent learning.

Recent Advances at the Interface of Neuroscience and Artificial Neural Networks

Biological neural networks adapt and learn in diverse behavioral contexts. Artificial neural networks (ANNs) have exploited biological properties to solve complex problems. However, despite their effectiveness for specific tasks, ANNs are yet to realize the flexibility and adaptability of biological cognition. This review highlights recent advances in computational and experimental research to advance our understanding of biological and artificial intelligence. In particular, we discuss critical mechanisms from the cellular, systems, and cognitive neuroscience fields that have contributed to refining the architecture and training algorithms of ANNs. Additionally, we discuss how recent work used ANNs to understand complex neuronal correlates of cognition and to process high throughput behavioral data.

Ultrafast simulation of large-scale neocortical microcircuitry with biophysically realistic neurons

Understanding the activity of the mammalian brain requires an integrative knowledge of circuits at distinct scales, ranging from ion channel gating to circuit connectomics. Computational models are regularly employed to understand how multiple parameters contribute synergistically to circuit behavior. However, traditional models of anatomically and biophysically realistic neurons are computationally demanding, especially when scaled to model local circuits. To overcome this limitation, we trained several artificial neural network (ANN) architectures to model the activity of realistic multicompartmental cortical neurons. We identified an ANN architecture that accurately predicted subthreshold activity and action potential firing. The ANN could correctly generalize to previously unobserved synaptic input, including in models containing nonlinear dendritic properties. When scaled, processing times were orders of magnitude faster compared with traditional approaches, allowing for rapid parameter-space mapping in a circuit model of Rett syndrome. Thus, we present a novel ANN approach allowing for rapid, detailed network experiments using inexpensive and commonly available computational resources.

Layer-specific, retinotopically-diffuse modulation in human visual cortex in response to viewing emotionally expressive faces

Viewing faces that are perceived as emotionally expressive evokes enhanced neural responses in multiple brain regions, a phenomenon thought to depend critically on the amygdala. This emotion-related modulation is evident even in primary visual cortex (V1), providing a potential neural substrate by which emotionally salient stimuli can affect perception. How does emotional valence information, computed in the amygdala, reach V1? Here we use high-resolution functional MRI to investigate the layer profile and retinotopic distribution of neural activity specific to emotional facial expressions. Across three experiments, human participants viewed centrally presented face stimuli varying in emotional expression and performed a gender judgment task. We found that facial valence sensitivity was evident only in superficial cortical layers and was not restricted to the retinotopic location of the stimuli, consistent with diffuse feedback-like projections from the amygdala. Together, our results provide a feedback mechanism by which the amygdala directly modulates activity at the earliest stage of visual processing.

Chronic Ca2+ imaging of cortical neurons with long-term expression of GCaMP-X

Dynamic Ca2+ signals reflect acute changes in membrane excitability, and also mediate signaling cascades in chronic processes. In both cases, chronic Ca2+ imaging is often desired, but challenged by the cytotoxicity intrinsic to calmodulin (CaM)-based GCaMP, a series of genetically-encoded Ca2+ indicators that have been widely applied. Here, we demonstrate the performance of GCaMP-X in chronic Ca2+ imaging of cortical neurons, where GCaMP-X by design is to eliminate the unwanted interactions between the conventional GCaMP and endogenous (apo)CaM-binding proteins. By expressing in adult mice at high levels over an extended time frame, GCaMP-X showed less damage and improved performance in two-photon imaging of sensory (whisker-deflection) responses or spontaneous Ca2+ fluctuations, in comparison with GCaMP. Chronic Ca2+ imaging of one month or longer was conducted for cultured cortical neurons expressing GCaMP-X, unveiling that spontaneous/local Ca2+ transients progressively developed into autonomous/global Ca2+ oscillations. Along with the morphological indices of neurite length and soma size, the major metrics of oscillatory Ca2+, including rate, amplitude and synchrony were also examined. Dysregulations of both neuritogenesis and Ca2+ oscillations became discernible around 2-3 weeks after virus injection or drug induction to express GCaMP in newborn or mature neurons, which were exacerbated by stronger or prolonged expression of GCaMP. In contrast, neurons expressing GCaMP-X were significantly less damaged or perturbed, altogether highlighting the unique importance of oscillatory Ca2+ to neural development and neuronal health. In summary, GCaMP-X provides a viable solution for Ca2+ imaging applications involving long-time and/or high-level expression of Ca2+ probes.

Quantitative analysis of the GABAergic innervation of the soma and axon initial segment of pyramidal cells in the human and mouse neocortex

Perisomatic GABAergic innervation in the cerebral cortex is carried out mostly by basket and chandelier cells, which differentially participate in the control of pyramidal cell action potential output and synchronization. These cells establish multiple synapses with the cell body (and proximal dendrites) and the axon initial segment (AIS) of pyramidal neurons, respectively. Using multiple immunofluorescence, confocal microscopy and 3D quantification techniques, we have estimated the number and density of GABAergic boutons on the cell body and AIS of pyramidal neurons located through cortical layers of the human and mouse neocortex. The results revealed, in both species, that there is clear variability across layers regarding the density and number of perisomatic GABAergic boutons. We found a positive linear correlation between the surface area of the soma, or the AIS, and the number of GABAergic terminals in apposition to these 2 neuronal domains. Furthermore, the density of perisomatic GABAergic boutons was higher in the human cortex than in the mouse. These results suggest a selectivity for the GABAergic innervation of the cell body and AIS that might be related to the different functional attributes of the microcircuits in which neurons from different layers are involved in both human and mouse.

Light-evoked dendritic spikes in sustained but not transient rabbit retinal ganglion cells

Dendritic computations have a central role in neuronal function, but it is unknown how cell-class heterogeneity of dendritic electrical excitability shapes physiologically engaged neuronal and circuit computations. To address this, we examined dendritic integration in closely related classes of retinal ganglion cells (GCs) using simultaneous somato-dendritic electrical recording techniques in a functionally intact circuit. Simultaneous recordings revealed sustained OFF-GCs generated powerful dendritic spikes in response to visual input that drove action potential firing. In contrast, the dendrites of transient OFF-GCs were passive and did not generate dendritic spikes. Dendritic spike generation allowed sustained, but not transient, OFF-GCs to signal into action potential output the local motion of visual stimuli to produce a continuous wave of action potential firing in adjacent cells as images moved across the retina. Conversely, this representation was highly fragmented in transient OFF-GCs. Thus, a heterogeneity of dendritic excitability defines the computations executed by classes of GCs.

Reduced expression of the psychiatric risk gene DLG2 (PSD93) impairs hippocampal synaptic integration and plasticity

Copy number variants indicating loss of function in the DLG2 gene have been associated with markedly increased risk for schizophrenia, autism spectrum disorder, and intellectual disability. DLG2 encodes the postsynaptic scaffolding protein DLG2 (PSD93) that interacts with NMDA receptors, potassium channels, and cytoskeletal regulators but the net impact of these interactions on synaptic plasticity, likely underpinning cognitive impairments associated with these conditions, remains unclear. Here, hippocampal CA1 neuronal excitability and synaptic function were investigated in a novel clinically relevant heterozygous Dlg2+/- rat model using ex vivo patch-clamp electrophysiology, pharmacology, and computational modelling. Dlg2+/- rats had reduced supra-linear dendritic integration of synaptic inputs resulting in impaired associative long-term potentiation. This impairment was not caused by a change in synaptic input since NMDA receptor-mediated synaptic currents were, conversely, increased and AMPA receptor-mediated currents were unaffected. Instead, the impairment in associative long-term potentiation resulted from an increase in potassium channel function leading to a decrease in input resistance, which reduced supra-linear dendritic integration. Enhancement of dendritic excitability by blockade of potassium channels or activation of muscarinic M1 receptors with selective allosteric agonist 77-LH-28-1 reduced the threshold for dendritic integration and 77-LH-28-1 rescued the associative long-term potentiation impairment in the Dlg2+/- rats. These findings demonstrate a biological phenotype that can be reversed by compound classes used clinically, such as muscarinic M1 receptor agonists, and is therefore a potential target for therapeutic intervention.

A calcium-based plasticity model for predicting long-term potentiation and depression in the neocortex

Pyramidal cells (PCs) form the backbone of the layered structure of the neocortex, and plasticity of their synapses is thought to underlie learning in the brain. However, such long-term synaptic changes have been experimentally characterized between only a few types of PCs, posing a significant barrier for studying neocortical learning mechanisms. Here we introduce a model of synaptic plasticity based on data-constrained postsynaptic calcium dynamics, and show in a neocortical microcircuit model that a single parameter set is sufficient to unify the available experimental findings on long-term potentiation (LTP) and long-term depression (LTD) of PC connections. In particular, we find that the diverse plasticity outcomes across the different PC types can be explained by cell-type-specific synaptic physiology, cell morphology and innervation patterns, without requiring type-specific plasticity. Generalizing the model to in vivo extracellular calcium concentrations, we predict qualitatively different plasticity dynamics from those observed in vitro. This work provides a first comprehensive null model for LTP/LTD between neocortical PC types in vivo, and an open framework for further developing models of cortical synaptic plasticity.

Assessing Local and Branch-specific Activity in Dendrites

Dendrites are elaborate neural processes which integrate inputs from various sources in space and time. While decades of work have suggested an independent role for dendrites in driving nonlinear computations for the cell, only recently have technological advances enabled us to capture the variety of activity in dendrites and their coupling dynamics with the soma. Under certain circumstances, activity generated in a given dendritic branch remains isolated, such that the soma or even sister dendrites are not privy to these localized signals. Such branch-specific activity could radically increase the capacity and flexibility of coding for the cell as a whole. Here, we discuss these forms of localized and branch-specific activity, their functional relevance in plasticity and behavior, and their supporting biophysical and circuit-level mechanisms. We conclude by showcasing electrical and optical approaches in hippocampal area CA3, using original experimental data to discuss experimental and analytical methodology and key considerations to take when investigating the functional relevance of independent dendritic activity.

Learning-related congruent and incongruent changes of excitation and inhibition in distinct cortical areas

Excitatory and inhibitory neurons in diverse cortical regions are likely to contribute differentially to the transformation of sensory information into goal-directed motor plans. Here, we investigate the relative changes across mouse sensorimotor cortex in the activity of putative excitatory and inhibitory neurons—categorized as regular spiking (RS) or fast spiking (FS) according to their action potential (AP) waveform—comparing before and after learning of a whisker detection task with delayed licking as perceptual report. Surprisingly, we found that the whisker-evoked activity of RS versus FS neurons changed in opposite directions after learning in primary and secondary whisker motor cortices, while it changed similarly in primary and secondary orofacial motor cortices. Our results suggest that changes in the balance of excitation and inhibition in local circuits concurrent with changes in the long-range synaptic inputs in distinct cortical regions might contribute to performance of delayed sensory-to-motor transformation.

A study of mouse sensorimotor cortex during a whisker detection task shows that learning of a goal-directed sensorimotor transformation is accompanied by differential changes in excitation and inhibition in distinct neocortical regions, helping to link sensory cortex and motor cortex for correct task performance.

SAM: A Unified Self-Adaptive Multicompartmental Spiking Neuron Model for Learning With Working Memory

Working memory is a fundamental feature of biological brains for perception, cognition, and learning. In addition, learning with working memory, which has been show in conventional artificial intelligence systems through recurrent neural networks, is instrumental to advanced cognitive intelligence. However, it is hard to endow a simple neuron model with working memory, and to understand the biological mechanisms that have resulted in such a powerful ability at the neuronal level. This article presents a novel self-adaptive multicompartment spiking neuron model, referred to as SAM, for spike-based learning with working memory. SAM integrates four major biological principles including sparse coding, dendritic non-linearity, intrinsic self-adaptive dynamics, and spike-driven learning. We first describe SAM's design and explore the impacts of critical parameters on its biological dynamics. We then use SAM to build spiking networks to accomplish several different tasks including supervised learning of the MNIST dataset using sequential spatiotemporal encoding, noisy spike pattern classification, sparse coding during pattern classification, spatiotemporal feature detection, meta-learning with working memory applied to a navigation task and the MNIST classification task, and working memory for spatiotemporal learning. Our experimental results highlight the energy efficiency and robustness of SAM in these wide range of challenging tasks. The effects of SAM model variations on its working memory are also explored, hoping to offer insight into the biological mechanisms underlying working memory in the brain. The SAM model is the first attempt to integrate the capabilities of spike-driven learning and working memory in a unified single neuron with multiple timescale dynamics. The competitive performance of SAM could potentially contribute to the development of efficient adaptive neuromorphic computing systems for various applications from robotics to edge computing.

Compartmentalized dendritic plasticity during associative learning

Experience-dependent changes in behavior are mediated by long-term functional modifications in brain circuits. Activity-dependent plasticity of synaptic input is a major underlying cellular process. Although we have a detailed understanding of synaptic and dendritic plasticity in vitro, little is known about the functional and plastic properties of active dendrites in behaving animals. Using deep brain two-photon Ca²⁺ imaging, we investigated how sensory responses in amygdala principal neurons develop upon classical fear conditioning, a form of associative learning. Fear conditioning induced differential plasticity in dendrites and somas regulated by compartment-specific inhibition. Our results indicate that learning-induced plasticity can be uncoupled between soma and dendrites, reflecting distinct synaptic and microcircuit-level mechanisms that increase the computational capacity of amygdala circuits.

Dynamic interplay between thalamic activity and Cajal-Retzius cells regulates the wiring of cortical layer 1

Cortical wiring relies on guidepost cells and activity-dependent processes that are thought to act sequentially. Here, we show that the construction of layer 1 (L1), a main site of top-down integration, is regulated by crosstalk between transient Cajal-Retzius cells (CRc) and spontaneous activity of the thalamus, a main driver of bottom-up information. While activity was known to regulate CRc migration and elimination, we found that prenatal spontaneous thalamic activity and NMDA receptors selectively control CRc early density, without affecting their demise. CRc density, in turn, regulates the distribution of upper layer interneurons and excitatory synapses, thereby drastically impairing the apical dendrite activity of output pyramidal neurons. In contrast, postnatal sensory-evoked activity had a limited impact on L1 and selectively perturbed basal dendrites synaptogenesis. Collectively, our study highlights a remarkable interplay between thalamic activity and CRc in L1 functional wiring, with major implications for our understanding of cortical development.

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Prenatal thalamic waves of activity regulate CRc density in L1

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Prenatal and postnatal CRc manipulations alter specific interneuron populations

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Postnatal CRc shape L5 apical dendrite structural and functional properties

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Early sensory activity selectively regulates L5 basal dendrite spine formation

Breaking trade-offs: development of fast, high-resolution, wide-field two-photon microscopes to reveal the computational principles of the brain

Information in the brain is represented by the collective and coordinated activity of single neurons. Activity is determined by a large amount of dynamic synaptic inputs from neurons in the same and/or distant brain regions. Therefore, the simultaneous recording of single neurons across several brain regions is critical for revealing the interactions among neurons that reflect the computational principles of the brain. Recently, several wide-field two-photon (2P) microscopes equipped with sizeable objective lenses have been reported. These microscopes enable large-scale in vivo calcium imaging and have the potential to make a significant contribution to the elucidation of information-processing mechanisms in the cerebral cortex. This review discusses recent reports on wide-field 2P microscopes and describes the trade-offs encountered in developing wide-field 2P microscopes. Large-scale imaging of neural activity allows us to test hypotheses proposed in theoretical neuroscience, and to identify rare but influential neurons that have potentially significant impacts on the whole-brain system.

Robust effects of corticothalamic feedback and behavioral state on movie responses in mouse dLGN

Neurons in the dorsolateral geniculate nucleus (dLGN) of the thalamus receive a substantial proportion of modulatory inputs from corticothalamic (CT) feedback and brain stem nuclei. Hypothesizing that these modulatory influences might be differentially engaged depending on the visual stimulus and behavioral state, we performed in vivo extracellular recordings from mouse dLGN while optogenetically suppressing CT feedback and monitoring behavioral state by locomotion and pupil dilation. For naturalistic movie clips, we found CT feedback to consistently increase dLGN response gain and promote tonic firing. In contrast, for gratings, CT feedback effects on firing rates were mixed. For both stimulus types, the neural signatures of CT feedback closely resembled those of behavioral state, yet effects of behavioral state on responses to movies persisted even when CT feedback was suppressed. We conclude that CT feedback modulates visual information on its way to cortex in a stimulus-dependent manner, but largely independently of behavioral state.

Reorganization of CA1 dendritic dynamics by hippocampal sharp-wave ripples during learning

The hippocampus plays a critical role in memory consolidation, mediated by coordinated network activity during sharp-wave ripple (SWR) events. Despite the link between SWRs and hippocampal plasticity, little is known about how network state affects information processing in dendrites, the primary sites of synaptic input integration and plasticity. Here, we monitored somatic and basal dendritic activity in CA1 pyramidal cells in behaving mice using longitudinal two-photon calcium imaging integrated with simultaneous local field potential recordings. We found immobility was associated with an increase in dendritic activity concentrated during SWRs. Coincident dendritic and somatic activity during SWRs predicted increased coupling during subsequent exploration of a novel environment. In contrast, somatic-dendritic coupling and SWR recruitment varied with cells' tuning distance to reward location during a goal-learning task. Our results connect SWRs with the stabilization of information processing within CA1 neurons and suggest that these mechanisms may be dynamically biased by behavioral demands.

The role of higher order thalamus during learning and correct performance in goal-directed behavior

The thalamus is a gateway to the cortex. Cortical encoding of complex behavior can therefore only be understood by considering the thalamic processing of sensory and internally generated information. Here, we use two-photon Ca²⁺ imaging and optogenetics to investigate the role of axonal projections from the posteromedial nucleus of the thalamus (POm) to the forepaw area of the mouse primary somatosensory cortex (forepaw S1). By recording the activity of POm axonal projections within forepaw S1 during expert and chance performance in two tactile goal-directed tasks, we demonstrate that POm axons increase activity in the response and, to a lesser extent, reward epochs specifically during correct HIT performance. When performing at chance level during learning of a new behavior, POm axonal activity was decreased to naive rates and did not correlate with task performance. However, once evoked, the Ca²⁺ transients were larger than during expert performance, suggesting POm input to S1 differentially encodes chance and expert performance. Furthermore, the POm influences goal-directed behavior, as photoinactivation of archaerhodopsin-expressing neurons in the POm decreased the learning rate and overall success in the behavioral task. Taken together, these findings expand the known roles of the higher-thalamic nuclei, illustrating the POm encodes and influences correct action during learning and performance in a sensory-based goal-directed behavior.

The neural hierarchy of consciousness

The chief undertaking in the studies of consciousness is that of unravelling "the minimal set of neural processes that are together sufficient for the conscious experience of a particular content - the neural correlates of consciousness". To this day, this crusade remains at an impasse, with a clash of two main theories: consciousness may arise either in a graded and cortically-localised fashion, or in an all-or-none and widespread one. In spite of the long-lasting theoretical debates, neurophysiological theories of consciousness have been mostly dissociated from them. Herein, a theoretical review will be put forth with the aim to change that. In its first half, we will cover the hard available evidence on the neurophysiology of consciousness, whereas in its second half we will weave a series of considerations on both theories and substantiate a novel take on conscious awareness: the levels of processing approach, partitioning the conscious architecture into lower- and higher-order, graded and nonlinear.

Emergence of synaptic organization and computation in dendrites

Single neurons in the brain exhibit astounding computational capabilities, which gradually emerge throughout development and enable them to become integrated into complex neural circuits. These capabilities derive in part from the precise arrangement of synaptic inputs on the neurons' dendrites. While the full computational benefits of this arrangement are still unknown, a picture emerges in which synapses organize according to their functional properties across multiple spatial scales. In particular, on the local scale (tens of microns), excitatory synaptic inputs tend to form clusters according to their functional similarity, whereas on the scale of individual dendrites or the entire tree, synaptic inputs exhibit dendritic maps where excitatory synapse function varies smoothly with location on the tree. The development of this organization is supported by inhibitory synapses, which are carefully interleaved with excitatory synapses and can flexibly modulate activity and plasticity of excitatory synapses. Here, we summarize recent experimental and theoretical research on the developmental emergence of this synaptic organization and its impact on neural computations.

The guide to dendritic spikes of the mammalian cortex in vitro and in vivo

Half a century since their discovery by Llinás and colleagues, dendritic spikes have been observed in various neurons in different brain regions, from the neocortex and cerebellum to the basal ganglia. Dendrites exhibit a terrifically diverse but stereotypical repertoire of spikes, sometimes specific to subregions of the dendrite. Despite their prevalence, we only have a glimpse into their role in the behaving animal. This article aims to survey the full range of dendritic spikes found in excitatory and inhibitory neurons, compare them in vivo versus in vitro, and discuss new studies describing dendritic spikes in the human cortex. We focus on dendritic spikes in neocortical and hippocampal neurons and present a roadmap to identify and understand the broader role of dendritic spikes in single-cell computation.

Four concurrent feedforward and feedback networks with different roles in the visual cortical hierarchy

Visual stimuli evoke fast-evolving activity patterns that are distributed across multiple cortical areas. These areas are hierarchically structured, as indicated by their anatomical projections, but how large-scale feedforward and feedback streams are functionally organized in this system remains an important missing clue to understanding cortical processing. By analyzing visual evoked responses in laminar recordings from 6 cortical areas in awake mice, we uncovered a dominant feedforward network with scale-free interactions in the time domain. In addition, we established the simultaneous presence of a gamma band feedforward and 2 low frequency feedback networks, each with a distinct laminar functional connectivity profile, frequency spectrum, temporal dynamics, and functional hierarchy. We could identify distinct roles for each of these 4 processing streams, by leveraging stimulus contrast effects, analyzing receptive field (RF) convergency along functional interactions, and determining relationships to spiking activity. Our results support a dynamic dual counterstream view of hierarchical processing and provide new insight into how separate functional streams can simultaneously and dynamically support visual processes.

Visual stimuli evoke fast-evolving activity patterns that are distributed across multiple cortical areas, but how large-scale feedforward and feedback streams are functionally organized in this system remains unclear. Visual evoked responses in laminar recordings from six cortical areas in awake mice reveal how layers and rhythms dynamically orchestrate functional streams in vision.

Thalamocortical Mechanisms Regulating the Relationship between Transient Beta Events and Human Tactile Perception

Transient neocortical events with high spectral power in the 15-29 Hz beta band are among the most reliable predictors of sensory perception. Prestimulus beta event rates in primary somatosensory cortex correlate with sensory suppression, most effectively 100-300 ms before stimulus onset. However, the neural mechanisms underlying this perceptual association are unknown. We combined human magnetoencephalography (MEG) measurements with biophysical neural modeling to test potential cellular and circuit mechanisms that underlie observed correlations between prestimulus beta events and tactile detection. Extending prior studies, we found that simulated bursts from higher-order, nonlemniscal thalamus were sufficient to drive beta event generation and to recruit slow supragranular inhibition acting on a 300 ms timescale to suppress sensory information. Further analysis showed that the same beta-generating mechanism can lead to facilitated perception for a brief period when beta events occur simultaneously with tactile stimulation before inhibition is recruited. These findings were supported by close agreement between model-derived predictions and empirical MEG data. The postevent suppressive mechanism explains an array of studies that associate beta with decreased processing, whereas the during-event facilitatory mechanism may demand a reinterpretation of the role of beta events in the context of coincident timing.

Long anisotropic absolute refractory periods with rapid rise times to reliable responsiveness

Refractoriness is a fundamental property of excitable elements, such as neurons, indicating the probability for re-excitation in a given time lag, and is typically linked to the neuronal hyperpolarization following an evoked spike. Here we measured the refractory periods (RPs) in neuronal cultures and observed that an average anisotropic absolute RP could exceed 10 ms and its tail is 20 ms, independent of a large stimulation frequency range. It is an order of magnitude longer than anticipated and comparable with the decaying membrane potential time scale. It is followed by a sharp rise-time (relative RP) of merely ∼1 md to complete responsiveness. Extracellular stimulations result in longer absolute RPs than solely intracellular ones, and a pair of extracellular stimulations from two different routes exhibits distinct absolute RPs, depending on their order. Our results indicate that a neuron is an accurate excitable element, where the diverse RPs cannot be attributed solely to the soma and imply fast mutual interactions between different stimulation routes and dendrites. Further elucidation of neuronal computational capabilities and their interplay with adaptation mechanisms is warranted.

Representational 'touch' and modulatory 'retouch'-two necessary neurobiological processes in thalamocortical interaction for conscious experience

Theories of consciousness using neurobiological data or being influenced by these data have been focused either on states of consciousness or contents of consciousness. These theories have occasionally used evidence from psychophysical phenomena where conscious experience is a dependent experimental variable. However, systematic catalog of many such relevant phenomena has not been offered in terms of these theories. In the perceptual retouch theory of thalamocortical interaction, recently developed to become a blend with the dendritic integration theory, consciousness states and contents of consciousness are explained by the same mechanism. This general-purpose mechanism has modulation of the cortical layer-5 pyramidal neurons that represent contents of consciousness as its core. As a surplus, many experimental psychophysical phenomena of conscious perception can be explained by the workings of this mechanism. Historical origins and current views inherent in this theory are presented and reviewed.

Itch perception is reflected by neuronal ignition in the primary somatosensory cortex

Multiple cortical areas including the primary somatosensory cortex (S1) are activated during itch signal processing, yet cortical representation of itch perception remains unknown. Using novel miniature two-photon microscopic imaging in free-moving mice, we investigated the coding of itch perception in S1. We found that pharmacological inactivation of S1 abolished itch-induced scratching behavior, and the itch-induced scratching behavior could be well predicted by the activity of a fraction of layer 2/3 pyramidal neurons, suggesting that a subpopulation of S1 pyramidal neurons encoded itch perception, as indicated by immediate subsequent scratching behaviors. With a newly established optogenetics-based paradigm that allows precisely controlled pruritic stimulation, we found that a small fraction of S1 neurons exhibited an ignition-like pattern at the detection threshold of itch perception. Our study revealed the neural mechanism underlying itch perceptual coding in S1, thus paving the way for the study of cortical representation of itch perception at the single-neuron level in freely moving animals.

Columnar processing of border ownership in primate visual cortex

To understand a visual scene, the brain segregates figures from background by assigning borders to foreground objects. Neurons in primate visual cortex encode which object owns a border (border ownership), but the underlying circuitry is not understood. Here, we used multielectrode probes to record from border ownership-selective units in different layers in macaque visual area V4 to study the laminar organization and timing of border ownership selectivity. We find that border ownership selectivity occurs first in deep layer units, in contrast to spike latency for small stimuli in the classical receptive field. Units on the same penetration typically share the preferred side of border ownership, also across layers, similar to orientation preference. Units are often border ownership-selective for a range of border orientations, where the preferred sides of border ownership are systematically organized in visual space. Together our data reveal a columnar organization of border ownership in V4 where the earliest border ownership signals are not simply inherited from upstream areas, but computed by neurons in deep layers, and may thus be part of signals fed back to upstream cortical areas or the oculomotor system early after stimulus onset. The finding that preferred border ownership is clustered and can cover a wide range of spatially contiguous locations suggests that the asymmetric context integrated by these neurons is provided in a systematically clustered manner, possibly through corticocortical feedback and horizontal connections.

To understand a visual scene, the brain needs to identify objects and distinguish them from background. A border marks the transition from object to background, but to differentiate which side of the border belongs to the object and which to background, the brain must integrate information across space. An early signature of this computation is that brain cells signal which side of a border is ‘owned’ by an object, also known as border ownership. But how the brain computes border ownership remains unknown.

The optic nerve is a cable-like group of nerve cells that transmits information from the eye to the brain’s visual processing areas and into the visual cortex. This flow of information is often described as traveling in a feedforward direction, away from the eyes to progressively more specialized areas in the visual cortex. However, there are also numerous feedback connections in the brain, running backward from more specialized to less specialized cortical areas.

To better understand the role of these feedforward and feedback circuits in the visual processing of object borders, Franken and Reynolds made use of their stereotyped projection patterns across the cortex layers. Feedforward connections terminate in the middle layers of a cortical area, whereas feedback connections terminate in upper and lower layers. Since time is required for information to traverse the cortical layers, dissecting the timing of border ownership signals may reveal if border ownership is computed in a feedforward or feedback manner. To find out more, electrodes were used to record neural activity in the upper, middle and lower layers of the visual cortex of two rhesus monkeys as they were presented with a set of abstract scenes composed of simple shapes on a background.

This revealed that cells signaling border ownership in deep layers of the cortex did so before the signals appeared in the middle layer. This suggests that feedback rather than feedforward is required to compute border ownership. Moreover, Franken and Reynolds found evidence that cells that prefer the same side of border ownership are clustered in columns, showing how these neural circuits are organized within the visual cortex.

In summary, Franken and Reynolds found that the circuits of the primate brain that compute border ownership occur as columns, in which cells in deep layers signal border ownership first, suggesting that border ownership relies on feedback from more specialized areas. A better understanding of how feedback in the brain works to process visual information helps us appreciate what happens when these systems are impaired.

Self-organization of a doubly asynchronous irregular network state for spikes and bursts

Cortical pyramidal cells (PCs) have a specialized dendritic mechanism for the generation of bursts, suggesting that these events play a special role in cortical information processing. In vivo, bursts occur at a low, but consistent rate. Theory suggests that this network state increases the amount of information they convey. However, because burst activity relies on a threshold mechanism, it is rather sensitive to dendritic input levels. In spiking network models, network states in which bursts occur rarely are therefore typically not robust, but require fine-tuning. Here, we show that this issue can be solved by a homeostatic inhibitory plasticity rule in dendrite-targeting interneurons that is consistent with experimental data. The suggested learning rule can be combined with other forms of inhibitory plasticity to self-organize a network state in which both spikes and bursts occur asynchronously and irregularly at low rate. Finally, we show that this network state creates the network conditions for a recently suggested multiplexed code and thereby indeed increases the amount of information encoded in bursts.

Burst control: Synaptic conditions for burst generation in cortical layer 5 pyramidal neurons

The output of neocortical layer 5 pyramidal cells (L5PCs) is expressed by a train of single spikes with intermittent bursts of multiple spikes at high frequencies. The bursts are the result of nonlinear dendritic properties, including Na+, Ca2+, and NMDA spikes, that interact with the ~10,000 synapses impinging on the neuron's dendrites. Output spike bursts are thought to implement key dendritic computations, such as coincidence detection of bottom-up inputs (arriving mostly at the basal tree) and top-down inputs (arriving mostly at the apical tree). In this study we used a detailed nonlinear model of L5PC receiving excitatory and inhibitory synaptic inputs to explore the conditions for generating bursts and for modulating their properties. We established the excitatory input conditions on the basal versus the apical tree that favor burst and show that there are two distinct types of bursts. Bursts consisting of 3 or more spikes firing at < 200 Hz, which are generated by stronger excitatory input to the basal versus the apical tree, and bursts of ~2-spikes at ~250 Hz, generated by prominent apical tuft excitation. Localized and well-timed dendritic inhibition on the apical tree differentially modulates Na+, Ca2+, and NMDA spikes and, consequently, finely controls the burst output. Finally, we explored the implications of different burst classes and respective dendritic inhibition for regulating synaptic plasticity.

Apical amplification-a cellular mechanism of conscious perception?

We present a theoretical view of the cellular foundations for network-level processes involved in producing our conscious experience. Inputs to apical synapses in layer 1 of a large subset of neocortical cells are summed at an integration zone near the top of their apical trunk. These inputs come from diverse sources and provide a context within which the transmission of information abstracted from sensory input to their basal and perisomatic synapses can be amplified when relevant. We argue that apical amplification enables conscious perceptual experience and makes it more flexible, and thus more adaptive, by being sensitive to context. Apical amplification provides a possible mechanism for recurrent processing theory that avoids strong loops. It makes the broadcasting hypothesized by global neuronal workspace theories feasible while preserving the distinct contributions of the individual cells receiving the broadcast. It also provides mechanisms that contribute to the holistic aspects of integrated information theory. As apical amplification is highly dependent on cholinergic, aminergic, and other neuromodulators, it relates the specific contents of conscious experience to global mental states and to fluctuations in arousal when awake. We conclude that apical dendrites provide a cellular mechanism for the context-sensitive selective amplification that is a cardinal prerequisite of conscious perception.

Semantic priming and neurobiology in schizophrenia: A theoretical review

In this theoretical review we bridge the cognitive and neurobiological sciences to shed light on the neurocognitive foundations of the semantic priming effect in schizophrenia. We review and theoretically evaluate the neurotransmitter systems (dopaminergic, GABAergic and glutamatergic) and neurobiological underpinnings of behavioural and electrophysiological (N400) semantic priming in the pathology, and the main hypotheses on their geneses: a disinhibition of the semantic spread of activation, a disorganised semantic storage or noisy lexical-semantic associations, a psychomotor artefact, an artefact of relatedness proportions, or an inability to mobilise contextual information. We further assess the literature on the endophenotype of Formal Thought Disorder from multiple standpoints, ranging from neurophysiology to cognition: considerations are weaved on neuronal (PV basket cell, SST, VIP) and receptor deficits (DRD1, NMDA), neurotransmitter imbalances (dopamine), cortical and dopaminergic lateralisation, inter alia. In conclusion, we put forth novel postulates on the underlying causes of controlled hypopriming, automatic hyperpriming, N400 reversals (larger amplitudes for close associations), indirect versus direct hyperpriming, and the endophenotype of lexical-semantic disturbances in schizophrenia.

Correcting for Superficial Bias in 7T Gradient Echo fMRI

The arrival of submillimeter ultra high-field fMRI makes it possible to compare activation profiles across cortical layers. However, the blood oxygenation level dependent (BOLD) signal measured by gradient echo (GE) fMRI is biased toward superficial layers of the cortex, which is a serious confound for laminar analysis. Several univariate and multivariate analysis methods have been proposed to correct this bias. We compare these methods using computational simulations of 7T fMRI data from regions of interest (ROI) during a visual attention paradigm. We also tested the methods on a pilot dataset of human 7T fMRI data. The simulations show that two methods–the ratio of ROI means across conditions and a novel application of Deming regression–offer the most robust correction for superficial bias. Deming regression has the additional advantage that it does not require that the conditions differ in their mean activation over voxels within an ROI. When applied to the pilot dataset, we observed strikingly different layer profiles when different attention metrics were used, but were unable to discern any differences in laminar attention across layers when Deming regression or ROI ratio was applied. Our simulations demonstrates that accurate correction of superficial bias is crucial to avoid drawing erroneous conclusions from laminar analyses of GE fMRI data, and this is affirmed by the results from our pilot 7T fMRI data.

Layer 6A Pyramidal Cell Subtypes Form Synaptic Microcircuits with Distinct Functional and Structural Properties

Neocortical layer 6 plays a crucial role in sensorimotor co-ordination and integration through functionally segregated circuits linking intracortical and subcortical areas. We performed whole-cell recordings combined with morphological reconstructions to identify morpho-electric types of layer 6A pyramidal cells (PCs) in rat barrel cortex. Cortico-thalamic (CT), cortico-cortical (CC), and cortico-claustral (CCla) PCs were classified based on their distinct morphologies and have been shown to exhibit different electrophysiological properties. We demonstrate that these three types of layer 6A PCs innervate neighboring excitatory neurons with distinct synaptic properties: CT PCs establish weak facilitating synapses onto other L6A PCs; CC PCs form synapses of moderate efficacy, while synapses made by putative CCla PCs display the highest release probability and a marked short-term depression. For excitatory-inhibitory synaptic connections in layer 6, both the presynaptic PC type and the postsynaptic interneuron type govern the dynamic properties of the respective synaptic connections. We have identified a functional division of local layer 6A excitatory microcircuits which may be responsible for the differential temporal engagement of layer 6 feed-forward and feedback networks. Our results provide a basis for further investigations on the long-range CC, CT, and CCla pathways.

GABA B Receptor-Mediated Regulation of Dendro-Somatic Synergy in Layer 5 Pyramidal Neurons

Synergistic interactions between independent synaptic input streams may fundamentally change the action potential (AP) output. Using partial information decomposition, we demonstrate here a substantial contribution of synergy between somatic and apical dendritic inputs to the information in the AP output of L5b pyramidal neurons. Activation of dendritic GABA_B receptors (GABA_BRs), known to decrease APs in vivo, potently decreased synergy and increased somatic control of AP output. Synergy was the result of the voltage-dependence of the transfer resistance between dendrite and soma, which showed a two-fold increase per 28.7 mV dendritic depolarization. GIRK channels activated by dendritic GABA_BRs decreased voltage-dependent transfer resistances and AP output. In contrast, inhibition of dendritic L-type Ca²⁺ channels prevented high-frequency bursts of APs, but did not affect dendro-somatic synergy. Finally, we show that NDNF-positive neurogliaform cells effectively control somatic AP via synaptic activation of dendritic GIRK channels. These results uncover a novel inhibitory mechanism that powerfully gates cellular information flow in the cortex.

Corticothalamic Pathways From Layer 5: Emerging Roles in Computation and Pathology

Large portions of the thalamus receive strong driving input from cortical layer 5 (L5) neurons but the role of this important pathway in cortical and thalamic computations is not well understood. L5-recipient "higher-order" thalamic regions participate in cortico-thalamo-cortical (CTC) circuits that are increasingly recognized to be (1) anatomically and functionally distinct from better-studied "first-order" CTC networks, and (2) integral to cortical activity related to learning and perception. Additionally, studies are beginning to elucidate the clinical relevance of these networks, as dysfunction across these pathways have been implicated in several pathological states. In this review, we highlight recent advances in understanding L5 CTC networks across sensory modalities and brain regions, particularly studies leveraging cell-type-specific tools that allow precise experimental access to L5 CTC circuits. We aim to provide a focused and accessible summary of the anatomical, physiological, and computational properties of L5-originating CTC networks, and outline their underappreciated contribution in pathology. We particularly seek to connect single-neuron and synaptic properties to network (dys)function and emerging theories of cortical computation, and highlight information processing in L5 CTC networks as a promising focus for computational studies.

Active Dendrites and Local Field Potentials: Biophysical Mechanisms and Computational Explorations

Neurons and glial cells are endowed with membranes that express a rich repertoire of ion channels, transporters, and receptors. The constant flux of ions across the neuronal and glial membranes results in voltage fluctuations that can be recorded from the extracellular matrix. The high frequency components of this voltage signal contain information about the spiking activity, reflecting the output from the neurons surrounding the recording location. The low frequency components of the signal, referred to as the local field potential (LFP), have been traditionally thought to provide information about the synaptic inputs that impinge on the large dendritic trees of various neurons. In this review, we discuss recent computational and experimental studies pointing to a critical role of several active dendritic mechanisms that can influence the genesis and the location-dependent spectro-temporal dynamics of LFPs, spanning different brain regions. We strongly emphasize the need to account for the several fast and slow dendritic events and associated active mechanisms - including gradients in their expression profiles, inter- and intra-cellular spatio-temporal interactions spanning neurons and glia, heterogeneities and degeneracy across scales, neuromodulatory influences, and activitydependent plasticity - towards gaining important insights about the origins of LFP under different behavioral states in health and disease. We provide simple but essential guidelines on how to model LFPs taking into account these dendritic mechanisms, with detailed methodology on how to account for various heterogeneities and electrophysiological properties of neurons and synapses while studying LFPs.

The Impact of SST and PV Interneurons on Nonlinear Synaptic Integration in the Neocortex

Excitatory synaptic inputs arriving at the dendrites of a neuron can engage active mechanisms that nonlinearly amplify the depolarizing currents. This supralinear synaptic integration is subject to modulation by inhibition. However, the specific rules by which different subtypes of interneurons affect the modulation have remained largely elusive. To examine how inhibition influences active synaptic integration, we optogenetically manipulated the activity of the following two subtypes of interneurons: dendrite-targeting somatostatin-expressing (SST) interneurons; and perisomatic-targeting parvalbumin-expressing (PV) interneurons. In acute slices of mouse primary visual cortex, electrical stimulation evoked nonlinear synaptic integration that depended on NMDA receptors. Optogenetic activation of SST interneurons in conjunction with electrical stimulation resulted in predominantly divisive inhibitory gain control, reducing the magnitude of the supralinear response without affecting its threshold. PV interneuron activation, on the other hand, had a minimal effect on the supralinear response. Together, these results delineate the roles for SST and PV neurons in active synaptic integration. Differential effects of inhibition by SST and PV interneurons likely increase the computational capacity of the pyramidal neurons in modulating the nonlinear integration of synaptic output.

Consciousness and complexity: a consilience of evidence

Over the last years, a surge of empirical studies converged on complexity-related measures as reliable markers of consciousness across many different conditions, such as sleep, anesthesia, hallucinatory states, coma, and related disorders. Most of these measures were independently proposed by researchers endorsing disparate frameworks and employing different methods and techniques. Since this body of evidence has not been systematically reviewed and coherently organized so far, this positive trend has remained somewhat below the radar. The aim of this paper is to make this consilience of evidence in the science of consciousness explicit. We start with a systematic assessment of the growing literature on complexity-related measures and identify their common denominator, tracing it back to core theoretical principles and predictions put forward more than 20 years ago. In doing this, we highlight a consistent trajectory spanning two decades of consciousness research and provide a provisional taxonomy of the present literature. Finally, we consider all of the above as a positive ground to approach new questions and devise future experiments that may help consolidate and further develop a promising field where empirical research on consciousness appears to have, so far, naturally converged.

An increase in dendritic plateau potentials is associated with experience-dependent cortical map reorganization

Here we describe a mechanism for cortical map plasticity. Classically, representational map changes are thought to be driven by changes within cortico-cortical circuits, e.g., Hebbian plasticity of synaptic circuits that lost vs. maintained an excitatory drive from the first-order thalamus, possibly steered by neuromodulatory forces from deep brain regions. Our work provides evidence for an additional gating mechanism, provided by plateau potentials, which are driven by higher-order thalamic feedback. Higher-order thalamic neurons are characterized by broad receptive fields, and the plateau potentials that they evoke strongly facilitate long-term potentiation and elicit spikes. We show that these features combined constitute a powerful driving force for the fusion or expansion of sensory representations within cortical maps.

The organization of sensory maps in the cerebral cortex depends on experience, which drives homeostatic and long-term synaptic plasticity of cortico-cortical circuits. In the mouse primary somatosensory cortex (S1) afferents from the higher-order, posterior medial thalamic nucleus (POm) gate synaptic plasticity in layer (L) 2/3 pyramidal neurons via disinhibition and the production of dendritic plateau potentials. Here we address whether these thalamocortically mediated responses play a role in whisker map plasticity in S1. We find that trimming all but two whiskers causes a partial fusion of the representations of the two spared whiskers, concomitantly with an increase in the occurrence of POm-driven N-methyl-D-aspartate receptor-dependent plateau potentials. Blocking the plateau potentials restores the archetypical organization of the sensory map. Our results reveal a mechanism for experience-dependent cortical map plasticity in which higher-order thalamocortically mediated plateau potentials facilitate the fusion of normally segregated cortical representations.

Active dendrites enable strong but sparse inputs to determine orientation selectivity

The dendrites of neocortical pyramidal neurons are excitable. However, it is unknown how synaptic inputs engage nonlinear dendritic mechanisms during sensory processing in vivo, and how they in turn influence action potential output. Here, we provide a quantitative account of the relationship between synaptic inputs, nonlinear dendritic events, and action potential output. We developed a detailed pyramidal neuron model constrained by in vivo dendritic recordings. We drive this model with realistic input patterns constrained by sensory responses measured in vivo and connectivity measured in vitro. We show mechanistically that under realistic conditions, dendritic Na+ and NMDA spikes are the major determinants of neuronal output in vivo. We demonstrate that these dendritic spikes can be triggered by a surprisingly small number of strong synaptic inputs, in some cases even by single synapses. We predict that dendritic excitability allows the 1% strongest synaptic inputs of a neuron to control the tuning of its output. Active dendrites therefore allow smaller subcircuits consisting of only a few strongly connected neurons to achieve selectivity for specific sensory features.

Auditory input enhances somatosensory encoding and tactile goal-directed behavior

The capacity of the brain to encode multiple types of sensory input is key to survival. Yet, how neurons integrate information from multiple sensory pathways and to what extent this influences behavior is largely unknown. Using two-photon Ca2+ imaging, optogenetics and electrophysiology in vivo and in vitro, we report the influence of auditory input on sensory encoding in the somatosensory cortex and show its impact on goal-directed behavior. Monosynaptic input from the auditory cortex enhanced dendritic and somatic encoding of tactile stimulation in layer 2/3 (L2/3), but not layer 5 (L5), pyramidal neurons in forepaw somatosensory cortex (S1). During a tactile-based goal-directed task, auditory input increased dendritic activity and reduced reaction time, which was abolished by photoinhibition of auditory cortex projections to forepaw S1. Taken together, these results indicate that dendrites of L2/3 pyramidal neurons encode multisensory information, leading to enhanced neuronal output and reduced response latency during goal-directed behavior.

Thalamic input to motor cortex facilitates goal-directed action initiation

Prompt execution of planned motor action is essential for survival. The interactions between frontal cortical circuits and the basal ganglia are central to goal-oriented action selection and initiation.^1-4 In rodents, the ventromedial thalamic nucleus (VM) is one of the critical nodes that conveys the output of the basal ganglia to the frontal cortical areas including the anterior lateral motor cortex (ALM).^5-9 Recent studies showed the critical role of ALM and its interplay with the motor thalamus in preparing sensory-cued rewarded movements, specifically licking.^10-12 Work in primates suggests that the basal ganglia output to the motor thalamus transmits an urgency or vigor signal,^13-15 which leads to shortened reaction times and faster movement initiation. As yet, little is known about what signals are transmitted from the motor thalamus to the cortex during cued movements and how these signals contribute to movement initiation. In the present study, we employed a tactile-cued licking task in mice while monitoring reaction times of the initial lick. We found that inactivation of ALM delayed the initiation of cued licking. Two-photon Ca²⁺ imaging of VM axons revealed that the majority of the axon terminals in ALM were transiently active during licking. Their activity was predictive of the time of the first lick. Chemogenetic and optogenetic manipulation of VM axons in ALM indicated that VM inputs facilitate the initiation of cue-triggered and impulsive licking in trained mice. Our results suggest that VM thalamocortical inputs increase the probability and vigor of initiating planned motor responses.

Local and Global Dynamics of Dendritic Activity in the Pyramidal Neuron

There has been increasing interest in the measurement and comparison of activity across compartments of the pyramidal neuron. Dendritic activity can occur both locally, on a single dendritic segment, or globally, involving multiple compartments of the single neuron. Little is known about how these dendritic dynamics shape and contribute to information processing and behavior. Although it has been difficult to characterize local and global activity in vivo due to the technical challenge of simultaneously recording from the entire dendritic arbor and soma, the rise of calcium imaging has driven the increased feasibility and interest of these experiments. However, the distinction between local and global activity made by calcium imaging requires careful consideration. In this review we describe local and global activity, discuss the difficulties and caveats of this distinction, and present the evidence of local and global activity in information processing and behavior.

Behavioral Approaches to Study Top-Down Influences on Active Listening

The massive network of descending corticofugal projections has been long-recognized by anatomists, but their functional contributions to sound processing and auditory-guided behaviors remain a mystery. Most efforts to characterize the auditory corticofugal system have been inductive; wherein function is inferred from a few studies employing a wide range of methods to manipulate varying limbs of the descending system in a variety of species and preparations. An alternative approach, which we focus on here, is to first establish auditory-guided behaviors that reflect the contribution of top-down influences on auditory perception. To this end, we postulate that auditory corticofugal systems may contribute to active listening behaviors in which the timing of bottom-up sound cues can be predicted from top-down signals arising from cross-modal cues, temporal integration, or self-initiated movements. Here, we describe a behavioral framework for investigating how auditory perceptual performance is enhanced when subjects can anticipate the timing of upcoming target sounds. Our first paradigm, studied both in human subjects and mice, reports species-specific differences in visually cued expectation of sound onset in a signal-in-noise detection task. A second paradigm performed in mice reveals the benefits of temporal regularity as a perceptual grouping cue when detecting repeating target tones in complex background noise. A final behavioral approach demonstrates significant improvements in frequency discrimination threshold and perceptual sensitivity when auditory targets are presented at a predictable temporal interval following motor self-initiation of the trial. Collectively, these three behavioral approaches identify paradigms to study top-down influences on sound perception that are amenable to head-fixed preparations in genetically tractable animals, where it is possible to monitor and manipulate particular nodes of the descending auditory pathway with unparalleled precision.