How does the cerebral cortex work? Learning, attention, and grouping by the laminar circuits of visual cortex

The organization of neocortex into layers is one of its most salient anatomical features. These layers include circuits that form functional columns in cortical maps. A major unsolved problem concerns how bottom-up, top-down, and horizontal interactions are organized within cortical layers to generate adaptive behaviors. This article models how these interactions help visual cortex to realize: (i) the binding process whereby cortex groups distributed data into coherent object representations; (ii) the attentional process whereby cortex selectively processes important events; and (iii) the developmental and learning processes whereby cortex shapes its circuits to match environmental constraints. New computational ideas about feedback systems suggest how neocortex develops and learns in a stable way, and why top-down attention requires converging bottom-up inputs to fully activate cortical cells, whereas perceptual groupings do not.     

Learning to link visual contours

In complex visual scenes, linking related contour elements is important for object recognition. This process, thought to be stimulus driven and hard wired, has substrates in primary visual cortex (V1). Here, however, we find contour integration in V1 to depend strongly on perceptual learning and top-down influences that are specific to contour detection. In naive monkeys, the information about contours embedded in complex backgrounds is absent in V1 neuronal responses and is independent of the locus of spatial attention. Training animals to find embedded contours induces strong contour-related responses specific to the trained retinotopic region. These responses are most robust when animals perform the contour detection task but disappear under anesthesia. Our findings suggest that top-down influences dynamically adapt neural circuits according to specific perceptual tasks. This may serve as a general neuronal mechanism of perceptual learning and reflect top-down mediated changes in cortical states.     

Integrating Top-Down and Bottom-Up Sensory Processing by Somato-Dendritic Interactions

The classical view of cortical information processing is that of a bottom-up process in a feedforward hierarchy. However, psychophysical, anatomical, and physiological evidence suggests that top-down effects play a crucial role in the processing of input stimuli. Not much is known about the neural mechanisms underlying these effects. Here we investigate a physiologically inspired model of two reciprocally connected cortical areas. Each area receives bottom-up as well as top-down information. This information is integrated by a mechanism that exploits recent findings on somato-dendritic interactions. (1) This results in a burst signal that is robust in the context of noise in bottom-up signals. (2) Investigating the influence of additional top-down information, priming-like effects on the processing of bottom-up input can be demonstrated. (3) In accordance with recent physiological findings, interareal coupling in low-frequency ranges is characteristically enhanced by top-down mechanisms. The proposed scheme combines a qualitative influence of top-down directed signals on the temporal dynamics of neuronal activity with a limited effect on the mean firing rate of the targeted neurons. As it gives an account of the system properties on the cellular level, it is possible to derive several experimentally testable predictions.     

Transient process of cortical activity during Necker cube perception: from local clusters to global synchrony

Background

It has been discussed that neural phase-synchrony across distant cortical areas (or global phase-synchrony) was correlated with various aspects of consciousness. The generating process of the synchrony, however, remains largely unknown. As a first step, we investigate transient process of global phase-synchrony, focusing on phase-synchronized clusters. We hypothesize that the phase-synchronized clusters are dynamically organized before global synchrony and clustering patterns depend on perceptual conditions.

Methods

In an EEG study, Kitajo reported that phase-synchrony across distant cortical areas was selectively enhanced by top-down attention around 4 Hz in Necker cube perception. Here, we further analyzed the phase-synchronized clusters using hierarchical clustering which sequentially binds up the nearest electrodes based on similarity of phase locking between the cortical signals. First, we classified dominant components of the phase-synchronized clusters over time. We then investigated how the phase-synchronized clusters change with time, focusing on their size and spatial structure.

Results

Phase-locked clusters organized a stable spatial pattern common to the perceptual conditions. In addition, the phase-locked clusters were modulated transiently depending on the perceptual conditions and the time from the perceptual switch. When top-down attention succeeded in switching perception as subjects intended, independent clusters at frontal and occipital areas grew to connect with each other around the time of the perceptual switch. However, the clusters in the occipital and left parietal areas remained divided when top-down attention failed in switching perception. When no primary biases exist, the cluster in the occipital area grew to its maximum at the time of the perceptual switch within the occipital area.

Conclusions

Our study confirmed the existence of stable phase-synchronized clusters. Furthermore, these clusters were transiently connected with each other. The connecting pattern depended on subjects’ internal states. These results suggest that subjects’ attentional states are associated with distinct spatio-temporal patterns of the phase-locked clusters.

     

Human Decision Making Based on Variations in Internal Noise: An EEG Study

Perceptual decision making is prone to errors, especially near threshold. Physiological, behavioural and modeling studies suggest this is due to the intrinsic or ‘internal’ noise in neural systems, which derives from a mixture of bottom-up and top-down sources. We show here that internal noise can form the basis of perceptual decision making when the external signal lacks the required information for the decision. We recorded electroencephalographic (EEG) activity in listeners attempting to discriminate between identical tones. Since the acoustic signal was constant, bottom-up and top-down influences were under experimental control. We found that early cortical responses to the identical stimuli varied in global field power and topography according to the perceptual decision made, and activity preceding stimulus presentation could predict both later activity and behavioural decision. Our results suggest that activity variations induced by internal noise of both sensory and cognitive origin are sufficient to drive discrimination judgments.     

Perceptual learning via modification of cortical top-down signals

The primary visual cortex (V1) is pre-wired to facilitate the extraction of behaviorally important visual features. Collinear edge detectors in V1, for instance, mutually enhance each other to improve the perception of lines against a noisy background. The same pre-wiring that facilitates line extraction, however, is detrimental when subjects have to discriminate the brightness of different line segments. How is it possible to improve in one task by unsupervised practicing, without getting worse in the other task? The classical view of perceptual learning is that practicing modulates the feedforward input stream through synaptic modifications onto or within V1. However, any rewiring of V1 would deteriorate other perceptual abilities different from the trained one. We propose a general neuronal model showing that perceptual learning can modulate top-down input to V1 in a task-specific way while feedforward and lateral pathways remain intact. Consistent with biological data, the model explains how context-dependent brightness discrimination is improved by a top-down recruitment of recurrent inhibition and a top-down induced increase of the neuronal gain within V1. Both the top-down modulation of inhibition and of neuronal gain are suggested to be universal features of cortical microcircuits which enable perceptual learning.     

Top-Down Feedback in an HMAX-Like Cortical Model of Object Perception Based on Hierarchical Bayesian Networks and Belief Propagation

Hierarchical generative models, such as Bayesian networks, and belief propagation have been shown to provide a theoretical framework that can account for perceptual processes, including feedforward recognition and feedback modulation. The framework explains both psychophysical and physiological experimental data and maps well onto the hierarchical distributed cortical anatomy. However, the complexity required to model cortical processes makes inference, even using approximate methods, very computationally expensive. Thus, existing object perception models based on this approach are typically limited to tree-structured networks with no loops, use small toy examples or fail to account for certain perceptual aspects such as invariance to transformations or feedback reconstruction. In this study we develop a Bayesian network with an architecture similar to that of HMAX, a biologically-inspired hierarchical model of object recognition, and use loopy belief propagation to approximate the model operations (selectivity and invariance). Crucially, the resulting Bayesian network extends the functionality of HMAX by including top-down recursive feedback. Thus, the proposed model not only achieves successful feedforward recognition invariant to noise, occlusions, and changes in position and size, but is also able to reproduce modulatory effects such as illusory contour completion and attention. Our novel and rigorous methodology covers key aspects such as learning using a layerwise greedy algorithm, combining feedback information from multiple parents and reducing the number of operations required. Overall, this work extends an established model of object recognition to include high-level feedback modulation, based on state-of-the-art probabilistic approaches. The methodology employed, consistent with evidence from the visual cortex, can be potentially generalized to build models of hierarchical perceptual organization that include top-down and bottom-up interactions, for example, in other sensory modalities.     

Subjective visual perception: from local processing to emergent phenomena of brain activity

The combination of electrophysiological recordings with ambiguous visual stimulation made possible the detection of neurons that represent the content of subjective visual perception and perceptual suppression in multiple cortical and subcortical brain regions. These neuronal populations, commonly referred to as the neural correlates of consciousness, are more likely to be found in the temporal and prefrontal cortices as well as the pulvinar, indicating that the content of perceptual awareness is represented with higher fidelity in higher-order association areas of the cortical and thalamic hierarchy, reflecting the outcome of competitive interactions between conflicting sensory information resolved in earlier stages. However, despite the significant insights into conscious perception gained through monitoring the activities of single neurons and small, local populations, the immense functional complexity of the brain arising from correlations in the activity of its constituent parts suggests that local, microscopic activity could only partially reveal the mechanisms involved in perceptual awareness. Rather, the dynamics of functional connectivity patterns on a mesoscopic and macroscopic level could be critical for conscious perception. Understanding these emergent spatio-temporal patterns could be informative not only for the stability of subjective perception but also for spontaneous perceptual transitions suggested to depend either on the dynamics of antagonistic ensembles or on global intrinsic activity fluctuations that may act upon explicit neural representations of sensory stimuli and induce perceptual reorganization. Here, we review the most recent results from local activity recordings and discuss the potential role of effective, correlated interactions during perceptual awareness.     

Dissociating explicit timing from temporal expectation with fMRI

Explicit timing is engaged whenever subjects make a deliberate estimate of discrete duration in order to compare it with a previously memorised standard. Conversely, implicit timing is engaged, even without a specific instruction to time, whenever sensorimotor information is temporally structured and can be used to predict the duration of future events. Both emergent timing (motor) and temporal expectation (perceptual) are forms of implicit timing. Recent fMRI studies demonstrate discrete neural substrates for explicit and implicit timing. Specifically, basal ganglia are activated almost invariably by explicit timing, with co-activation of prefrontal, premotor and cerebellar areas being more context-dependent. Conversely, implicit perceptual timing (or "temporal expectation") recruits cortical action circuits, comprising inferior parietal and premotor areas, highlighting its role in the optimisation of prospective behaviour.     

Oscillations in the prefrontal cortex: a gateway to memory and attention

We consider the potential role of oscillations in the prefrontal cortex (PFC) in mediating attention, working memory and memory consolidation. Activity in the theta, beta, and gamma bands is related to communication between PFC and different brain areas. While gamma/beta oscillations mediate bottom-up and top-down interactions between PFC and visual cortices, related to attention, theta rhythms are engaged by hippocampal/PFC interplay. These interactions are dynamic, depending on the nature and relevance of the information currently being processed. The profound modifications of the PFC neuronal network associated with changes in oscillatory coherence are controlled by neuromodulators such as dopamine, which thereby allow or prevent the formation of cell assemblies for information encoding and storage.     

Binocular Flash Suppression in the Primary Visual Cortex of Anesthetized and Awake Macaques

Primary visual cortex (V1) was implicated as an important candidate for the site of perceptual suppression in numerous psychophysical and imaging studies. However, neurophysiological results in awake monkeys provided evidence for competition mainly between neurons in areas beyond V1. In particular, only a moderate percentage of neurons in V1 were found to modulate in parallel with perception with magnitude substantially smaller than the physical preference of these neurons. It is yet unclear whether these small modulations are rooted from local circuits in V1 or influenced by higher cognitive states. To address this question we recorded multi-unit spiking activity and local field potentials in area V1 of awake and anesthetized macaque monkeys during the paradigm of binocular flash suppression. We found that a small but significant modulation was present in both the anesthetized and awake states during the flash suppression presentation. Furthermore, the relative amplitudes of the perceptual modulations were not significantly different in the two states. We suggest that these early effects of perceptual suppression might occur locally in V1, in prior processing stages or within early visual cortical areas in the absence of top-down feedback from higher cognitive stages that are suppressed under anesthesia.     

The neural basis of perceptual learning

Perceptual learning is a lifelong process. We begin by encoding information about the basic structure of the natural world and continue to assimilate information about specific patterns with which we become familiar. The specificity of the learning suggests that all areas of the cerebral cortex are plastic and can represent various aspects of learned information. The neural substrate of perceptual learning relates to the nature of the neural code itself, including changes in cortical maps, in the temporal characteristics of neuronal responses, and in modulation of contextual influences. Top-down control of these representations suggests that learning involves an interaction between multiple cortical areas.     

Vision without knowledge.

A brain-damaged patient (D.F.) with visual form agnosia is described and discussed. D.F. has a profound inability to recognize objects, places and people, in large part because of her inability to make perceptual discriminations of size, shape or orientation, despite having good visual acuity. Yet she is able to perform skilled actions that depend on that very same size, shape and orientation information that is missing from her perceptual awareness. It is suggested that her intact vision can best be understood within the framework of a dual processing model, according to which there are two cortical processing streams operating on different coding principles, for perception and for action, respectively. These may be expected to have different degrees of dependence on top-down information. One possibility is that D.F.''s lack of explicit awareness of the visual cues that guide her behaviour may result from her having to rely on a processing system which is not knowledge-based in a broad sense. Conversely, it may be that the perceptual system can provide conscious awareness of its products in normal individuals by virtue of the fact that it does interact with a stored base of visual knowledge.     

Perceptual learning and decision-making in human medial frontal cortex

The dominant view that perceptual learning is accompanied by changes in early sensory representations has recently been challenged. Here we tested the idea that perceptual learning can be accounted for by reinforcement learning involving changes in higher decision-making areas. We trained subjects on an orientation discrimination task involving feedback over 4 days, acquiring fMRI data on the first and last day. Behavioral improvements were well explained by a reinforcement learning model in which learning leads to enhanced readout of sensory information, thereby establishing noise-robust representations of decision variables. We find stimulus orientation encoded in early visual and higher cortical regions such as lateral parietal cortex and anterior cingulate cortex (ACC). However, only activity patterns in the ACC tracked changes in decision variables during learning. These results provide strong evidence for perceptual learning-related changes in higher order areas and suggest that perceptual and reward learning are based on a common neurobiological mechanism.     

Seeing invisible motion: a human FMRI study

A common view about visual consciousness is that it could arise when and where activity reaches some higher level of processing along the cortical hierarchy. Reports showing that activity in striate cortex can be dissociated from awareness , whereas the latter modulates activity in higher areas , point in this direction. In the specific case of visual motion, a central, "perceptual" role has been assigned to area V5: several human and monkey studies have shown V5 activity to correlate with the motion percept. Here we show that activity in this and other higher cortical areas can be also dissociated from perception and follow the physical stimulus instead. The motion information in a peripheral grating modulated fMRI responses, despite being invisible to human volunteers: under crowding conditions , areas V3A, V5, and parietal cortex still showed increased activity when the grating was moving compared to when it was flickering. We conclude that stimulus-specific activation of higher cortical areas does not necessarily result in awareness of the underlying stimulus.     

Recurrent V1–V2 interaction in early visual boundary processing

A majority of cortical areas are connected via feedforward and feedback fiber projections. In feedforward pathways we mainly observe stages of feature detection and integration. The computational role of the descending pathways at different stages of processing remains mainly unknown. Based on empirical findings we suggest that the top-down feedback pathways subserve a context-dependent gain control mechanism. We propose a new computational model for recurrent contour processing in which normalized activities of orientation selective contrast cells are fed forward to the next processing stage. There, the arrangement of input activation is matched against local patterns of contour shape. The resulting activities are subsequently fed back to the previous stage to locally enhance those initial measurements that are consistent with the top-down generated responses. In all, we suggest a computational theory for recurrent processing in the visual cortex in which the significance of local measurements is evaluated on the basis of a broader visual context that is represented in terms of contour code patterns. The model serves as a framework to link physiological with perceptual data gathered in psychophysical experiments. It handles a variety of perceptual phenomena, such as the local grouping of fragmented shape outline, texture surround and density effects, and the interpolation of illusory contours. Received: 28 October 1998 / Accepted in revised form: 19 March 1999     

Neural correlates of individual performance differences in resolving perceptual conflict

Attentional mechanisms are a crucial prerequisite to organize behavior. Most situations may be characterized by a 'competition' between salient, but irrelevant stimuli and less salient, relevant stimuli. In such situations top-down and bottom-up mechanisms interact with each other. In the present fMRI study, we examined how interindividual differences in resolving situations of perceptual conflict are reflected in brain networks mediating attentional selection. Doing so, we employed a change detection task in which subjects had to detect luminance changes in the presence and absence of competing distractors. The results show that good performers presented increased activation in the orbitofrontal cortex (BA 11), anterior cingulate (BA 25), inferior parietal lobule (BA 40) and visual areas V2 and V3 but decreased activation in BA 39. This suggests that areas mediating top-down attentional control are stronger activated in this group. Increased activity in visual areas reflects distinct neuronal enhancement relating to selective attentional mechanisms in order to solve the perceptual conflict. Opposed to good performers, brain areas activated by poor performers comprised the left inferior parietal lobule (BA 39) and fronto-parietal and visual regions were continuously deactivated, suggesting that poor performers perceive stronger conflict than good performers. Moreover, the suppression of neural activation in visual areas might indicate a strategy of poor performers to inhibit the processing of the irrelevant non-target feature. These results indicate that high sensitivity in perceptual areas and increased attentional control led to less conflict in stimulus processing and consequently to higher performance in competitive attentional selection.     

The Primary Visual Cortex Is Differentially Modulated by Stimulus-Driven and Top-Down Attention

Selective attention can be focused either volitionally, by top-down signals derived from task demands, or automatically, by bottom-up signals from salient stimuli. Because the brain mechanisms that underlie these two attention processes are poorly understood, we recorded local field potentials (LFPs) from primary visual cortical areas of cats as they performed stimulus-driven and anticipatory discrimination tasks. Consistent with our previous observations, in both tasks, we found enhanced beta activity, which we have postulated may serve as an attention carrier. We characterized the functional organization of task-related beta activity by (i) cortical responses (EPs) evoked by electrical stimulation of the optic chiasm and (ii) intracortical LFP correlations. During the anticipatory task, peripheral stimulation that was preceded by high-amplitude beta oscillations evoked large-amplitude EPs compared with EPs that followed low-amplitude beta. In contrast, during the stimulus-driven task, cortical EPs preceded by high-amplitude beta oscillations were, on average, smaller than those preceded by low-amplitude beta. Analysis of the correlations between the different recording sites revealed that beta activation maps were heterogeneous during the bottom-up task and homogeneous for the top-down task. We conclude that bottom-up attention activates cortical visual areas in a mosaic-like pattern, whereas top-down attentional modulation results in spatially homogeneous excitation.     

Perceptual learning and sensomotor flexibility: cortical plasticity under attentional control?

Recent research reveals long-lasting cortical plasticity of early sensory cortices even in adults. Sensory signals could be modified under top-down control if necessary quite early in order to optimize their signal-to-noise ratio, leading to 'low level' or 'early' perceptual learning (PL). For easy tasks, such elaborate top-down influences are usually not required, and learning is restricted to late selection of the appropriate signals on higher cortical levels, which seems easier and faster to achieve. But to reach the absolute limits of sensory performance, PL seems to optimize the entire chain of sensory processing. Hence, improvement for these extreme perceptual abilities is quite specific for a number of stimulus parameters, such as the position in the visual field and sometimes even the trained eye, reflecting the specificity of receptive fields in early sensory cortices. Early PL may be just one example--even if a very extensive one--of the mechanisms of neuronal plasticity and sensomotor flexibility that are constantly updating our sensomotor representations as a result of experience. As an illustration, this review contains some new experimental results on PL and sensory flexibility in the context of adaptation to multifocal intraocular lenses.     

Improvement of tactile discrimination performance and enlargement of cortical somatosensory maps after 5 Hz rTMS

Repetitive transcranial magnetic stimulation (rTMS) is increasingly used to investigate mechanisms of brain functions and plasticity, but also as a promising new therapeutic tool. The effects of rTMS depend on the intensity and frequency of stimulation and consist of changes of cortical excitability, which often persists several minutes after termination of rTMS. While these findings imply that cortical processing can be altered by applying current pulses from outside the brain, little is known about how rTMS persistently affects learning and perception. Here we demonstrate in humans, through a combination of psychophysical assessment of two-point discrimination thresholds and functional magnetic resonance imaging (fMRI), that brief periods of 5 Hz rTMS evoke lasting perceptual and cortical changes. rTMS was applied over the cortical representation of the right index finger of primary somatosensory cortex, resulting in a lowering of discrimination thresholds of the right index finger. fMRI revealed an enlargement of the right index finger representation in primary somatosensory cortex that was linearly correlated with the individual rTMS-induced perceptual improvement indicative of a close link between cortical and perceptual changes. The results demonstrate that repetitive, unattended stimulation from outside the brain, combined with a lack of behavioral information, are effective in driving persistent improvement of the perception of touch. The underlying properties and processes that allow cortical networks, after being modified through TMS pulses, to reach new organized stable states that mediate better performance remain to be clarified.