Different glutamate receptors convey feedforward and recurrent processing in macaque V1

Impaired conscious access and abnormal attentional amplification in schizophrenia

Previous research suggests that the conscious perception of a masked stimulus is impaired in schizophrenia, while unconscious bottom-up processing of the same stimulus, as assessed by subliminal priming, can be preserved. Here, we test this postulated dissociation between intact bottom-up and impaired top-down processing and evaluate its brain mechanisms using high-density recordings of event-related potentials. Sixteen patients with schizophrenia and sixteen controls were exposed to peripheral digits with various degrees of visibility, under conditions of either focused attention or distraction by another task. In the distraction condition, the brain activity evoked by masked digits was drastically reduced in both groups, but early bottom-up visual activation could still be detected and did not differ between patients and controls. By contrast, under focused top-down attention, a major impairment was observed: in patients, contrary to controls, the late non-linear ignition associated with the P3 component was reduced. Interestingly, the patients showed an essentially normal attentional amplification of the P1 and N2 components. These results suggest that some but not all top-down attentional amplification processes are impaired in schizophrenia, while bottom-up processing seems to be preserved.

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An elevated consciousness threshold is observed in schizophrenia.

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Under unattended conditions, brain activity was similarly reduced in schizophrenic patients and controls.

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Under attended conditions, the late ignition associated with the P3 component is impaired in patients.

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In schizophrenia, top-down attentional amplification is abnormal while bottom-up processing is essentially spared.

"Silent" NMDA Synapses Enhance Motion Sensitivity in a Mature Retinal Circuit

Retinal direction-selective ganglion cells (DSGCs) have the remarkable ability to encode motion over a wide range of contrasts, relying on well-coordinated excitation and inhibition (E/I). E/I is orchestrated by a diverse set of glutamatergic bipolar cells that drive DSGCs directly, as well as indirectly through feedforward GABAergic/cholinergic signals mediated by starburst amacrine cells. Determining how direction-selective responses are generated across varied stimulus conditions requires understanding how glutamate, acetylcholine, and GABA signals are precisely coordinated. Here, we use a combination of paired patch-clamp recordings, serial EM, and large-scale multi-electrode array recordings to show that a single high-sensitivity source of glutamate is processed differentially by starbursts via AMPA receptors and DSGCs via NMDA receptors. We further demonstrate how this novel synaptic arrangement enables DSGCs to encode direction robustly near threshold contrasts. Together, these results reveal a space-efficient synaptic circuit model for direction computations, in which "silent" NMDA receptors play critical roles.

Laminar fMRI and computational theories of brain function

Recently developed methods for functional MRI at the resolution of cortical layers (laminar fMRI) offer a novel window into neurophysiological mechanisms of cortical activity. Beyond physiology, laminar fMRI also offers an unprecedented opportunity to test influential theories of brain function. Specifically, hierarchical Bayesian theories of brain function, such as predictive coding, assign specific computational roles to different cortical layers. Combined with computational models, laminar fMRI offers a unique opportunity to test these proposals noninvasively in humans. This review provides a brief overview of predictive coding and related hierarchical Bayesian theories, summarises their predictions with regard to layered cortical computations, examines how these predictions could be tested by laminar fMRI, and considers methodological challenges. We conclude by discussing the potential of laminar fMRI for clinically useful computational assays of layer-specific information processing.

A Predictive Coding Account of Psychotic Symptoms in Autism Spectrum Disorder

The co-occurrence of psychotic and autism spectrum disorder (ASD) symptoms represents an important clinical challenge. Here we consider this problem in the context of a computational psychiatry approach that has been applied to both conditions-predictive coding. Some symptoms of schizophrenia have been explained in terms of a failure of top-down predictions or an enhanced weighting of bottom-up prediction errors. Likewise, autism has been explained in terms of similar perturbations. We suggest that this theoretical overlap may explain overlapping symptomatology. Experimental evidence highlights meaningful distinctions and consistencies between these disorders. We hypothesize individuals with ASD may experience some degree of delusions without the presence of any additional impairment, but that hallucinations are likely indicative of a distinct process.

The influence of attention and reward on the learning of stimulus-response associations

We can learn new tasks by listening to a teacher, but we can also learn by trial-and-error. Here, we investigate the factors that determine how participants learn new stimulus-response mappings by trial-and-error. Does learning in human observers comply with reinforcement learning theories, which describe how subjects learn from rewards and punishments? If yes, what is the influence of selective attention in the learning process? We developed a novel redundant-relevant learning paradigm to examine the conjoint influence of attention and reward feedback. We found that subjects only learned stimulus-response mappings for attended shapes, even when unattended shapes were equally informative. Reward magnitude also influenced learning, an effect that was stronger for attended than for non-attended shapes and that carried over to a subsequent visual search task. Our results provide insights into how attention and reward jointly determine how we learn. They support the powerful learning rules that capitalize on the conjoint influence of these two factors on neuronal plasticity.

Great Expectations: Is there Evidence for Predictive Coding in Auditory Cortex?

Predictive coding is possibly one of the most influential, comprehensive, and controversial theories of neural function. While proponents praise its explanatory potential, critics object that key tenets of the theory are untested or even untestable. The present article critically examines existing evidence for predictive coding in the auditory modality. Specifically, we identify five key assumptions of the theory and evaluate each in the light of animal, human and modeling studies of auditory pattern processing. For the first two assumptions - that neural responses are shaped by expectations and that these expectations are hierarchically organized - animal and human studies provide compelling evidence. The anticipatory, predictive nature of these expectations also enjoys empirical support, especially from studies on unexpected stimulus omission. However, for the existence of separate error and prediction neurons, a key assumption of the theory, evidence is lacking. More work exists on the proposed oscillatory signatures of predictive coding, and on the relation between attention and precision. However, results on these latter two assumptions are mixed or contradictory. Looking to the future, more collaboration between human and animal studies, aided by model-based analyses will be needed to test specific assumptions and implementations of predictive coding - and, as such, help determine whether this popular grand theory can fulfill its expectations.

Disconnecting Consciousness: Is There a Common Anesthetic End Point?

A quest for a systems-level neuroscientific basis of anesthetic-induced loss and return of consciousness has been in the forefront of research for the past 2 decades. Recent advances toward the discovery of underlying mechanisms have been achieved using experimental electrophysiology, multichannel electroencephalography, magnetoencephalography, and functional magnetic resonance imaging. By the careful dosing of various volatile and IV anesthetic agents to the level of behavioral unresponsiveness, both specific and common changes in functional and effective connectivity across large-scale brain networks have been discovered and interpreted in the context of how the synthesis of neural information might be affected during anesthesia. The results of most investigations to date converge toward the conclusion that a common neural correlate of anesthetic-induced unresponsiveness is a consistent depression or functional disconnection of lateral frontoparietal networks, which are thought to be critical for consciousness of the environment. A reduction in the repertoire of brain states may contribute to the anesthetic disruption of large-scale information integration leading to unconsciousness. In future investigations, a systematic delineation of connectivity changes with multiple anesthetics using the same experimental design, and the same analytical method will be desirable. The critical neural events that account for the transition between responsive and unresponsive states should be assessed at similar anesthetic doses just below and above the loss or return of responsiveness. There will also be a need to identify a robust, sensitive, and reliable measure of information transfer. Ultimately, finding a behavior-independent measure of subjective experience that can track covert cognition in unresponsive subjects and a delineation of causal factors versus correlated events will be essential to understand the neuronal basis of human consciousness and unconsciousness.

Development of Glutamatergic Proteins in Human Visual Cortex across the Lifespan

Traditionally, human primary visual cortex (V1) has been thought to mature within the first few years of life, based on anatomical studies of synapse formation, and establishment of intracortical and intercortical connections. Human vision, however, develops well beyond the first few years. Previously, we found prolonged development of some GABAergic proteins in human V1 (Pinto et al., 2010). Yet as >80% of synapses in V1 are excitatory, it remains unanswered whether the majority of synapses regulating experience-dependent plasticity and receptive field properties develop late, like their inhibitory counterparts. To address this question, we used Western blotting of postmortem tissue from human V1 (12 female, 18 male) covering a range of ages. Then we quantified a set of postsynaptic glutamatergic proteins (PSD-95, GluA2, GluN1, GluN2A, GluN2B), calculated indices for functional pairs that are developmentally regulated (GluA2:GluN1; GluN2A:GluN2B), and determined interindividual variability. We found early loss of GluN1, prolonged development of PSD-95 and GluA2 into late childhood, protracted development of GluN2A until ∼40 years, and dramatic loss of GluN2A in aging. The GluA2:GluN1 index switched at ∼1 year, but the GluN2A:GluN2B index continued to shift until ∼40 year before changing back to GluN2B in aging. We also identified young childhood as a stage of heightened interindividual variability. The changes show that human V1 develops gradually through a series of five orchestrated stages, making it likely that V1 participates in visual development and plasticity across the lifespan.SIGNIFICANCE STATEMENT Anatomical structure of human V1 appears to mature early, but vision changes across the lifespan. This discrepancy has fostered two hypotheses: either other aspects of V1 continue changing, or later changes in visual perception depend on extrastriate areas. Previously, we showed that some GABAergic synaptic proteins change across the lifespan, but most synapses in V1 are excitatory leaving unanswered how they change. So we studied expression of glutamatergic proteins in human V1 to determine their development. Here we report prolonged maturation of glutamatergic proteins, with five stages that map onto life-long changes in human visual perception. Thus, the apparent discrepancy between development of structure and function may be explained by life-long synaptic changes in human V1.

A Predictive Coding Account of Psychotic Symptoms in Autism Spectrum Disorder

The co-occurrence of psychotic and autism spectrum disorder (ASD) symptoms represents an important clinical challenge. Here we consider this problem in the context of a computational psychiatry approach that has been applied to both conditions-predictive coding. Some symptoms of schizophrenia have been explained in terms of a failure of top-down predictions or an enhanced weighting of bottom-up prediction errors. Likewise, autism has been explained in terms of similar perturbations. We suggest that this theoretical overlap may explain overlapping symptomatology. Experimental evidence highlights meaningful distinctions and consistencies between these disorders. We hypothesize individuals with ASD may experience some degree of delusions without the presence of any additional impairment, but that hallucinations are likely indicative of a distinct process.

Modeling the Time-Course of Responses for the Border Ownership Selectivity Based on the Integration of Feedforward Signals and Visual Cortical Interactions

Border ownership (BO) indicates which side of a contour owns a border, and it plays a fundamental role in figure-ground segregation. The majority of neurons in V2 and V4 areas of monkeys exhibit BO selectivity. A physiological work reported that the responses of BO-selective cells show a rapid transition when a presented square is flipped along its classical receptive field (CRF) so that the opposite BO is presented, whereas the transition is significantly slower when a square with a clear BO is replaced by an ambiguous edge, e.g., when the square is enlarged greatly. The rapid transition seemed to reflect the influence of feedforward processing on BO selectivity. Herein, we investigated the role of feedforward signals and cortical interactions for time-courses in BO-selective cells by modeling a visual cortical network comprising V1, V2, and posterior parietal (PP) modules. In our computational model, the recurrent pathways among these modules gradually established the visual progress and the BO assignments. Feedforward inputs mainly determined the activities of these modules. Surrounding suppression/facilitation of early-level areas modulates the activities of V2 cells to provide BO signals. Weak feedback signals from the PP module enhanced the contrast gain extracted in V1, which underlies the attentional modulation of BO signals. Model simulations exhibited time-courses depending on the BO ambiguity, which were caused by the integration delay of V1 and V2 cells and the local inhibition therein given the difference in input stimulus. However, our model did not fully explain the characteristics of crucially slow transition: the responses of BO-selective physiological cells indicated the persistent activation several times longer than that of our model after the replacement with the ambiguous edge. Furthermore, the time-course of BO-selective model cells replicated the attentional modulation of response time in human psychophysical experiments. These attentional modulations for time-courses were induced by selective enhancement of early-level features due to interactions between V1 and PP. Our proposed model suggests fundamental roles of surrounding suppression/facilitation based on feedforward inputs as well as the interactions between early and parietal visual areas with respect to the ambiguity dependence of the neural dynamics in intermediate-level vision.

The use of in vivo, ex vivo, in vitro, computational models and volunteer studies in vision research and therapy, and their contribution to the Three Rs

Much is known about mammalian vision, and considerable progress has been achieved in treating many vision disorders, especially those due to changes in the eye, by using various therapeutic methods, including stem cell and gene therapy. While cells and tissues from the main parts of the eye and the visual cortex (VC) can be maintained in culture, and many computer models exist, the current non-animal approaches are severely limiting in the study of visual perception and retinotopic imaging. Some of the early studies with cats and non-human primates (NHPs) are controversial for animal welfare reasons and are of questionable clinical relevance, particularly with respect to the treatment of amblyopia. More recently, the UK Home Office records have shown that attention is now more focused on rodents, especially the mouse. This is likely to be due to the perceived need for genetically-altered animals, rather than to knowledge of the similarities and differences of vision in cats, NHPs and rodents, and the fact that the same techniques can be used for all of the species. We discuss the advantages and limitations of animal and non-animal methods for vision research, and assess their relative contributions to basic knowledge and clinical practice, as well as outlining the opportunities they offer for implementing the principles of the Three Rs (Replacement, Reduction and Refinement).

The laminar integration of sensory inputs with feedback signals in human cortex

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Understanding how the cortex integrates feedback and feedforward signals is central to understanding brain function.

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The data-driven framework of apical amplification which is hypothesized to have a central role in cognition is highlighted.

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Human neuroimaging data provides evidence for layer-specific cortical feedback relevant for theories of predictive feedback.

The cortex constitutes the largest area of the human brain. Yet we have only a basic understanding of how the cortex performs one vital function: the integration of sensory signals (carried by feedforward pathways) with internal representations (carried by feedback pathways). A multi-scale, multi-species approach is essential for understanding the site of integration, computational mechanism and functional role of this processing. To improve our knowledge we must rely on brain imaging with improved spatial and temporal resolution and paradigms which can measure internal processes in the human brain, and on the bridging of disciplines in order to characterize this processing at cellular and circuit levels. We highlight apical amplification as one potential mechanism for integrating feedforward and feedback inputs within pyramidal neurons in the rodent brain. We reflect on the challenges and progress in applying this model neuronal process to the study of human cognition. We conclude that cortical-layer specific measures in humans will be an essential contribution for better understanding the landscape of information in cortical feedback, helping to bridge the explanatory gap.

Cerebral responses to local and global auditory novelty under general anesthesia

Primate brains can detect a variety of unexpected deviations in auditory sequences. The local-global paradigm dissociates two hierarchical levels of auditory predictive coding by examining the brain responses to first-order (local) and second-order (global) sequence violations. Using the macaque model, we previously demonstrated that, in the awake state, local violations cause focal auditory responses while global violations activate a brain circuit comprising prefrontal, parietal and cingulate cortices. Here we used the same local-global auditory paradigm to clarify the encoding of the hierarchical auditory regularities in anesthetized monkeys and compared their brain responses to those obtained in the awake state as measured with fMRI. Both, propofol, a GABAA-agonist, and ketamine, an NMDA-antagonist, left intact or even enhanced the cortical response to auditory inputs. The local effect vanished during propofol anesthesia and shifted spatially during ketamine anesthesia compared with wakefulness. Under increasing levels of propofol, we observed a progressive disorganization of the global effect in prefrontal, parietal and cingulate cortices and its complete suppression under ketamine anesthesia. Anesthesia also suppressed thalamic activations to the global effect. These results suggest that anesthesia preserves initial auditory processing, but disturbs both short-term and long-term auditory predictive coding mechanisms. The disorganization of auditory novelty processing under anesthesia relates to a loss of thalamic responses to novelty and to a disruption of higher-order functional cortical networks in parietal, prefrontal and cingular cortices.

Effects of Acute Ketamine Infusion on Visual Working Memory: Event-Related Potentials

BACKGROUND

Working memory (WM) deficits are a core feature of schizophrenia. Electrophysiological studies suggest that impaired early visual processing may contribute to impaired WM in the visual domain. Abnormal N-methyl-D-aspartate (NMDA) receptor function has been implicated both in WM and in early visual processing deficits in schizophrenia. We investigated whether ketamine, a noncompetitive NMDA antagonist, would replicate in healthy volunteers the WM performance and early visual processing abnormalities we and others have reported in patients with schizophrenia.

METHODS

Forty-four healthy volunteers were randomly assigned to receive intravenous ketamine or placebo. During infusion, the effects of ketamine were recorded using standardized psychiatric scales. Visual evoked potentials (P100 and P300 components) were recorded during performance of a delayed matching to sample task.

RESULTS

Ketamine induced mild psychosis-like symptoms and impaired WM performance. It also significantly increased the P100 amplitude, while P300 amplitude decreased in a load-dependent manner. Amplitudes of P100 during retrieval correlated with cognitive performance only in the placebo group.

CONCLUSIONS

We confirmed previous studies showing that ketamine reproduces the impairment of WM performance and smaller P300 amplitudes observed in schizophrenia. However, ketamine increased visual P100 amplitude in contrast to our observation of reduced P100 amplitudes in established schizophrenia. The effects of ketamine on WM and P300 are likely to involve impaired NMDA function, as these receptors are implicated in changes of synaptic strength underlying associative learning and memory. Increased P100 amplitude may reflect the secondary disinhibition of cortical glutamate release that occurs after NMDA blockade.

The NMDA receptor partial agonist d-cycloserine does not enhance motor learning

RATIONALE

There has recently been increasing interest in pharmacological manipulations that could potentially enhance exposure-based cognitive behaviour therapy for anxiety disorders. One such medication is the partial NMDA agonist d-cycloserine. It has been suggested that d-cycloserine enhances cognitive behaviour therapy by making learning faster. While animal studies have supported this view of the drug accelerating learning, evidence in human studies has been mixed. We therefore designed an experiment to measure the effects of d-cycloserine on human motor learning.

METHODS

Fifty-four healthy human volunteers were randomly assigned to a single dose of 250mg d-cycloserine versus placebo in a double-blind design. They then performed a motor sequence learning task.

RESULTS

D-cycloserine did not increase the speed of motor learning or the overall amount learnt. However, we noted that participants on d-cycloserine tended to respond more carefully (shifting towards slower, but more correct responses).

CONCLUSION

The results suggest that d-cycloserine does not exert beneficial effects on psychological treatments via mechanisms involved in motor learning. Further studies are needed to clarify the influence on other cognitive mechanisms.

Texture Segregation Causes Early Figure Enhancement and Later Ground Suppression in Areas V1 and V4 of Visual Cortex

Segregation of images into figures and background is fundamental for visual perception. Cortical neurons respond more strongly to figural image elements than to background elements, but the mechanisms of figure–ground modulation (FGM) are only partially understood. It is unclear whether FGM in early and mid-level visual cortex is caused by an enhanced response to the figure, a suppressed response to the background, or both.

We studied neuronal activity in areas V1 and V4 in monkeys performing a texture segregation task. We compared texture-defined figures with homogeneous textures and found an early enhancement of the figure representation, and a later suppression of the background. Across neurons, the strength of figure enhancement was independent of the strength of background suppression.

We also examined activity in the different V1 layers. Both figure enhancement and ground suppression were strongest in superficial and deep layers and weaker in layer 4. The current–source density profiles suggested that figure enhancement was caused by stronger synaptic inputs in feedback-recipient layers 1, 2, and 5 and ground suppression by weaker inputs in these layers, suggesting an important role for feedback connections from higher level areas. These results provide new insights into the mechanisms for figure–ground organization.

Erythrocytes Are Oxygen-Sensing Regulators of the Cerebral Microcirculation

Energy production in the brain depends almost exclusively on oxidative metabolism. Neurons have small energy reserves and require a continuous supply of oxygen (O2). It is therefore not surprising that one of the hallmarks of normal brain function is the tight coupling between cerebral blood flow and neuronal activity. Since capillaries are embedded in the O2-consuming neuropil, we have here examined whether activity-dependent dips in O2 tension drive capillary hyperemia. In vivo analyses showed that transient dips in tissue O2 tension elicit capillary hyperemia. Ex vivo experiments revealed that red blood cells (RBCs) themselves act as O2 sensors that autonomously regulate their own deformability and thereby flow velocity through capillaries in response to physiological decreases in O2 tension. This observation has broad implications for understanding how local changes in blood flow are coupled to synaptic transmission.

Early Visual Cortex as a Multiscale Cognitive Blackboard

Neurons in early visual cortical areas not only represent incoming visual information but are also engaged by higher level cognitive processes, including attention, working memory, imagery, and decision-making. Are these cognitive effects an epiphenomenon or are they functionally relevant for these mental operations? We review evidence supporting the hypothesis that the modulation of activity in early visual areas has a causal role in cognition. The modulatory influences allow the early visual cortex to act as a multiscale cognitive blackboard for read and write operations by higher visual areas, which can thereby efficiently exchange information. This blackboard architecture explains how the activity of neurons in the early visual cortex contributes to scene segmentation and working memory, and relates to the subject's inferences about the visual world. The architecture also has distinct advantages for the processing of visual routines that rely on a number of sequentially executed processing steps.

Spike synchrony generated by modulatory common input through NMDA-type synapses

Common excitatory input to neurons increases their firing rates and the strength of the spike correlation (synchrony) between them. Little is known, however, about the synchronizing effects of modulatory common input. Here, we show that modulatory common input with the slow synaptic kinetics of N-methyl-d-aspartate (NMDA) receptors enhances firing rates and also produces synchrony. Tight synchrony (correlations on the order of milliseconds) always increases with modulatory strength. Unexpectedly, the relationship between strength of modulation and strength of loose synchrony (tens of milliseconds) is not monotonic: The strongest loose synchrony is obtained for intermediate modulatory amplitudes. This finding explains recent neurophysiological results showing that in cortical areas V1 and V2, presumed modulatory top-down input due to contour grouping increases (loose and tight) synchrony but that additional modulatory input due to top-down attention does not change tight synchrony and actually decreases loose synchrony. These neurophysiological findings are understood from our model of integrate-and-fire neurons under the assumption that contour grouping as well as attention lead to additive modulatory common input through NMDA-type synapses. In contrast, circuits with common projections through model α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors did not exhibit the paradoxical decrease of synchrony with increased input. Our results suggest that NMDA receptors play a critical role in top-down response modulation in the visual cortex.

Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model’s feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison.

From one moment to the next, in an ever-changing world, and awash in a deluge of sensory data, the brain fluidly guides our actions throughout an astonishing variety of tasks. Processing this ongoing bombardment of information is a fundamental problem faced by its underlying neural circuits. Given that the structure of our actions along with the organization of the environment in which they are performed can be intuitively decomposed into sequences of simpler patterns, an encoding strategy reflecting the temporal nature of these patterns should offer an efficient approach for assembling more complex memories and behaviors. We present a model that demonstrates how activity could propagate through recurrent cortical microcircuits as a result of a learning rule based on neurobiologically plausible time courses and dynamics. The model predicts that the interaction between several learning and dynamical processes constitute a compound mnemonic engram that can flexibly generate sequential step-wise increases of activity within neural populations.

Confidence through consensus: a neural mechanism for uncertainty monitoring

Models that integrate sensory evidence to a threshold can explain task accuracy, response times and confidence, yet it is still unclear how confidence is encoded in the brain. Classic models assume that confidence is encoded in some form of balance between the evidence integrated in favor and against the selected option. However, recent experiments that measure the sensory evidence’s influence on choice and confidence contradict these classic models. We propose that the decision is taken by many loosely coupled modules each of which represent a stochastic sample of the sensory evidence integral. Confidence is then encoded in the dispersion between modules. We show that our proposal can account for the well established relations between confidence, and stimuli discriminability and reaction times, as well as the fluctuations influence on choice and confidence.

How a (sub)Cellular Coincidence Detection Mechanism Featuring Layer-5 Pyramidal Cells May Help Produce Various Visual Phenomena

Perceptual phenomena such as spatio-temporal illusions and masking are typically explained by psychological (cognitive) processing theories or large-scale neural theories involving inter-areal connectivity and neural circuits comprising of hundreds or more interconnected single cells. Subcellular mechanisms are hardly used for such purpose. Here, a mechanistic theoretical view is presented on how a subcellular brain mechanism of integration of presynaptic signals that arrive at different compartments of layer-5 pyramidal neurons could explain a couple of spatiotemporal visual-phenomenal effects unfolding along very brief time intervals within the range of the sub-second temporal scale.

The Notch ligand E3 ligase, Mind Bomb1, regulates glutamate receptor localization in Drosophila

The postsynaptic density (PSD) is a protein-rich network important for the localization of postsynaptic glutamate receptors (GluRs) and for signaling downstream of these receptors. Although hundreds of PSD proteins have been identified, many are functionally uncharacterized. We conducted a reverse genetic screen for mutations that affected GluR localization using Drosophila genes that encode homologs of mammalian PSD proteins. 42.8% of the mutants analyzed exhibited a significant change in GluR localization at the third instar larval neuromuscular junction (NMJ), a model synapse that expresses homologs of AMPA receptors. We identified the E3 ubiquitin ligase, Mib1, which promotes Notch signaling, as a regulator of synaptic GluR localization. Mib1 positively regulates the localization of the GluR subunits GluRIIA, GluRIIB, and GluRIIC. Mutations in mib1 and ubiquitous expression of Mib1 that lacks its ubiquitin ligase activity result in the loss of synaptic GluRIIA-containing receptors. In contrast, overexpression of Mib1 in all tissues increases postsynaptic levels of GluRIIA. Cellular levels of Mib1 are also important for the structure of the presynaptic motor neuron. While deficient Mib1 signaling leads to overgrowth of the NMJ, ubiquitous overexpression of Mib1 results in a reduction in the number of presynaptic motor neuron boutons and branches. These synaptic changes may be secondary to attenuated glutamate release from the presynaptic motor neuron in mib1 mutants as mib1 mutants exhibit significant reductions in the vesicle-associated protein cysteine string protein and in the frequency of spontaneous neurotransmission.

Neural Mechanisms of Cortical Motion Computation Based on a Neuromorphic Sensory System

The visual cortex analyzes motion information along hierarchically arranged visual areas that interact through bidirectional interconnections. This work suggests a bio-inspired visual model focusing on the interactions of the cortical areas in which a new mechanism of feedforward and feedback processing are introduced. The model uses a neuromorphic vision sensor (silicon retina) that simulates the spike-generation functionality of the biological retina. Our model takes into account two main model visual areas, namely V1 and MT, with different feature selectivities. The initial motion is estimated in model area V1 using spatiotemporal filters to locally detect the direction of motion. Here, we adapt the filtering scheme originally suggested by Adelson and Bergen to make it consistent with the spike representation of the DVS. The responses of area V1 are weighted and pooled by area MT cells which are selective to different velocities, i.e. direction and speed. Such feature selectivity is here derived from compositions of activities in the spatio-temporal domain and integrating over larger space-time regions (receptive fields). In order to account for the bidirectional coupling of cortical areas we match properties of the feature selectivity in both areas for feedback processing. For such linkage we integrate the responses over different speeds along a particular preferred direction. Normalization of activities is carried out over the spatial as well as the feature domains to balance the activities of individual neurons in model areas V1 and MT. Our model was tested using different stimuli that moved in different directions. The results reveal that the error margin between the estimated motion and synthetic ground truth is decreased in area MT comparing with the initial estimation of area V1. In addition, the modulated V1 cell activations shows an enhancement of the initial motion estimation that is steered by feedback signals from MT cells.

Cognitive functions of intracellular mechanisms for contextual amplification

Evidence for the hypothesis that input to the apical tufts of neocortical pyramidal cells plays a central role in cognition by amplifying their responses to feedforward input is reviewed. Apical tufts are electrically remote from the soma, and their inputs come from diverse sources including direct feedback from higher cortical regions, indirect feedback via the thalamus, and long-range lateral connections both within and between cortical regions. This suggests that input to tuft dendrites may amplify the cell's response to basal inputs that they receive via layer 4 and which have synapses closer to the soma. ERP data supporting this inference is noted. Intracellular studies of apical amplification (AA) and of disamplification by inhibitory interneurons targeted only at tufts are reviewed. Cognitive processes that have been related to them by computational, electrophysiological, and psychopathological studies are then outlined. These processes include: figure-ground segregation and Gestalt grouping; contextual disambiguation in perception and sentence comprehension; priming; winner-take-all competition; attention and working memory; setting the level of consciousness; cognitive control; and learning. It is argued that theories in cognitive neuroscience should not assume that all neurons function as integrate-and-fire point processors, but should use the capabilities of cells with distinct sites of integration for driving and modulatory inputs. Potentially 'unifying' theories that depend upon these capabilities are reviewed. It is concluded that evolution of the primitives of AA and disamplification in neocortex may have extended cognitive capabilities beyond those built from the long-established primitives of excitation, inhibition, and disinhibition.

Spike synchrony reveals emergence of proto-objects in visual cortex

Neurons at early stages of the visual cortex signal elemental features, such as pieces of contour, but how these signals are organized into perceptual objects is unclear. Theories have proposed that spiking synchrony between these neurons encodes how features are grouped (binding-by-synchrony), but recent studies did not find the predicted increase in synchrony with binding. Here we propose that features are grouped to "proto-objects" by intrinsic feedback circuits that enhance the responses of the participating feature neurons. This hypothesis predicts synchrony exclusively between feature neurons that receive feedback from the same grouping circuit. We recorded from neurons in macaque visual cortex and used border-ownership selectivity, an intrinsic property of the neurons, to infer whether or not two neurons are part of the same grouping circuit. We found that binding produced synchrony between same-circuit neurons, but not between other pairs of neurons, as predicted by the grouping hypothesis. In a selective attention task, synchrony emerged with ignored as well as attended objects, and higher synchrony was associated with faster behavioral responses, as would be expected from early grouping mechanisms that provide the structure for object-based processing. Thus, synchrony could be produced by automatic activation of intrinsic grouping circuits. However, the binding-related elevation of synchrony was weak compared with its random fluctuations, arguing against synchrony as a code for binding. In contrast, feedback grouping circuits encode binding by modulating the response strength of related feature neurons. Thus, our results suggest a novel coding mechanism that might underlie the proto-objects of perception.

The Role of Dendritic Signaling in the Anesthetic Suppression of Consciousness

Despite considerable progress in the identification of the molecular targets of general anesthetics, it remains unclear how these drugs affect the brain at the systems level to suppress consciousness. According to recent proposals, anesthetics may achieve this feat by interfering with corticocortical top–down processes, that is, by interrupting information flow from association to early sensory cortices. Such a view entails two immediate questions. First, at which anatomical site, and by virtue of which physiological mechanism, do anesthetics interfere with top–down signals? Second, why does a breakdown of top–down signaling cause unconsciousness? While an answer to the first question can be gleaned from emerging neurophysiological evidence on dendritic signaling in cortical pyramidal neurons, a response to the second is offered by increasingly popular theoretical frameworks that place the element of prediction at the heart of conscious perception.

Combined contributions of feedforward and feedback inputs to bottom-up attention

In order to deal with a large amount of information carried by visual inputs entering the brain at any given point in time, the brain swiftly uses the same inputs to enhance processing in one part of visual field at the expense of the others. These processes, collectively called bottom-up attentional selection, are assumed to solely rely on feedforward processing of the external inputs, as it is implied by the nomenclature. Nevertheless, evidence from recent experimental and modeling studies points to the role of feedback in bottom-up attention. Here, we review behavioral and neural evidence that feedback inputs are important for the formation of signals that could guide attentional selection based on exogenous inputs. Moreover, we review results from a modeling study elucidating mechanisms underlying the emergence of these signals in successive layers of neural populations and how they depend on feedback from higher visual areas. We use these results to interpret and discuss more recent findings that can further unravel feedforward and feedback neural mechanisms underlying bottom-up attention. We argue that while it is descriptively useful to separate feedforward and feedback processes underlying bottom-up attention, these processes cannot be mechanistically separated into two successive stages as they occur at almost the same time and affect neural activity within the same brain areas using similar neural mechanisms. Therefore, understanding the interaction and integration of feedforward and feedback inputs is crucial for better understanding of bottom-up attention.

How attention can create synaptic tags for the learning of working memories in sequential tasks

Intelligence is our ability to learn appropriate responses to new stimuli and situations. Neurons in association cortex are thought to be essential for this ability. During learning these neurons become tuned to relevant features and start to represent them with persistent activity during memory delays. This learning process is not well understood. Here we develop a biologically plausible learning scheme that explains how trial-and-error learning induces neuronal selectivity and working memory representations for task-relevant information. We propose that the response selection stage sends attentional feedback signals to earlier processing levels, forming synaptic tags at those connections responsible for the stimulus-response mapping. Globally released neuromodulators then interact with tagged synapses to determine their plasticity. The resulting learning rule endows neural networks with the capacity to create new working memory representations of task relevant information as persistent activity. It is remarkably generic: it explains how association neurons learn to store task-relevant information for linear as well as non-linear stimulus-response mappings, how they become tuned to category boundaries or analog variables, depending on the task demands, and how they learn to integrate probabilistic evidence for perceptual decisions.

On the functions, mechanisms, and malfunctions of intracortical contextual modulation

A broad neuron-centric conception of contextual modulation is reviewed and re-assessed in the light of recent neurobiological studies of amplification, suppression, and synchronization. Behavioural and computational studies of perceptual and higher cognitive functions that depend on these processes are outlined, and evidence that those functions and their neuronal mechanisms are impaired in schizophrenia is summarized. Finally, we compare and assess the long-term biological functions of contextual modulation at the level of computational theory as formalized by the theories of coherent infomax and free energy reduction. We conclude that those theories, together with the many empirical findings reviewed, show how contextual modulation at the neuronal level enables the cortex to flexibly adapt the use of its knowledge to current circumstances by amplifying and grouping relevant activities and by suppressing irrelevant activities.

Experience-dependent central vision deficits: Neurobiology and visual acuity

Abnormal visual experience during childhood often leads to amblyopia, with strong links to binocular dysfunction that can include poor acuity in both eyes, especially in central vision. In animal models of amblyopia, the non-deprived eye is often considered normal and what limits binocular acuity. This leaves open the question whether monocular deprivation (MD) induces binocular dysfunction similar to what is found in amblyopia. In previous studies of MD cats, we found a loss of excitatory receptors restricted to the central visual field representation in visual cortex (V1), including both eyes' columns. This led us to ask two questions about the effects of MD: how quickly are receptors lost in V1? and is there an impact on binocular acuity? We found that just a few hours of MD caused a rapid loss of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor proteins across all of V1. But after a few days of MD, there was recovery in the visual periphery, leaving a loss of AMPA receptors only in the central region of V1. We reared animals with early MD followed by a long period of binocular vision and found binocular acuity deficits that were greatest in the central visual field. Our results suggest that the greater binocular acuity deficits in the central visual field are driven in part by the long-term loss of AMPA receptors in the central region of V1.

NMDA Receptor Antagonist Ketamine Distorts Object Recognition by Reducing Feedback to Early Visual Cortex

It is a well-established fact that top-down processes influence neural representations in lower-level visual areas. Electrophysiological recordings in monkeys as well as theoretical models suggest that these top-down processes depend on NMDA receptor functioning. However, this underlying neural mechanism has not been tested in humans. We used fMRI multivoxel pattern analysis to compare the neural representations of ambiguous Mooney images before and after they were recognized with their unambiguous grayscale version. Additionally, we administered ketamine, an NMDA receptor antagonist, to interfere with this process. Our results demonstrate that after recognition, the pattern of brain activation elicited by a Mooney image is more similar to that of its easily recognizable grayscale version than to the pattern evoked by the identical Mooney image before recognition. Moreover, recognition of Mooney images decreased mean response; however, neural representations of separate images became more dissimilar. So from the neural perspective, unrecognizable Mooney images all "look the same", whereas recognized Mooneys look different. We observed these effects in posterior fusiform part of lateral occipital cortex and in early visual cortex. Ketamine distorted these effects of recognition, but in early visual cortex only. This suggests that top-down processes from higher- to lower-level visual areas might operate via an NMDA pathway.

Adaptive learning in a compartmental model of visual cortex-how feedback enables stable category learning and refinement

The categorization of real world objects is often reflected in the similarity of their visual appearances. Such categories of objects do not necessarily form disjunct sets of objects, neither semantically nor visually. The relationship between categories can often be described in terms of a hierarchical structure. For instance, tigers and leopards build two separate mammalian categories, both of which are subcategories of the category Felidae. In the last decades, the unsupervised learning of categories of visual input stimuli has been addressed by numerous approaches in machine learning as well as in computational neuroscience. However, the question of what kind of mechanisms might be involved in the process of subcategory learning, or category refinement, remains a topic of active investigation. We propose a recurrent computational network architecture for the unsupervised learning of categorial and subcategorial visual input representations. During learning, the connection strengths of bottom-up weights from input to higher-level category representations are adapted according to the input activity distribution. In a similar manner, top-down weights learn to encode the characteristics of a specific stimulus category. Feedforward and feedback learning in combination realize an associative memory mechanism, enabling the selective top-down propagation of a category's feedback weight distribution. We suggest that the difference between the expected input encoded in the projective field of a category node and the current input pattern controls the amplification of feedforward-driven representations. Large enough differences trigger the recruitment of new representational resources and the establishment of additional (sub-) category representations. We demonstrate the temporal evolution of such learning and show how the proposed combination of an associative memory with a modulatory feedback integration successfully establishes category and subcategory representations.

Temporal dynamics of different cases of bi-stable figure-ground perception

Segmentation of a visual scene in "figure" and "ground" is essential for perception of the three-dimensional layout of a scene. In cases of bi-stable perception, two distinct figure-ground interpretations alternate over time. We were interested in the temporal dynamics of these alternations, in particular when the same image is presented repeatedly, with short blank periods in-between. Surprisingly, we found that the intermittent presentation of Rubin's classical "face-or-vase" figure, which is frequently taken as a standard case of bi-stable figure-ground perception, often evoked perceptual switches during the short presentations and stabilization was not prominent. Interestingly, bi-stable perception of Kanizsa's anomalous transparency figure did strongly stabilize across blanks. We also found stabilization for the Necker cube, which we used for comparison. The degree of stabilization (and the lack of it) varied across stimuli and across individuals. Our results indicate, against common expectation, that the stabilization phenomenon cannot be generally evoked by intermittent presentation. We argue that top-down feedback factors such as familiarity, semantics, expectation, and perceptual bias contribute to the complex processes underlying the temporal dynamics of bi-stable figure-ground perception.

Contributions of cortical feedback to sensory processing in primary visual cortex

Closing the structure-function divide is more challenging in the brain than in any other organ (Lichtman and Denk, 2011). For example, in early visual cortex, feedback projections to V1 can be quantified (e.g., Budd, 1998) but the understanding of feedback function is comparatively rudimentary (Muckli and Petro, 2013). Focusing on the function of feedback, we discuss how textbook descriptions mask the complexity of V1 responses, and how feedback and local activity reflects not only sensory processing but internal brain states.

A role beyond learning for NMDA receptors in reward-based decision-making-a pharmacological study using d-cycloserine

N-methyl-D-aspartate (NMDA) receptors are known to fulfill crucial functions in many forms of learning and plasticity. More recently, biophysical models, however, have suggested an additional role of NMDA receptors in evidence integration for decision-making, going beyond their role in learning. We designed a task to study the role of NMDA receptors in human reward-guided learning and decision-making. Human participants were assigned to receive either 250 mg of the partial NMDA agonist d-cycloserine (n=20) or matching placebo capsules (n=27). Reward-guided learning and decision-making were assessed using a task in which participants had to integrate learnt and explicitly shown value information to maximize their monetary wins and minimize their losses. To tease apart the effects of NMDA on learning and decision-making we used simple learning models. D-cycloserine shifted decision-making towards a more optimal integration of the learnt and the explicitly shown information, in the absence of a direct learning effect. In conclusion, our results reveal a distinct role for NMDA receptors in reward-guided decision-making. We discuss these findings in the context of NMDA's roles in neuronal super-additivity and as crucial for evidence integration for decisions.

Computing with a canonical neural circuits model with pool normalization and modulating feedback

Evidence suggests that the brain uses an operational set of canonical computations like normalization, input filtering, and response gain enhancement via reentrant feedback. Here, we propose a three-stage columnar architecture of cascaded model neurons to describe a core circuit combining signal pathways of feedforward and feedback processing and the inhibitory pooling of neurons to normalize the activity. We present an analytical investigation of such a circuit by first reducing its detail through the lumping of initial feedforward response filtering and reentrant modulating signal amplification. The resulting excitatory-inhibitory pair of neurons is analyzed in a 2D phase-space. The inhibitory pool activation is treated as a separate mechanism exhibiting different effects. We analyze subtractive as well as divisive (shunting) interaction to implement center-surround mechanisms that include normalization effects in the characteristics of real neurons. Different variants of a core model architecture are derived and analyzed--in particular, individual excitatory neurons (without pool inhibition), the interaction with an inhibitory subtractive or divisive (i.e., shunting) pool, and the dynamics of recurrent self-excitation combined with divisive inhibition. The stability and existence properties of these model instances are characterized, which serve as guidelines to adjust these properties through proper model parameterization. The significance of the derived results is demonstrated by theoretical predictions of response behaviors in the case of multiple interacting hypercolumns in a single and in multiple feature dimensions. In numerical simulations, we confirm these predictions and provide some explanations for different neural computational properties. Among those, we consider orientation contrast-dependent response behavior, different forms of attentional modulation, contrast element grouping, and the dynamic adaptation of the silent surround in extraclassical receptive field configurations, using only slight variations of the same core reference model.

Orientation-tuned surround suppression in mouse visual cortex

The firing rates of neurons in primary visual cortex (V1) are suppressed by large stimuli, an effect known as surround suppression. In cats and monkeys, the strength of suppression is sensitive to orientation; responses to regions containing uniform orientations are more suppressed than those containing orientation contrast. This effect is thought to be important for scene segmentation, but the underlying neural mechanisms are poorly understood. We asked whether it is possible to study these mechanisms in the visual cortex of mice, because of recent advances in technology for studying the cortical circuitry in mice. It is unknown whether neurons in mouse V1 are sensitive to orientation contrast. We measured the orientation selectivity of surround suppression in the different layers of mouse V1. We found strong surround suppression in layer 4 and the superficial layers, part of which was orientation tuned: iso-oriented surrounds caused more suppression than cross-oriented surrounds. Surround suppression was delayed relative to the visual response and orientation-tuned suppression was delayed further, suggesting two separate suppressive mechanisms. Previous studies proposed that surround suppression depends on the activity of inhibitory somatostatin-positive interneurons in the superficial layers. To test the involvement of the superficial layers we topically applied lidocaine. Silencing of the superficial layers did not prevent orientation-tuned suppression in layer 4. These results show that neurons in mouse V1, which lacks orientation columns, show orientation-dependent surround suppression in layer 4 and the superficial layers and that surround suppression in layer 4 does not require contributions from neurons in the superficial layers.

Alpha and gamma oscillations characterize feedback and feedforward processing in monkey visual cortex

Cognitive functions rely on the coordinated activity of neurons in many brain regions, but the interactions between cortical areas are not yet well understood. Here we investigated whether low-frequency (α) and high-frequency (γ) oscillations characterize different directions of information flow in monkey visual cortex. We recorded from all layers of the primary visual cortex (V1) and found that γ-waves are initiated in input layer 4 and propagate to the deep and superficial layers of cortex, whereas α-waves propagate in the opposite direction. Simultaneous recordings from V1 and downstream area V4 confirmed that γ- and α-waves propagate in the feedforward and feedback direction, respectively. Microstimulation in V1 elicited γ-oscillations in V4, whereas microstimulation in V4 elicited α-oscillations in V1, thus providing causal evidence for the opposite propagation of these rhythms. Furthermore, blocking NMDA receptors, thought to be involved in feedback processing, suppressed α while boosting γ. These results provide new insights into the relation between brain rhythms and cognition.

Optimal control of transient dynamics in balanced networks supports generation of complex movements

Populations of neurons in motor cortex engage in complex transient dynamics of large amplitude during the execution of limb movements. Traditional network models with stochastically assigned synapses cannot reproduce this behavior. Here we introduce a class of cortical architectures with strong and random excitatory recurrence that is stabilized by intricate, fine-tuned inhibition, optimized from a control theory perspective. Such networks transiently amplify specific activity states and can be used to reliably execute multidimensional movement patterns. Similar to the experimental observations, these transients must be preceded by a steady-state initialization phase from which the network relaxes back into the background state by way of complex internal dynamics. In our networks, excitation and inhibition are as tightly balanced as recently reported in experiments across several brain areas, suggesting inhibitory control of complex excitatory recurrence as a generic organizational principle in cortex.

Hierarchical representation of shapes in visual cortex-from localized features to figural shape segregation

Visual structures in the environment are segmented into image regions and those combined to a representation of surfaces and prototypical objects. Such a perceptual organization is performed by complex neural mechanisms in the visual cortex of primates. Multiple mutually connected areas in the ventral cortical pathway receive visual input and extract local form features that are subsequently grouped into increasingly complex, more meaningful image elements. Such a distributed network of processing must be capable to make accessible highly articulated changes in shape boundary as well as very subtle curvature changes that contribute to the perception of an object. We propose a recurrent computational network architecture that utilizes hierarchical distributed representations of shape features to encode surface and object boundary over different scales of resolution. Our model makes use of neural mechanisms that model the processing capabilities of early and intermediate stages in visual cortex, namely areas V1-V4 and IT. We suggest that multiple specialized component representations interact by feedforward hierarchical processing that is combined with feedback signals driven by representations generated at higher stages. Based on this, global configurational as well as local information is made available to distinguish changes in the object's contour. Once the outline of a shape has been established, contextual contour configurations are used to assign border ownership directions and thus achieve segregation of figure and ground. The model, thus, proposes how separate mechanisms contribute to distributed hierarchical cortical shape representation and combine with processes of figure-ground segregation. Our model is probed with a selection of stimuli to illustrate processing results at different processing stages. We especially highlight how modulatory feedback connections contribute to the processing of visual input at various stages in the processing hierarchy.

Hallucinogen persisting perception disorder and the serotonergic system: a comprehensive review including new MDMA-related clinical cases

Hallucinogen persisting perception disorder (HPPD) is a drug-induced condition associated with inaccurate visual representations. Since the underlying mechanism(s) are largely unknown, this review aims to uncover aspects underlying its etiology. Available evidence on HPPD and drug-related altered visual processing was reviewed and the majority of HPPD cases were attributed to drugs with agonistic effects on serotonergic 5-HT₂A receptors. Moreover, we present 31 new HPPD cases that link HPPD to the use of ecstasy (MDMA), which is known to reverse serotonin reuptake and acts as agonist on 5-HT₂A receptors. The available evidence suggests that HPPD symptoms may be a result from a misbalance of inhibitory-excitatory activity in low-level visual processing and GABA-releasing inhibitory interneurons may be involved. However, high co-morbidities with anxiety, attention problems and derealization symptoms add complexity to the etiology of HPPD. Also, other perceptual disorders that show similarity to HPPD cannot be ruled out in presentations to clinical treatment. Taken together, evidence is still sparse, though low-level visual processing may play an important role. A novel finding of this review study, evidenced by our new cases, is that ecstasy (MDMA) use may also induce symptoms of HPPD.

NMDA spikes enhance action potential generation during sensory input

Recent evidence in vitro suggests that the tuft dendrites of pyramidal neurons are capable of evoking local NMDA receptor-dependent electrogenesis, so-called NMDA spikes. However, it has so far proved difficult to demonstrate their existence in vivo. Moreover, it is not clear whether NMDA spikes are relevant to the output of pyramidal neurons. We found that local NMDA spikes occurred in tuft dendrites of layer 2/3 pyramidal neurons both spontaneously and following sensory input, and had a large influence on the number of output action potentials. Using two-photon activation of an intracellular caged NMDA receptor antagonist (tc-MK801), we found that isolated NMDA spikes typically occurred in multiple branches simultaneously and that sensory stimulation substantially increased their probability. Our results demonstrate that NMDA receptors have a vital role in coupling the tuft region of the layer 2/3 pyramidal neuron to the cell body, enhancing the effectiveness of layer 1 input.