Feedforward, feedback and inhibitory connections in primate visual cortex

Calretinin immunoreactivity in the claustrum of the rat

The claustrum is a telencephalic structure which consists of dorsal segment adjoining the insular cortex and a ventral segment termed also endopiriform nucleus (END). The dorsal segment (claustrum) is divided into a dorsal and ventral zone, while the END is parcellated into dorsal, ventral and intermediate END. The claustrum and the END consist of glutamatergic projection neurons and GABAergic local interneurons coexpressing calcium binding proteins. Among neurons expressing calcium binding proteins the calretinin (CR)-immunoreactive interneurons exert specific functions in neuronal circuits, including disinhibition of excitatory neurons. Previous anatomical data indicate extensive and reciprocally organized claustral projections with cerebral cortex. We asked if the distribution of cells immunoreactive for CR delineates anatomical or functional subdivisions in the claustrum and in the END. Both segments of the claustrum and all subdivisions of the END contained CR immunoreactive neurons with varying distribution. The ventral zone of the claustrum exhibited weak labeling with isolated cell bodies and thin fibers and is devoid of immunoreactive puncta. Within the medial margin of the intermediate END we noted a group of strongly positive neurons. Cells immunoreactive for CR in all subdivisions of the claustrum and END were bipolar, multipolar and oval with smooth, beaded aspiny dendrites. Small number of CR-immunoreactive neurons displayed thin dendrites which enter to adjoining structures. Penetration of dendrites was reciprocal. These results show an inhomogenity over the claustrum and the END in distribution and types of CR immunoreactive neurons. The distribution of the CR-immunoreactive neurons respects the anatomical but not functional zones of the claustral complex.

Lack of functional specialization of neurons in the mouse primary visual cortex that have expressed calretinin

Calretinin is a calcium-binding protein often used as a marker for a subset of inhibitory interneurons in the mammalian neocortex. We studied the labeled cells in offspring from a cross of a Cre-dependent reporter line with the CR-ires-Cre mice, which express Cre-recombinase in the same pattern as calretinin. We found that in the mature visual cortex, only a minority of the cells that have expressed calretinin and Cre-recombinase during their lifetime is GABAergic and only about 20% are immunoreactive for calretinin. The reason behind this is that calretinin is transiently expressed in many cortical pyramidal neurons during development. To determine whether neurons that express or have expressed calretinin share any distinct functional characteristics, we recorded their visual response properties using GCaMP6s calcium imaging. The average orientation selectivity, size tuning, and temporal and spatial frequency tuning of this group of cells, however, match the response profile of the general neuronal population, revealing the lack of functional specialization for the features studied.

Spatiotemporal dynamics underlying object completion in human ventral visual cortex

Natural vision often involves recognizing objects from partial information. Recognition of objects from parts presents a significant challenge for theories of vision because it requires spatial integration and extrapolation from prior knowledge. Here we recorded intracranial field potentials of 113 visually selective electrodes from epilepsy patients in response to whole and partial objects. Responses along the ventral visual stream, particularly the inferior occipital and fusiform gyri, remained selective despite showing only 9%-25% of the object areas. However, these visually selective signals emerged ∼100 ms later for partial versus whole objects. These processing delays were particularly pronounced in higher visual areas within the ventral stream. This latency difference persisted when controlling for changes in contrast, signal amplitude, and the strength of selectivity. These results argue against a purely feedforward explanation of recognition from partial information, and provide spatiotemporal constraints on theories of object recognition that involve recurrent processing.

Hypothesis-driven methods to augment human cognition by optimizing cortical oscillations

Cortical oscillations have been shown to represent fundamental functions of a working brain, e.g., communication, stimulus binding, error monitoring, and inhibition, and are directly linked to behavior. Recent studies intervening with these oscillations have demonstrated effective modulation of both the oscillations and behavior. In this review, we collect evidence in favor of how hypothesis-driven methods can be used to augment cognition by optimizing cortical oscillations. We elaborate their potential usefulness for three target groups: healthy elderly, patients with attention deficit/hyperactivity disorder, and healthy young adults. We discuss the relevance of neuronal oscillations in each group and show how each of them can benefit from the manipulation of functionally-related oscillations. Further, we describe methods for manipulation of neuronal oscillations including direct brain stimulation as well as indirect task alterations. We also discuss practical considerations about the proposed techniques. In conclusion, we propose that insights from neuroscience should guide techniques to augment human cognition, which in turn can provide a better understanding of how the human brain works.

Modality effects in paced serial addition task: differential responses to auditory and visual stimuli

Paced Auditory Serial-Addition Task (PASAT) is a complex task commonly used to examine patients with diffuse brain damage. A visual version of the neuropsychological test (Paced Visual Serial-Addition Task, PVSAT) has also been introduced to clinical practice, and both versions were adapted to be used in neuroimaging, namely functional magnetic resonance imaging (fMRI). The aim of our work was direct comparison of auditory and visual versions of the paced serial addition test (PASAT/PVSAT) in a within-subject and within-session study and description of the commonalities and differences in both activated and deactivated brain regions. Twenty young adult right-handed healthy volunteers participated in the study and underwent whole-brain fMRI examination during PASAT and PVSAT performance. Higher-level statistical analysis was performed to generate group mean activation and deactivation maps for both tasks, their conjunctions and differences across modalities. In PASAT/PVSAT activation conjunction analysis, we confirmed the existence of a modality-independent neural network similar to working memory tasks and to previous PASAT or PVSAT studies. In PASAT/PVSAT deactivation conjunction analysis, we observed a rather symmetrical extensive pattern of deactivated regions, overlapping the default mode network. Significant differences between PASAT and PVSAT were found in the right frontal eye field (FEF) and bilaterally in the striate and extrastriate cortices. Activation in one task and deactivation in the other jointly contributed to significant differences in all occipital and occipitotemporal regions. Both tasks activated right FEF, but activation during PASAT was significantly stronger than during PVSAT. Between-modality differences should be considered when preparing and interpreting neuroimaging experiments.

Role of motor cortex NMDA receptors in learning-dependent synaptic plasticity of behaving mice

The primary motor cortex has an important role in the precise execution of learned motor responses. During motor learning, synaptic efficacy between sensory and primary motor cortical neurons is enhanced, possibly involving long-term potentiation and N-methyl-D-aspartate (NMDA)-specific glutamate receptor function. To investigate whether NMDA receptor in the primary motor cortex can act as a coincidence detector for activity-dependent changes in synaptic strength and associative learning, here we generate mice with deletion of the Grin1 gene, encoding the essential NMDA receptor subunit 1 (GluN1), specifically in the primary motor cortex. The loss of NMDA receptor function impairs primary motor cortex long-term potentiation in vivo. Importantly, it impairs the synaptic efficacy between the primary somatosensory and primary motor cortices and significantly reduces classically conditioned eyeblink responses. Furthermore, compared with wild-type littermates, mice lacking primary motor cortex show slower learning in Skinner-box tasks. Thus, primary motor cortex NMDA receptors are necessary for activity-dependent synaptic strengthening and associative learning.

Motor cortex NMDA receptors have a key role in the acquisition of associative memories. Hasan et al. generate mice lacking NMDA receptor activity in the motor cortex and find that this impairs LTP, strengthening of synapses between somatosensory and motor cortices, and associative learning.

Postsynaptic target specific synaptic dysfunctions in the CA3 area of BACE1 knockout mice

Beta-amyloid precursor protein cleaving enzyme 1 (BACE1), a major neuronal β-secretase critical for the formation of β-amyloid (Aβ) peptide, is considered one of the key therapeutic targets that can prevent the progression of Alzheimer's disease (AD). Although a complete ablation of BACE1 gene prevents Aβ formation, we previously reported that BACE1 knockouts (KOs) display presynaptic deficits, especially at the mossy fiber (MF) to CA3 synapses. Whether the defect is specific to certain inputs or postsynaptic targets in CA3 is unknown. To determine this, we performed whole-cell recording from pyramidal cells (PYR) and the stratum lucidum (SL) interneurons in the CA3, both of which receive excitatory MF terminals with high levels of BACE1 expression. BACE1 KOs displayed an enhancement of paired-pulse facilitation at the MF inputs to CA3 PYRs without changes at the MF inputs to SL interneurons, which suggests postsynaptic target specific regulation. The synaptic dysfunction in CA3 PYRs was not restricted to excitatory synapses, as seen by an increase in the paired-pulse ratio of evoked inhibitory postsynaptic currents from SL to CA3 PYRs. In addition to the changes in evoked synaptic transmission, BACE1 KOs displayed a reduction in the frequency of miniature excitatory and inhibitory postsynaptic currents (mEPSCs and mIPSCs) in CA3 PYRs without alteration in mEPSCs recorded from SL interneurons. This suggests that the impairment may be more global across diverse inputs to CA3 PYRs. Our results indicate that the synaptic dysfunctions seen in BACE1 KOs are specific to the postsynaptic target, the CA3 PYRs, independent of the input type.

Age-related changes to layer 3 pyramidal cells in the rhesus monkey visual cortex

The effects of normal aging on morphologic and electrophysiologic properties of layer 3 pyramidal neurons in rhesus monkey primary visual cortex (V1) were assessed with whole-cell, patch-clamp recordings in in vitro slices. In another cohort of monkeys, the ultrastructure of synapses in the layers 2-3 neuropil of V1 was assessed using electron microscopy. Distal apical dendritic branching complexity was reduced in aged neurons, as was the total spine density, due to specific loss of mushroom spines from the apical tree and of thin spines from the basal tree. There was also an age-related decrease in the numerical density of symmetric and asymmetric synapses. In contrast to these structural changes, intrinsic membrane, action potential (AP), and excitatory and inhibitory synaptic current properties were the same in aged and young neurons. Computational modeling using morphologic reconstructions predicts that reduced dendritic complexity leads to lower attenuation of voltage outward from the soma (e.g., backpropagating APs) in aged neurons. Importantly, none of the variables that changed with age differed in neurons from cognitively impaired versus unimpaired aged monkeys. In summary, there are age-related alterations to the structural properties of V1 neurons, but these are not associated with significant electrophysiologic changes or with cognitive decline.

Deep cortical layers are activated directly by thalamus

The thalamocortical (TC) projection to layer 4 (L4) is thought to be the main route by which sensory organs communicate with cortex. Sensory information is believed to then propagate through the cortical column along the L4→L2/3→L5/6 pathway. Here, we show that sensory-evoked responses of L5/6 neurons in rats derive instead from direct TC synapses. Many L5/6 neurons exhibited sensory-evoked postsynaptic potentials with the same latencies as L4. Paired in vivo recordings from L5/6 neurons and thalamic neurons revealed substantial convergence of direct TC synapses onto diverse types of infragranular neurons, particularly in L5B. Pharmacological inactivation of L4 had no effect on sensory-evoked synaptic input to L5/6 neurons. L4 is thus not an obligatory distribution hub for cortical activity, and thalamus activates two separate, independent "strata" of cortex in parallel.

Monocular inhibition reveals temporal and spatial changes in gene expression in the primary visual cortex of marmoset

We investigated the time course of the expression of several activity-dependent genes evoked by visual inputs in the primary visual cortex (V1) in adult marmosets. In order to examine the rapid time course of activity-dependent gene expression, marmosets were first monocularly inactivated by tetrodotoxin (TTX), kept in darkness for two days, and then exposed to various length of light stimulation. Activity-dependent genes including HTR1B, HTR2A, whose activity-dependency were previously reported by us, and well-known immediate early genes (IEGs), c-FOS, ZIF268, and ARC, were examined by in situ hybridization. Using this system, first, we demonstrated the ocular dominance type of gene expression pattern in V1 under this condition. IEGs were expressed in columnar patterns throughout layers II–VI of all the tested monocular marmosets. Second, we showed the regulation of HTR1B and HTR2A expressions by retinal spontaneous activity, because HTR1B and HTR2A mRNA expressions sustained a certain level regardless of visual stimulation and were inhibited by a blockade of the retinal activity with TTX. Third, IEGs dynamically changed its laminar distribution from half an hour to several hours upon a stimulus onset with the unique time course for each gene. The expression patterns of these genes were different in neurons of each layer as well. These results suggest that the regulation of each neuron in the primary visual cortex of marmosets is subjected to different regulation upon the change of activities from retina. It should be related to a highly differentiated laminar structure of marmoset visual systems, reflecting the functions of the activity-dependent gene expression in marmoset V1.

Trading speed and accuracy by coding time: a coupled-circuit cortical model

Our actions take place in space and time, but despite the role of time in decision theory and the growing acknowledgement that the encoding of time is crucial to behaviour, few studies have considered the interactions between neural codes for objects in space and for elapsed time during perceptual decisions. The speed-accuracy trade-off (SAT) provides a window into spatiotemporal interactions. Our hypothesis is that temporal coding determines the rate at which spatial evidence is integrated, controlling the SAT by gain modulation. Here, we propose that local cortical circuits are inherently suited to the relevant spatial and temporal coding. In simulations of an interval estimation task, we use a generic local-circuit model to encode time by ‘climbing’ activity, seen in cortex during tasks with a timing requirement. The model is a network of simulated pyramidal cells and inhibitory interneurons, connected by conductance synapses. A simple learning rule enables the network to quickly produce new interval estimates, which show signature characteristics of estimates by experimental subjects. Analysis of network dynamics formally characterizes this generic, local-circuit timing mechanism. In simulations of a perceptual decision task, we couple two such networks. Network function is determined only by spatial selectivity and NMDA receptor conductance strength; all other parameters are identical. To trade speed and accuracy, the timing network simply learns longer or shorter intervals, driving the rate of downstream decision processing by spatially non-selective input, an established form of gain modulation. Like the timing network's interval estimates, decision times show signature characteristics of those by experimental subjects. Overall, we propose, demonstrate and analyse a generic mechanism for timing, a generic mechanism for modulation of decision processing by temporal codes, and we make predictions for experimental verification.

Studies in neuroscience have characterized how the brain represents objects in space and how these objects are selected for detailed perceptual processing. The selection process entails a decision about which object is favoured by the available evidence over time. This period of time is typically in the range of hundreds of milliseconds and is widely believed to be crucial for decisions, allowing neurons to filter noise in the evidence. Despite the widespread belief that time plays this role in decisions and the growing recognition that the brain estimates elapsed time during perceptual tasks, few studies have considered how the encoding of time effects decision making. We propose that neurons encode time in this range by the same general mechanisms used to select objects for detailed processing, and that these temporal representations determine how long evidence is filtered. To this end, we simulate a perceptual decision by coupling two instances of a neural network widely used to simulate localized regions of the cerebral cortex. One network encodes the passage of time and the other makes decisions based on noisy evidence. The former influences the performance of the latter, reproducing signature characteristics of temporal estimates and perceptual decisions.

A cellular mechanism for cortical associations: an organizing principle for the cerebral cortex

A basic feature of intelligent systems such as the cerebral cortex is the ability to freely associate aspects of perceived experience with an internal representation of the world and make predictions about the future. Here, a hypothesis is presented that the extraordinary performance of the cortex derives from an associative mechanism built in at the cellular level to the basic cortical neuronal unit: the pyramidal cell. The mechanism is robustly triggered by coincident input to opposite poles of the neuron, is exquisitely matched to the large- and fine-scale architecture of the cortex, and is tightly controlled by local microcircuits of inhibitory neurons targeting subcellular compartments. This article explores the experimental evidence and the implications for how the cortex operates.

Layer-specific entrainment of γ-band neural activity by the α rhythm in monkey visual cortex

Although the mammalian neocortex has a clear laminar organization, layer-specific neuronal computations remain to be uncovered. Several studies suggest that gamma band activity in primary visual cortex (V1) is produced in granular and superficial layers and is associated with the processing of visual input. Oscillatory alpha band activity in deeper layers has been proposed to modulate neuronal excitability associated with changes in arousal and cognitive factors. To investigate the layer-specific interplay between these two phenomena, we characterized the coupling between alpha and gamma band activity of the local field potential in V1 of the awake macaque. Using multicontact laminar electrodes to measure spontaneous signals simultaneously from all layers of V1, we found a robust coupling between alpha phase in the deeper layers and gamma amplitude in granular and superficial layers. Moreover, the power in the two frequency bands was anticorrelated. Taken together, these findings demonstrate robust interlaminar cross-frequency coupling in the visual cortex, supporting the view that neuronal activity in the alpha frequency range phasically modulates processing in the cortical microcircuit in a top-down manner.

Parvalbumin-producing cortical interneurons receive inhibitory inputs on proximal portions and cortical excitatory inputs on distal dendrites

To examine inputs to parvalbumin (PV)-producing interneurons, we generated transgenic mice expressing somatodendritic membrane-targeted green fluorescent protein specifically in the interneurons, and completely visualized their dendrites and somata. Using immunolabeling for vesicular glutamate transporter (VGluT)1, VGluT2, and vesicular GABA transporter, we found that VGluT1-positive terminals made contacts 4- and 3.1-fold more frequently with PV-producing interneurons than VGluT2-positive and GABAergic terminals, respectively, in the primary somatosensory cortex. Even in layer 4, where VGluT2-positive terminals were most densely distributed, VGluT1-positive inputs to PV-producing interneurons were 2.4-fold more frequent than VGluT2-positive inputs. Furthermore, although GABAergic inputs to PV-producing interneurons were as numerous as VGluT2-positive inputs in most cortical layers, GABAergic inputs clearly preferred the proximal dendrites and somata of the interneurons, indicating that the sites of GABAergic inputs were more optimized than those of VGluT2-positive inputs. Simulation analysis with a PV-producing interneuron model compatible with the present morphological data revealed a plausible reason for this observation, by showing that GABAergic and glutamatergic postsynaptic potentials evoked by inputs to distal dendrites were attenuated to 60 and 87%, respectively, of those evoked by somatic inputs. As VGluT1-positive and VGluT2-positive axon terminals were presumed to be cortical and thalamic glutamatergic inputs, respectively, cortical excitatory inputs to PV-producing interneurons outnumbered the thalamic excitatory and intrinsic inhibitory inputs more than two-fold in any cortical layer. Although thalamic inputs are known to evoke about two-fold larger unitary excitatory postsynaptic potentials than cortical ones, the present results suggest that cortical inputs control PV-producing interneurons at least as strongly as thalamic inputs.

Cortical GABAergic interneurons in cross-modal plasticity following early blindness

Early loss of a given sensory input in mammals causes anatomical and functional modifications in the brain via a process called cross-modal plasticity. In the past four decades, several animal models have illuminated our understanding of the biological substrates involved in cross-modal plasticity. Progressively, studies are now starting to emphasise on cell-specific mechanisms that may be responsible for this intermodal sensory plasticity. Inhibitory interneurons expressing γ-aminobutyric acid (GABA) play an important role in maintaining the appropriate dynamic range of cortical excitation, in critical periods of developmental plasticity, in receptive field refinement, and in treatment of sensory information reaching the cerebral cortex. The diverse interneuron population is very sensitive to sensory experience during development. GABAergic neurons are therefore well suited to act as a gate for mediating cross-modal plasticity. This paper attempts to highlight the links between early sensory deprivation, cortical GABAergic interneuron alterations, and cross-modal plasticity, discuss its implications, and further provide insights for future research in the field.

Layer-specific fMRI reflects different neuronal computations at different depths in human V1

Recent work has established that cerebral blood flow is regulated at a spatial scale that can be resolved by high field fMRI to show cortical columns in humans. While cortical columns represent a cluster of neurons with similar response properties (spanning from the pial surface to the white matter), important information regarding neuronal interactions and computational processes is also contained within a single column, distributed across the six cortical lamina. A basic understanding of underlying neuronal circuitry or computations may be revealed through investigations of the distribution of neural responses at different cortical depths. In this study, we used T₂-weighted imaging with 0.7 mm (isotropic) resolution to measure fMRI responses at different depths in the gray matter while human subjects observed images with either recognizable or scrambled (physically impossible) objects. Intact and scrambled images were partially occluded, resulting in clusters of activity distributed across primary visual cortex. A subset of the identified clusters of voxels showed a preference for scrambled objects over intact; in these clusters, the fMRI response in middle layers was stronger during the presentation of scrambled objects than during the presentation of intact objects. A second experiment, using stimuli targeted at either the magnocellular or the parvocellular visual pathway, shows that laminar profiles in response to parvocellular-targeted stimuli peak in more superficial layers. These findings provide new evidence for the differential sensitivity of high-field fMRI to modulations of the neural responses at different cortical depths.

Depression-biased reverse plasticity rule is required for stable learning at top-down connections

Top-down synapses are ubiquitous throughout neocortex and play a central role in cognition, yet little is known about their development and specificity. During sensory experience, lower neocortical areas are activated before higher ones, causing top-down synapses to experience a preponderance of post-synaptic activity preceding pre-synaptic activity. This timing pattern is the opposite of that experienced by bottom-up synapses, which suggests that different versions of spike-timing dependent synaptic plasticity (STDP) rules may be required at top-down synapses. We consider a two-layer neural network model and investigate which STDP rules can lead to a distribution of top-down synaptic weights that is stable, diverse and avoids strong loops. We introduce a temporally reversed rule (rSTDP) where top-down synapses are potentiated if post-synaptic activity precedes pre-synaptic activity. Combining analytical work and integrate-and-fire simulations, we show that only depression-biased rSTDP (and not classical STDP) produces stable and diverse top-down weights. The conclusions did not change upon addition of homeostatic mechanisms, multiplicative STDP rules or weak external input to the top neurons. Our prediction for rSTDP at top-down synapses, which are distally located, is supported by recent neurophysiological evidence showing the existence of temporally reversed STDP in synapses that are distal to the post-synaptic cell body.

The complex circuitry in the cerebral cortex is characterized by bottom-up connections, which carry feedforward information from the sensory periphery to higher areas, and top-down connections, where the information flow is reversed. Changes over time in the strength of synaptic connections between neurons underlie development, learning and memory. A fundamental mechanism to change synaptic strength is spike timing dependent plasticity, whereby synapses are strengthened whenever pre-synaptic spikes shortly precede post-synaptic spikes and are weakened otherwise; the relative timing of spikes therefore dictates the direction of plasticity. Spike timing dependent plasticity has been observed in multiple species and different brain areas. Here, we argue that top-down connections obey a learning rule with a reversed temporal dependence, which we call reverse spike timing dependent plasticity. We use mathematical analysis and computational simulations to show that this reverse time learning rule, and not previous learning rules, leads to a biologically plausible connectivity pattern with stable synaptic strengths. This reverse time learning rule is supported by recent neuroanatomical and neurophysiological experiments and can explain empirical observations about the development and function of top-down synapses in the brain.

Developmental neuroplasticity after cochlear implantation

Cortical development is dependent on stimulus-driven learning. The absence of sensory input from birth, as occurs in congenital deafness, affects normal growth and connectivity needed to form a functional sensory system, resulting in deficits in oral language learning. Cochlear implants bypass cochlear damage by directly stimulating the auditory nerve and brain, making it possible to avoid many of the deleterious effects of sensory deprivation. Congenitally deaf animals and children who receive implants provide a platform to examine the characteristics of cortical plasticity in the auditory system. In this review, we discuss the existence of time limits for, and mechanistic constraints on, sensitive periods for cochlear implantation and describe the effects of multimodal and cognitive reorganization that result from long-term auditory deprivation.

High-field FMRI for human applications: an overview of spatial resolution and signal specificity

In the last decade, dozens of 7 Tesla scanners have been purchased or installed around the world, while 3 Tesla systems have become a standard. This increased interest in higher field strengths is driven by a demonstrated advantage of high fields for available signal-to-noise ratio (SNR) in the magnetic resonance signal. Functional imaging studies have additional advantages of increases in both the contrast and the spatial specificity of the susceptibility based BOLD signal. One use of this resultant increase in the contrast to noise ratio (CNR) for functional MRI studies at high field is increased image resolution. However, there are many factors to consider in predicting exactly what kind of resolution gains might be made at high fields, and what the opportunity costs might be. The first part of this article discusses both hardware and image quality considerations for higher resolution functional imaging. The second part draws distinctions between image resolution, spatial specificity, and functional specificity of the fMRI signals that can be acquired at high fields, suggesting practical limitations for attainable resolutions of fMRI experiments at a given field, given the current state of the art in imaging techniques. Finally, practical resolution limitations and pulse sequence options for studies in human subjects are considered.

Infragranular sources of sustained local field potential responses in macaque primary visual cortex

A local field potential (LFP) response can be measured throughout the visual cortex in response to the abrupt appearance of a visual stimulus. Averaging LFP responses to many stimulus presentations isolates transient, phase-locked components of the response that are consistent from trial to trial. However, stimulus responses are also composed of sustained components, which differ in their phase from trial to trial and therefore must be evaluated using other methods, such as computing the power of the response of each trial before averaging. Here, we investigate the basis of phase-locked and non-phase-locked LFP responses in the primary visual cortex of the macaque monkey using a novel variant of current source density (CSD) analysis. We applied a linear array of electrode contacts spanning the thickness of the cortex to measure the LFP and compute band-limited CSD power to identify the laminar sites of persistent current exchange that may be the basis of sustained visual LFP responses. In agreement with previous studies, we found a short-latency phase-locked current sink, thought to correspond to thalamocortical input to layer 4C. In addition, we found a prominent non-phase-locked component of the CSD that persisted as long as the stimulus was physically present. The latter was relatively broadband, lasted throughout the stimulus presentation, and was centered ∼500 μm deeper than the initial current sink. These findings demonstrate a fundamental difference in the neural mechanisms underlying the initial and sustained processing of simple visual stimuli in the V1 microcircuit.

Synaptic properties of connections between the primary and secondary auditory cortices in mice

Little is known regarding the synaptic properties of corticocortical connections from one cortical area to another. To expand on this knowledge, we assessed the synaptic properties of excitatory projections from the primary to secondary auditory cortex and vice versa. We identified 2 types of postsynaptic responses. The first class of responses have larger initial excitatory postsynaptic potentials (EPSPs), exhibit paired-pulse depression, are limited to ionotropic glutamate receptor activation, and have larger synaptic terminals; the second has smaller initial EPSPs, paired-pulse facilitation, metabotropic glutamate receptor activation, and smaller synaptic terminals. These responses are similar to the driver and modulator properties previously identified for thalamic and thalamocortical circuitry, suggesting that the same classification may extend to corticocortical inputs and have an implication for the functional organization of corticocortical circuits.

Hierarchical temporal processing deficit model of reality distortion and psychoses

We posit in this article that hierarchical temporal processing deficit is the underlying basis of reality distortion and psychoses. Schizophrenia is a prototypical reality distortion disorder in which the patient manifests with auditory hallucinations, delusions, disorganized speech and thinking, cognitive impairment, avolition and social and occupational dysfunction. Reality distortion can be present in many other disorders including bipolar disorder, major depression and even dementia. Conceptually, schizophrenia is a heterogeneous entity likely to be because of numerous causes similar to dementia. Although no single symptom or set of symptoms is pathognomonic, a cardinal feature in all patients with schizophrenia is chronic distortion of reality. The model that we have proposed accounts for the varied manifestations of reality distortion including hallucinations and delusions. In this paper we consider the implications of this model for the underlying biology of psychoses and also for the neurobiology of schizophrenia and suggest potential targets to consider for the etiology and pathophysiology of reality distortion, especially in the context of schizophrenia.

Altered expression of parvalbumin and calbindin in interneurons within the primary visual cortex of neonatal enucleated hamsters

In the present study, we tested the hypothesis that the expression of calcium binding proteins (CaBPs), parvalbumin (PV), calretinin (CR) and calbindin (CB), is dependent upon sensory experience as emphasized in visual deprivation and deafferentation studies. The expression of CaBPs was studied in interneurons within the primary and extrastriate visual cortices (V1, V2M, V2L) and auditory cortex (AC) of adult hamsters enucleated at birth. The effects of enucleation were mainly confined to area V1 where there was a significant volume reduction (26%) and changes in the laminar distribution of PV and CB immunoreactive (IR) cells. The density of PV-IR cell bodies was significantly increased in layer IV and reduced in layer V. Moreover, the density of CB-IR neurons was inferior in layer V of V1 in enucleated hamsters (EH) compared to controls. These results suggest that some features of the laminar distribution of specific CaBPs, in primary sensory cortices, are dependent upon or modulated by sensory input.

How cortical neurons help us see: visual recognition in the human brain

Through a series of complex transformations, the pixel-like input to the retina is converted into rich visual perceptions that constitute an integral part of visual recognition. Multiple visual problems arise due to damage or developmental abnormalities in the cortex of the brain. Here, we provide an overview of how visual information is processed along the ventral visual cortex in the human brain. We discuss how neurophysiological recordings in macaque monkeys and in humans can help us understand the computations performed by visual cortex.

Early transformations in odor representation

Sensory representations are repeatedly transformed by neural computations that determine which of their attributes can be effectively processed at each stage. Whereas some early computations are common across multiple sensory systems, they can utilize dissimilar underlying mechanisms depending on the properties of each modality. Recent work in the olfactory bulb has substantially clarified the neural algorithms underlying early odor processing. The high-dimensionality of odor space strictly limits the utility of topographical representations, forcing similarity-dependent computations such as decorrelation to employ unusual neural algorithms. The distinct architectures and properties of the two prominent computational layers in the olfactory bulb suggest that the bulb is directly comparable not only to the retina but also to primary visual cortex.

Differential modulatory influences between primary auditory cortex and the anterior auditory field

Neuroanatomical studies have revealed a vast network of corticocortical connections among the various fields that form cat auditory cortex. However, few studies have explored the functional communicative properties of these connections. The purpose of the present study was to examine the bidirectional processing contributions between the primary auditory cortex (A1) and the nonprimary anterior auditory field (AAF). Using acute recording techniques, multiunit neuronal activity was collected from the right hemisphere of nine mature cats. Cortical maps were generated, and the precise location of A1 and AAF was identified. Subsequently, the synaptic activity of A1 or AAF was suppressed with reversible thermal deactivation procedures while the neuronal response to tonal stimuli of the non-inactivated area (A1 or AAF) was measured. We examined response strength and latency, characteristic frequency, bandwidth, and neuronal threshold of A1 and AAF receptive fields before and during epochs of deactivation. Three major changes in A1 response properties were observed during AAF neuronal suppression: a decrease in response strength, an increase in neuronal thresholds, and a sharpening of receptive field bandwidths. In contrast, A1 deactivation did not produce any discernible changes in AAF neuronal responses. Collectively, these results suggest that the modulation of acoustic information between A1 and AAF in cat auditory cortex is dominated by a unidirectional AAF to A1 pathway.

Motif distribution, dynamical properties, and computational performance of two data-based cortical microcircuit templates

The neocortex is a continuous sheet composed of rather stereotypical local microcircuits that consist of neurons on several laminae with characteristic synaptic connectivity patterns. An understanding of the structure and computational function of these cortical microcircuits may hold the key for understanding the enormous computational power of the neocortex. Two templates for the structure of laminar cortical microcircuits have recently been published by Thomson et al. and Binzegger et al., both resulting from long-lasting experimental studies (but based on different methods). We analyze and compare in this article the structure of these two microcircuit templates. In particular, we examine the distribution of network motifs, i.e. of subcircuits consisting of a small number of neurons. The distribution of these building blocks has recently emerged as a method for characterizing similarities and differences among complex networks. We show that the two microcircuit templates have quite different distributions of network motifs, although they both have a characteristic small-world property. In order to understand the dynamical and computational properties of these two microcircuit templates, we have generated computer models of them, consisting of Hodgkin-Huxley point neurons with conductance based synapses that have a biologically realistic short-term plasticity. The performance of these two cortical microcircuit models was studied for seven generic computational tasks that require accumulation and merging of information contained in two afferent spike inputs. Although the two models exhibit a different performance for some of these tasks, their average computational performance is very similar. When we changed the connectivity structure of these two microcircuit models in order to see which aspects of it are essential for computational performance, we found that the distribution of degrees of nodes is a common key factor for their computational performance. We also show that their computational performance is correlated with specific statistical properties of the circuit dynamics that is induced by a particular distribution of degrees of nodes.

Timing, timing, timing: fast decoding of object information from intracranial field potentials in human visual cortex

The difficulty of visual recognition stems from the need to achieve high selectivity while maintaining robustness to object transformations within hundreds of milliseconds. Theories of visual recognition differ in whether the neuronal circuits invoke recurrent feedback connections or not. The timing of neurophysiological responses in visual cortex plays a key role in distinguishing between bottom-up and top-down theories. Here, we quantified at millisecond resolution the amount of visual information conveyed by intracranial field potentials from 912 electrodes in 11 human subjects. We could decode object category information from human visual cortex in single trials as early as 100 ms poststimulus. Decoding performance was robust to depth rotation and scale changes. The results suggest that physiological activity in the temporal lobe can account for key properties of visual recognition. The fast decoding in single trials is compatible with feedforward theories and provides strong constraints for computational models of human vision.

Metabolic and hemodynamic events after changes in neuronal activity: current hypotheses, theoretical predictions and in vivo NMR experimental findings

Unraveling the energy metabolism and the hemodynamic outcomes of excitatory and inhibitory neuronal activity is critical not only for our basic understanding of overall brain function, but also for the understanding of many brain disorders. Methodologies of magnetic resonance spectroscopy (MRS) and magnetic resonance imaging (MRI) are powerful tools for the noninvasive investigation of brain metabolism and physiology. However, the temporal and spatial resolution of in vivo MRS and MRI is not suitable to provide direct evidence for hypotheses that involve metabolic compartmentalization between different cell types, or to untangle the complex neuronal microcircuitry, which results in changes of electrical activity. This review aims at describing how the current models of brain metabolism, mainly built on the basis of in vitro evidence, relate to experimental findings recently obtained in vivo by (1)H MRS, (13)C MRS, and MRI. The hypotheses related to the role of different metabolic substrates, the metabolic neuron-glia interactions, along with the available theoretical predictions of the energy budget of neurotransmission will be discussed. In addition, the cellular and network mechanisms that characterize different types of increased and suppressed neuronal activity will be considered within the sensitivity-constraints of MRS and MRI.

Modeling the top-down influences on the lateral interactions in the visual cortex

Attention modulates the amount of excitatory and inhibitory lateral interactions in the visual cortex. A recurrent neural network is proposed to account for modulatory influence of top-down signals. In the model, two types of inhibitions are distinguished: dendritic and lateral inhibitions. Dendritic inhibition regulates the amount of impact that surrounding cells may exert on a target cell via the dendrites of excitatory neurons and the dendrites of subpopulation of inhibitory neurons mediating lateral inhibition. Attention increases the amount of dendritic inhibition and prevents contextual interactions, while it has no effect on the target cell when there is no surround input. Computer simulations showed that the proposed model is able to exhibit properties of attentional gating. In the condition of focused attention, neural activity in the presence of surrounding stimuli is restored to the level as when the target stimulus is presented alone. Moreover, the model is able to show contrast gain and response gain on the contrast sensitivity function depending on the strength of the dendritic inhibition.

Active dendritic conductances dynamically regulate GABA release from thalamic interneurons

Inhibitory interneurons in the dorsal lateral geniculate nucleus (dLGN) process visual information by precisely controlling spike timing and by refining the receptive fields of thalamocortical (TC) neurons. Previous studies indicate that dLGN interneurons inhibit TC neurons by releasing GABA from both axons and dendrites. However, the mechanisms controlling GABA release are poorly understood. Here, using simultaneous whole-cell recordings from interneurons and TC neurons and two-photon calcium imaging, we find that synchronous activation of multiple retinal ganglion cells (RGCs) triggers sodium spikes that propagate throughout interneuron axons and dendrites, and calcium spikes that invade dendrites but not axons. These distinct modes of interneuron firing can trigger both a rapid and a sustained component of inhibition onto TC neurons. Our studies suggest that active conductances make LGN interneurons flexible circuit-elements that can shift their spatial and temporal properties of GABA release in response to coincident activation of functionally related subsets of RGCs.

Spectrotemporal processing differences between auditory cortical fast-spiking and regular-spiking neurons

Excitatory pyramidal neurons and inhibitory interneurons constitute the main elements of cortical circuitry and have distinctive morphologic and electrophysiological properties. Here, we differentiate them by analyzing the time course of their action potentials (APs) and characterizing their receptive field properties in auditory cortex. Pyramidal neurons have longer APs and discharge as regular-spiking units (RSUs), whereas basket and chandelier cells, which are inhibitory interneurons, have shorter APs and are fast-spiking units (FSUs). To compare these neuronal classes, we stimulated cat primary auditory cortex neurons with a dynamic moving ripple stimulus and constructed single-unit spectrotemporal receptive fields (STRFs) and their associated nonlinearities. FSUs had shorter latencies, broader spectral tuning, greater stimulus specificity, and higher temporal precision than RSUs. The STRF structure of FSUs was more separable, suggesting more independence between spectral and temporal processing regimens. The nonlinearities associated with the two cell classes were indicative of higher feature selectivity for FSUs. These global functional differences between RSUs and FSUs suggest fundamental distinctions between putative excitatory and inhibitory interneurons that shape auditory cortical processing.

Organizational principles of human visual cortex revealed by receptor mapping

This receptorarchitectonic study of the human visual cortex investigated interareal differences in mean receptor concentrations and laminar distribution patterns of 16 neurotransmitter receptors in the dorsal and ventral parts of areas V1, V2, V3 as well as in adjoining areas V4 (ventrally) and V3A (dorsally). Both the functional hierarchy of these areas and a distinction between dorsal and ventral visual cortices were reflected by significant receptorarchitectonic differences. The observation that dorso-ventral differences existed in all extrastriate areas (including V2) is particularly important for the discussion about the relationship between dorsal and ventral V3 as it indicates that a receptorarchitectonic distinction between the ventral and dorsal visual cortices is present in but not specific to V3. This molecular specificity is mirrored by previously reported differences in retinal microstructure and functional differences as revealed in behavioral experiments demonstrating differential advantages for stimulus processing in the upper and lower visual fields. We argue that these anatomical and functional differences may be regarded as the result of an evolutionary optimization adapting to the processing of the most relevant stimuli occurring in the upper and lower visual fields.

Top-down laminar organization of the excitatory network in motor cortex

Cortical layering is a hallmark of the mammalian neocortex and a major determinant of local synaptic circuit organization in sensory systems. In motor cortex, the laminar organization of cortical circuits has not been resolved, although their input-output operations are crucial for motor control. Here, we developed a general approach for estimating layer-specific connectivity in cortical circuits and applied it to mouse motor cortex. From these data we computed a laminar presynaptic --> postsynaptic connectivity matrix, W(post,pre), revealing a complement of stereotypic pathways dominated by layer 2 outflow to deeper layers. Network modeling predicted, and experiments with disinhibited slices confirmed, that stimuli targeting upper, but not lower, cortical layers effectively evoked network-wide events. Thus, in motor cortex, descending excitation from a preamplifier-like network of upper-layer neurons drives output neurons in lower layers. Our analysis provides a quantitative wiring-diagram framework for further investigation of the excitatory networks mediating cortical mechanisms of motor control.

Signals and noise in an inhibitory interneuron diverge to control activity in nearby retinal ganglion cells

Information about sensory stimuli is represented by spatiotemporal patterns of neural activity. The complexity of the central nervous system, however, frequently obscures the origin and properties of signals and noise that underlie these activity patterns. We minimized this constraint by examining mechanisms governing correlated activity in mouse retinal ganglion cells (RGCs) under conditions in which light-evoked responses traverse a specific circuit, the rod bipolar pathway. Signals and noise in this circuit produced correlated synaptic input to neighboring On and Off RGCs. Temporal modulation of light intensity did not alter the degree to which noise in the input to nearby RGCs was correlated, and action potential generation in individual RGCs was largely insensitive to differences in network noise generated by dynamic and static light stimuli. Together, these features enable noise in shared circuitry to diminish simultaneous action potential generation in neighboring On and Off RGCs under a variety of conditions.

Visual recognition and inference using dynamic overcomplete sparse learning

We present a hierarchical architecture and learning algorithm for visual recognition and other visual inference tasks such as imagination, reconstruction of occluded images, and expectation-driven segmentation. Using properties of biological vision for guidance, we posit a stochastic generative world model and from it develop a simplified world model (SWM) based on a tractable variational approximation that is designed to enforce sparse coding. Recent developments in computational methods for learning overcomplete representations (Lewicki & Sejnowski, 2000; Teh, Welling, Osindero, & Hinton, 2003) suggest that overcompleteness can be useful for visual tasks, and we use an overcomplete dictionary learning algorithm (Kreutz-Delgado, et al., 2003) as a preprocessing stage to produce accurate, sparse codings of images. Inference is performed by constructing a dynamic multilayer network with feedforward, feedback, and lateral connections, which is trained to approximate the SWM. Learning is done with a variant of the back-propagation-through-time algorithm, which encourages convergence to desired states within a fixed number of iterations. Vision tasks require large networks, and to make learning efficient, we take advantage of the sparsity of each layer to update only a small subset of elements in a large weight matrix at each iteration. Experiments on a set of rotated objects demonstrate various types of visual inference and show that increasing the degree of overcompleteness improves recognition performance in difficult scenes with occluded objects in clutter.

Information processing in the primate retina: circuitry and coding

The function of any neural circuit is governed by connectivity of neurons in the circuit and the computations performed by the neurons. Recent research on retinal function has substantially advanced understanding in both areas. First, visual information is transmitted to the brain by at least 17 distinct retinal ganglion cell types defined by characteristic morphology, light response properties, and central projections. These findings provide a much more accurate view of the parallel visual pathways emanating from the retina than do previous models, and they highlight the importance of identifying distinct cell types and their connectivity in other neural circuits. Second, encoding of visual information involves significant temporal structure and interactions in the spike trains of retinal neurons. The functional importance of this structure is revealed by computational analysis of encoding and decoding, an approach that may be applicable to understanding the function of other neural circuits.

Cortical evolution and human behaviour

All mammals have complex behaviours but these are generally stereotyped in nature and lack the flexibility of human behaviour. Can the flexibility of human behaviour be understood as an evolutionary extension of previous behaviours or is it a departure? Theories pertaining to this question have a long history including, now refuted, theories on neoteny. This paper, using an evolutionary developmental biology approach, outlines some existing theories and suggests some novel ideas. Previous trends during brain evolution are determined by outlining the phylogeny and ontogeny of the six layered mammalian isocortex with particular reference to the primate lineage. These evolutionary trends are extrapolated to hominids to postulate the effect of increasingly large brains. The palaeoanthropological literature is cited to debate the nature and time course of behavioural change during hominid evolution. In particular, when was truly flexible behaviour first evident, and did it occur gradually or suddenly? The proposed isocortical and behavioural changes during hominid evolution are then equated to determine if modern human behaviour can be seen as part of a continuum. It is concluded that a continuation of previous trends in isocortical evolution maybe inadequate to explain human behavioural flexibility. Several possible departures from previous trends that would be compatible with increased behavioural flexibility are suggested. These mainly relate to evolutionary changes in the later stages of isocortical development and in particular during the activity-dependant phase when cortico-cortical connections are refined.

Integrated mechanisms of anticipation and rate-of-change computations in cortical circuits

Local neocortical circuits are characterized by stereotypical physiological and structural features that subserve generic computational operations. These basic computations of the cortical microcircuit emerge through the interplay of neuronal connectivity, cellular intrinsic properties, and synaptic plasticity dynamics. How these interacting mechanisms generate specific computational operations in the cortical circuit remains largely unknown. Here, we identify the neurophysiological basis of both the rate of change and anticipation computations on synaptic inputs in a cortical circuit. Through biophysically realistic computer simulations and neuronal recordings, we show that the rate-of-change computation is operated robustly in cortical networks through the combination of two ubiquitous brain mechanisms: short-term synaptic depression and spike-frequency adaptation. We then show how this rate-of-change circuit can be embedded in a convergently connected network to anticipate temporally incoming synaptic inputs, in quantitative agreement with experimental findings on anticipatory responses to moving stimuli in the primary visual cortex. Given the robustness of the mechanism and the widespread nature of the physiological machinery involved, we suggest that rate-of-change computation and temporal anticipation are principal, hard-wired functions of neural information processing in the cortical microcircuit.

The cerebral cortex is the region of the brain whose intricate connectivity and physiology is thought to subserve most computations required for effective action in mammals. Through biophysically realistic computer simulation and experimental recordings in brain tissue, the authors show how a specific combination of physiological mechanisms often found in neurons of the cortex transforms an input signal into another signal that represents the rate of change of the slower components of the input. This is the first report of a neurobiological implementation of an approximate mathematical derivative in the cortex. Further, such a signal integrates naturally into a neurobiologically simple network that is able to generate a linear prediction of its inputs. Anticipation of information is a primary concern of spatially extended organisms which are subject to neural delays, and it has been demonstrated at various different levels: from the retina to sensori-motor integration. We present here a simple and general mechanism for anticipation that can operate incrementally within local circuits of the cortex, to compensate for time-consuming computations and conduction delays and thus contribute to effective real-time action.

Laminar processing in the visual cortical column

Sensory regions of neocortex are organized as arrays of vertical columns composed of cells that share similar response properties, with the orientation columns of the cat's visual cortex being the best known example. Interest in how sensitivity to different stimulus features first emerges in the columns and how this selectivity is refined by subsequent processing has fueled decades of research. A natural starting point in approaching these issues is anatomy. Each column traverses the six cortical layers and each layer has a unique pattern of inputs, intrinsic connections and outputs. Thus, it makes sense to explore the possibility of corresponding laminar differences in sensory function, that is, to examine relationships between morphology and physiology. In addition, to help identify general patterns of cortical organization, it is useful to compare results obtained from different sensory systems and diverse species. The picture that emerges from such comparisons is that each cortical layer serves a distinct role in sensory function. Furthermore, different cortices appear to share some common strategies for processing information but also have specialized mechanisms adapted for the demands of specific sensory tasks.