Visual feature integration and the temporal correlation hypothesis

A Neuro-hormonal Circuit for Paternal Behavior Controlled by a Hypothalamic Network Oscillation

Parental behavior is pervasive throughout the animal kingdom and essential for species survival. However, the relative contribution of the father to offspring care differs markedly across animals, even between related species. The mechanisms that organize and control paternal behavior remain poorly understood. Using Sprague-Dawley rats and C57BL/6 mice, two species at opposite ends of the paternal spectrum, we identified that distinct electrical oscillation patterns in neuroendocrine dopamine neurons link to a chain of low dopamine release, high circulating prolactin, prolactin receptor-dependent activation of medial preoptic area galanin neurons, and paternal care behavior in male mice. In rats, the same parameters exhibit inverse profiles. Optogenetic manipulation of these rhythms in mice dramatically shifted serum prolactin and paternal behavior, whereas injecting prolactin into non-paternal rat sires triggered expression of parental care. These findings identify a frequency-tuned brain-endocrine-brain circuit that can act as a gain control system determining a species’ parental strategy.

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Species-specific hypothalamic dopamine neuron rhythms yield distinct prolactin release

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Serum prolactin primes the “parental” neural circuit for pup care during fatherhood

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Optogenetic control of TIDA frequency tunes prolactin and paternal behavior

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Prolactin receptors in the MPOA are required for paternal behavior

The presence of joint predictors generates conjunctive predictions

The cognitive system readily detects statistical relationships where a cue predicts a specific outcome. What is less known is how the mind generates predictions when multiple cues are presented simultaneously that predict different outcomes. Here, we examine the nature of such predictions. Specifically, we examine whether the presence of joint cues leads to conjunctive predictions that represent the overlap between the outcomes associated with the cues, or disjunctive predictions that represent the total possible outcomes. To test this, we used a visual search paradigm where participants first viewed a cue and then searched for a target in an array. Each cue predicted where the target would appear in one of the four quadrants in the array. After learning the cue-location associations, two cues were presented simultaneously, and participants searched for the target where the target now appeared in each quadrant with equal probability. We found that participants were faster to find the target in conjunctive locations over disjunctive ones upon seeing two cues (Experiment 1). This attentional prioritization was not solely driven by the smaller spatial scope of conjunctive locations (Experiment 2), and was present when two cues were presented in two feature dimensions in a single object (Experiment 3). These results were consistent with a conjunctive account, where the presence of joint cues led to a conjunctive prediction that represented the overlap of the different outcomes associated with each cue. The study sheds a new light on how the mind makes predictions in novel contexts based on existing knowledge.

Reduced Monocular Luminance Increases Monocular Temporal Synchrony Threshold in Human Adults

Purpose

The purpose of this study was to present our investigation of the influence of reduced monocular luminance on monocular and dichoptic temporal synchrony processing in healthy adults.

Methods

Ten adults with normal or corrected to normal visual acuity participated in our psychophysical study. The temporal synchrony threshold in dichoptic (experiment 1), monocular (experiment 2), and binocular (experiment 3) viewing configurations was obtained from each observer. Four flickering Gaussian dots (one synchronous and one asynchronous pair of two dots) were displayed, from which the observers were asked to identify the asynchronous pair. The temporal phase lag in the signal pair (asynchronous) but not in the reference pair (synchronous) was varied. In addition, a neutral density (ND) filter of various intensities (1.3 and 2.0 log units) was placed before the dominant eye throughout the behavioral measurement. In the end, dichoptic, monocular, and binocular thresholds were measured for each observer.

Results

With decreasing monocular luminance, the dichoptic threshold (2 ND vs. 0 ND, P < 0.001; 2 ND vs. 1.3 ND P = 0.001) and monocular threshold (2 ND vs. 0 ND, P < 0.001; 2 ND vs. 1.3 ND, P = 0.003) increased; however, the bincoular threshold remained unaffected (P = 0.576).

Conclusions

Reduced luminance induces delay and disturbs the discrimination of temporal synchrony. Our findings have clinical implications in visual disorders.

Multiplexing rhythmic information by spike timing dependent plasticity

Rhythmic activity has been associated with a wide range of cognitive processes including the encoding of sensory information, navigation, the transfer of information and others. Rhythmic activity in the brain has also been suggested to be used for multiplexing information. Multiplexing is the ability to transmit more than one signal via the same channel. Here we focus on frequency division multiplexing, in which different signals are transmitted in different frequency bands. Recent work showed that spike-timing-dependent plasticity (STDP) can facilitate the transfer of rhythmic activity downstream the information processing pathway. However, STDP has also been known to generate strong winner-take-all like competition between subgroups of correlated synaptic inputs. This competition between different rhythmicity channels, induced by STDP, may prevent the multiplexing of information. Thus, raising doubts whether STDP is consistent with the idea of multiplexing. This study explores whether STDP can facilitate the multiplexing of information across multiple frequency channels, and if so, under what conditions. We address this question in a modelling study, investigating the STDP dynamics of two populations synapsing downstream onto the same neuron in a feed-forward manner. Each population was assumed to exhibit rhythmic activity, albeit in a different frequency band. Our theory reveals that the winner-take-all like competitions between the two populations is limited, in the sense that different rhythmic populations will not necessarily fully suppress each other. Furthermore, we found that for a wide range of parameters, the network converged to a solution in which the downstream neuron responded to both rhythms. Yet, the synaptic weights themselves did not converge to a fixed point, rather remained dynamic. These findings imply that STDP can support the multiplexing of rhythmic information, and demonstrate how functionality (multiplexing of information) can be retained in the face of continuous remodeling of all the synaptic weights. The constraints on the types of STDP rules that can support multiplexing provide a natural test for our theory.

Spike timing dependent plasticity (STDP) quantifies the change in the synaptic efficacy as a function of the temporal relationship between pre- and post-synaptic firing. STDP can be viewed as a microscopic unsupervised learning rule, and a wide range of such microscopic learning rules have been described empirically. Since there is no supervisor in unsupervised learning (which would provide with the system its goal), theoreticians have struggled with the question of the possible computational roles of the various STDP rules. Previous studies have focused on the possible contribution of STDP to the spontaneous development of spatial structure. However, the rich temporal repertoire of reported STDP rules has largely been ignored. Here we studied the contribution of STDP to the development of temporal structure. We show how STDP can shape synaptic efficacies to facilitate the transfer of rhythmic information downstream and to enable the multiplexing of information across different frequency channels. Our work emphasizes the relationship between the temporal structure of the STDP rule and the rhythmic activity it can support.

Translational Neuroscience in Autism: From Neuropathology to Transcranial Magnetic Stimulation Therapies

The presence of heterotopias, increased regional density of neurons at the gray-white matter junction, and focal cortical dysplasias all suggest an abnormality of neuronal migration in autism spectrum disorder (ASD). The abnormality is borne from a dissonance in timing between radial and tangentially migrating neuroblasts to the developing cortical plate. The uncoupling of excitatory and inhibitory cortical cells disturbs the coordinated interactions of neurons within local networks, thus providing abnormal patterns of brainwave activity in the gamma bandwidth. In ASD, gamma oscillation abnormalities and autonomic markers offer measures of therapeutic progress and help in the identification of subgroups.

Understanding the dynamics of biological and neural oscillator networks through exact mean-field reductions: a review

Many biological and neural systems can be seen as networks of interacting periodic processes. Importantly, their functionality, i.e., whether these networks can perform their function or not, depends on the emerging collective dynamics of the network. Synchrony of oscillations is one of the most prominent examples of such collective behavior and has been associated both with function and dysfunction. Understanding how network structure and interactions, as well as the microscopic properties of individual units, shape the emerging collective dynamics is critical to find factors that lead to malfunction. However, many biological systems such as the brain consist of a large number of dynamical units. Hence, their analysis has either relied on simplified heuristic models on a coarse scale, or the analysis comes at a huge computational cost. Here we review recently introduced approaches, known as the Ott-Antonsen and Watanabe-Strogatz reductions, allowing one to simplify the analysis by bridging small and large scales. Thus, reduced model equations are obtained that exactly describe the collective dynamics for each subpopulation in the oscillator network via few collective variables only. The resulting equations are next-generation models: Rather than being heuristic, they exactly link microscopic and macroscopic descriptions and therefore accurately capture microscopic properties of the underlying system. At the same time, they are sufficiently simple to analyze without great computational effort. In the last decade, these reduction methods have become instrumental in understanding how network structure and interactions shape the collective dynamics and the emergence of synchrony. We review this progress based on concrete examples and outline possible limitations. Finally, we discuss how linking the reduced models with experimental data can guide the way towards the development of new treatment approaches, for example, for neurological disease.

Gamma-band correlations in the primary visual cortex

This paper generalizes and extends previous work on using neural field theory to quantitatively analyze the two-dimensional (2D) spatiotemporal correlation properties of gamma-band (30-70 Hz) oscillations evoked by stimuli arriving at the primary visual cortex, and modulated by patchy connectivities that depend on orientation preference (OP). Correlation functions are derived analytically for general stimulus and measurement conditions. The theoretical results reproduce a range of published experimental results. These include (i) the existence of two-point oscillatory temporal cross correlations with zero time lag between neurons with similar OP; (ii) the influence of spatial separation of neurons on the strength of the correlations; and (iii) the effects of differing stimulus orientations. They go beyond prior work by incorporating experimentally observed patchy projection patterns to predict the 2D correlation structure including both OP and ocular dominance effects, thereby relaxing assumptions of translational invariance implicit in prior one-dimensional analysis.

Phasic cholinergic signaling promotes emergence of local gamma rhythms in excitatory-inhibitory networks

Recent experimental results have shown that the detection of cues in behavioral attention tasks relies on transient increases of acetylcholine (ACh) release in frontal cortex and cholinergically driven oscillatory activity in the gamma frequency band (Howe et al. Journal of Neuroscience, 2017, 37, 3215). The cue-induced gamma rhythmic activity requires stimulation of M1 muscarinic receptors. Using biophysical computational modeling, we show that a network of excitatory (E) and inhibitory (I) neurons that initially displays asynchronous firing can generate transient gamma oscillatory activity in response to simulated brief pulses of ACh. ACh effects are simulated as transient modulation of the conductance of an M-type K⁺ current which is blocked by activation of muscarinic receptors and has significant effects on neuronal excitability. The ACh-induced effects on the M current conductance, g_Ks , change network dynamics to promote the emergence of network gamma rhythmicity through a Pyramidal-Interneuronal Network Gamma mechanism. Depending on connectivity strengths between and among E and I cells, gamma activity decays with the simulated g_Ks transient modulation or is sustained in the network after the g_Ks transient has completely dissipated. We investigated the sensitivity of the emergent gamma activity to synaptic strengths, external noise and simulated levels of g_Ks modulation. To address recent experimental findings that cholinergic signaling is likely spatially focused and dynamic, we show that localized g_Ks modulation can induce transient changes of cellular excitability in local subnetworks, subsequently causing population-specific gamma oscillations. These results highlight dynamical mechanisms underlying localization of ACh-driven responses and suggest that spatially localized, cholinergically induced gamma may contribute to selectivity in the processing of competing external stimuli, as occurs in attentional tasks.

Early-stage dysfunction of hippocampal theta and gamma oscillations and its modulation of neural network in a transgenic 5xFAD mouse model

Alzheimer's disease (AD) is pathologically characterized by amyloid-β (Aβ) accumulation, which induces Aβ-dependent neuronal dysfunctions. We focused on the early-stage disease progression and examined the neuronal network functioning in the 5xFAD mice. The simultaneous intracranial recordings were obtained from the hippocampal perforant path (PP) and the dentate gyrus (DG). Concomitant to Aβ accumulation, theta power was strongly attenuated in the PP and DG regions of 5xFAD mice compared to those in nontransgenic littermates. For either theta rhythm or gamma oscillation, the phase synchronization on the PP-DG pathway was impaired, evidenced by decreased phase locking value and diminished coherency index. To alleviate the neural oscillatory deficits in early-stage AD, a neural modulation approach (rTMS) was used to activate gamma oscillations and strengthen the synchronicity of neuronal activity on the PP-DG pathway. In brief, there was a significant neuronal network dysfunction at an early-stage AD-like pathology, which preceded the onset of cognitive deficits and was likely driven by Aβ accumulation, suggesting that the neural oscillation analysis played an important role in early AD diagnosis.

Electrophysiological correlates of gist perception: a steady-state visually evoked potentials study

Gist perception refers to perceiving the substance or general meaning of a scene. To investigate its neuronal mechanisms, we used the steady-state visually evoked potential (SSVEP) method—an evoked oscillatory cortical response at the same frequency as a visual stimulus flickered at this frequency. Two neighboring stimuli were flickered at different frequencies f₁ and f₂, for example, a drawing of a sun on the left side of the screen flickering at 8.6 Hz and the drawing of a parasol on the right side of the screen flickering at 12 Hz. SSVEPs enabled us to separate the responses to the two distinct stimuli by extracting oscillatory brain responses at f₁ and f₂. Additionally, it allowed to investigate intermodulation frequencies, that is, the brain’s response at a linear combination of f₁ and f₂ (here at f₁ + f₂ = 20.6 Hz) as an indicator of processing shared aspects of the input, that is, gist perception (here: a beach scene). We recorded high-density EEG of 18 participants. Results revealed clear and separable neuronal oscillations at f₁ and f₂. Additionally, occipital electrodes showed increased amplitudes at the intermodulation frequency in related as compared to unrelated pairs. The increase in intermodulation frequency was associated with bilateral temporal and parietal lobe activation, probably reflecting the interaction of local object representations as a basis for activating the gist network. The study demonstrates that SSVEPs are an excellent method to unravel mechanisms underlying the processing within multi-stimulus displays in the context of gist perception.

Neurophysiological Hallmarks of Neurodegenerative Cognitive Decline: The Study of Brain Connectivity as A Biomarker of Early Dementia

Neurodegenerative processes of various types of dementia start years before symptoms, but the presence of a "neural reserve", which continuously feeds and supports neuroplastic mechanisms, helps the aging brain to preserve most of its functions within the "normality" frame. Mild cognitive impairment (MCI) is an intermediate stage between dementia and normal brain aging. About 50% of MCI subjects are already in a stage that is prodromal-to-dementia and during the following 3 to 5 years will develop clinically evident symptoms, while the other 50% remains at MCI or returns to normal. If the risk factors favoring degenerative mechanisms are modified during early stages (i.e., in the prodromal), the degenerative process and the loss of abilities in daily living activities will be delayed. It is therefore extremely important to have biomarkers able to identify-in association with neuropsychological tests-prodromal-to-dementia MCI subjects as early as possible. MCI is a large (i.e., several million in EU) and substantially healthy population; therefore, biomarkers should be financially affordable, largely available and non-invasive, but still accurate in their diagnostic prediction. Neurodegeneration initially affects synaptic transmission and brain connectivity; methods exploring them would represent a 1st line screening. Neurophysiological techniques able to evaluate mechanisms of synaptic function and brain connectivity are attracting general interest and are described here. Results are quite encouraging and suggest that by the application of artificial intelligence (i.e., learning-machine), neurophysiological techniques represent valid biomarkers for screening campaigns of the MCI population.

Entrainment and synchronization of brain oscillations to auditory stimulations

Oscillations of neural excitability shape sensory, motor or cognitive processes. Furthermore, a large body of research demonstrates that intrinsic oscillations are entrained by external rhythms, allowing a simple and efficient way to enhance human brain functions. As an external stimulation source, repeating acoustic stimuli have been shown to provide a possible pacing signal for modulating the electrical activity recorded by the electroencephalogram (EEG). In this review, we discuss recent advances in understanding how rhythmic auditory stimulation can selectively modulate EEG oscillations. Despite growing evidence, recent evidence suggests that standard methods of data analysis are often insufficient for a definite proof of entrainment in some instances. In particular, we stressed that the complexity of the elicited modulations, often varying in phase and frequency on a short timescale, requires time-frequency measures that are better appropriate to analyze driven brain phenomena. Once entrainment is clearly established, one can assess the specificity of its expression, thus providing a better understanding of the physiology underlying brain modulation and a faster translation to treatment programs in various psychopathologic conditions.

Theta- and Gamma-Band Activity Discriminates Face, Body and Object Perception

Face and body perception is mediated by configural mechanisms, which allow the perception of these stimuli as a whole, rather than the sum of individual parts. Indirect measures of configural processing in visual cognition are the face and body inversion effects (FIE and BIE), which refer to the drop in performance when these stimuli are perceived upside-down. Albeit FIE and BIE have been well characterized at the behavioral level, much still needs to be understood in terms of the neurophysiological correlates of these effects. Thus, in the current study, the brain’s electrical activity has been recorded by a 128 channel electroencephalogram (EEG) in 24 healthy participants while perceiving (upright and inverted) faces, bodies and houses. EEG data were analyzed in both the time domain (i.e., event-related potentials—ERPs) and the frequency domain [i.e., induced theta (5–7 Hz) and gamma (28–45 Hz) oscillations]. ERPs amplitude results showed increased N170 amplitude for inverted faces and bodies (compared to the same stimuli presented in canonical position) but not for houses. ERPs latency results showed delayed N170 components for inverted (vs. upright) faces, houses, but not bodies. Spectral analysis of induced oscillations indicated physiological FIE and BIE; that is decreased gamma-band synchronization over right occipito-temporal electrodes for inverted (vs. upright) faces, and increased bilateral frontoparietal theta-band synchronization for inverted (vs. upright) faces. Furthermore, increased left occipito-temporal and right frontal theta-band synchronization for upright (vs. inverted) bodies was found. Our findings, thus, demonstrate clear differences in the neurophysiological correlates of face and body perception. The neurophysiological FIE suggests disruption of feature binding processes (decrease in occipital gamma oscillations for inverted faces), together with enhanced feature-based attention (increase in frontoparietal theta oscillations for inverted faces). In contrast, the BIE may suggest that structural encoding for bodies is mediated by the first stages of configural processing (decrease in occipital theta oscillations for inverted bodies).

Experience Transforms Conjunctive Object Representations: Neural Evidence for Unitization After Visual Expertise

Certain transformations must occur within the brain to allow rapid processing of familiar experiences. Complex objects are thought to become unitized, whereby multifeature conjunctions are retrieved as rapidly as a single feature. Behavioral studies strongly support unitization theory, but a compelling neural mechanism is lacking. Here, we examined how unitization transforms conjunctive representations to become more “feature-like” by recruiting posterior regions of the ventral visual stream (VVS) whose architecture is specialized for processing single features. We used functional magnetic resonance imaging to scan humans before and after visual training with novel objects. We implemented a novel multivoxel pattern analysis to measure a conjunctive code, which represented a conjunction of object features above and beyond the sum of the parts. Importantly, a multivoxel searchlight showed that the strength of conjunctive coding in posterior VVS increased posttraining. Furthermore, multidimensional scaling revealed representational separation at the level of individual features in parallel to the changes at the level of feature conjunctions. Finally, functional connectivity between anterior and posterior VVS was higher for novel objects than for trained objects, consistent with early involvement of anterior VVS in unitizing feature conjunctions in response to novelty. These data demonstrate that the brain implements unitization as a mechanism to refine complex object representations over the course of multiple learning experiences.

Spontaneous network activity <35 ​Hz accounts for variability in stimulus-induced gamma responses

Gamma activity is thought to serve several cognitive processes, including attention and memory. Even for the simplest stimulus, the occurrence of gamma activity is highly variable, both within and between individuals. The sources of this variability, however, are largely unknown. In this paper, we address one possible cause: the cross-frequency influence of spontaneous, whole-brain network activity on visual stimulus processing. By applying Hidden Markov modelling to MEG data, we reveal that the trial-averaged gamma response to a moving grating depends on the individual network dynamics, inferred from slower brain activity (<35 ​Hz) in the absence of stimulation (resting-state and task baseline). In addition, we demonstrate that modulations of network activity in task baseline influence the gamma response on the level of trials. In summary, our results reveal a cross-frequency and cross-session association between gamma responses induced by visual stimulation and spontaneous network activity. These findings underline the dependency of visual stimulus processing on the individual, functional network architecture.

Emergence of β and γ networks following multisensory training

Our perceptual reality relies on inferences about the causal structure of the world given by multiple sensory inputs. In ecological settings, multisensory events that cohere in time and space benefit inferential processes: hearing and seeing a speaker enhances speech comprehension, and the acoustic changes of flapping wings naturally pace the motion of a flock of birds. Here, we asked how a few minutes of (multi)sensory training could shape cortical interactions in a subsequent unisensory perceptual task. For this, we investigated oscillatory activity and functional connectivity as a function of individuals' sensory history during training. Human participants performed a visual motion coherence discrimination task while being recorded with magnetoencephalography. Three groups of participants performed the same task with visual stimuli only, while listening to acoustic textures temporally comodulated with the strength of visual motion coherence, or with auditory noise uncorrelated with visual motion. The functional connectivity patterns before and after training were contrasted to resting-state networks to assess the variability of common task-relevant networks, and the emergence of new functional interactions as a function of sensory history. One major finding is the emergence of a large-scale synchronization in the high γ (gamma: 60-120Hz) and β (beta: 15-30Hz) bands for individuals who underwent comodulated multisensory training. The post-training network involved prefrontal, parietal, and visual cortices. Our results suggest that the integration of evidence and decision-making strategies become more efficient following congruent multisensory training through plasticity in network routing and oscillatory regimes.

Oscillatory, Computational, and Behavioral Evidence for Impaired GABAergic Inhibition in Schizophrenia

The dysconnection hypothesis of schizophrenia (SZ) proposes that psychosis is best understood in terms of aberrant connectivity. Specifically, it suggests that dysconnectivity arises through aberrant synaptic modulation associated with deficits in GABAergic inhibition, excitation-inhibition balance and disturbances of high-frequency oscillations. Using a computational model combined with a graded-difficulty visual orientation discrimination paradigm, we demonstrate that, in SZ, perceptual performance is determined by the balance of excitation-inhibition in superficial cortical layers. Twenty-eight individuals with a DSM-IV diagnosis of SZ, and 30 age- and gender-matched healthy controls participated in a psychophysics orientation discrimination task, a visual grating magnetoencephalography (MEG) recording, and a magnetic resonance spectroscopy (MRS) scan for GABA. Using a neurophysiologically informed model, we quantified group differences in GABA, gamma measures, and the predictive validity of model parameters for orientation discrimination in the SZ group. MEG visual gamma frequency was reduced in SZ, with lower peak frequency associated with more severe negative symptoms. Orientation discrimination performance was impaired in SZ. Dynamic causal modeling of the MEG data showed that local synaptic connections were reduced in SZ and local inhibition correlated negatively with the severity of negative symptoms. The effective connectivity between inhibitory interneurons and superficial pyramidal cells predicted orientation discrimination performance within the SZ group; consistent with graded, behaviorally relevant, disease-related changes in local GABAergic connections. Occipital GABA levels were significantly reduced in SZ but did not predict behavioral performance or oscillatory measures. These findings endorse the importance, and behavioral relevance, of GABAergic synaptic disconnection in schizophrenia that underwrites excitation-inhibition balance.

Abnormal Monocular and Dichoptic Temporal Synchrony in Adults with Amblyopia

Purpose

We investigate temporal synchrony within one eye and between both eyes in adults with amblyopia.

Methods

Eight adult amblyopes (range, 19.88-27.81 years old; median, 22.86 years old) and 12 age-matched adults with normal vision (range, 21.2-50.30 years old; median, 23.78 years old) participated in the experiment. We showed two pairs of Gaussian blobs flickering at 1 Hz as visual stimuli, one pair with the same temporal phase modulation (i.e., the reference) and another pair with a distinct temporal phase (i.e., the signal). We employed the constant stimuli method to measure the minimum degree of temporal phase (temporal synchrony threshold), at which participants were able to discriminate the signal pair under binocular, monocular, and dichoptic viewing configurations.

Results

The temporal synchrony threshold was different across the six configurations (P = 0.001). There was also an interaction between the configuration and the group (P = 0.004). The synchrony threshold was significantly higher in amblyopes than in controls under the configurations where two pairs of blobs were presented to the amblyopic eye (136.52 ± 50.19 vs. 97.08 ± 22.02 ms, P = 0.027) and where the paired blobs were presented to different eyes (163.15 ± 80.85 vs. 111.61 ± 22.46 ms, P = 0.049). The visual deficits in these two configurations were significantly correlated (r = 0.824, P = 0.012).

Conclusions

The threshold for detecting temporal asynchrony increased when the stimuli were presented only to the amblyopic eye and when they were dichoptically presented to the amblyopic and fellow eyes.

Multi-domain Features of the Non-phase-locked Component of Interest Extracted from ERP Data by Tensor Decomposition

The waveform in the time domain, spectrum in the frequency domain, and topography in the space domain of component(s) of interest are the fundamental indices in neuroscience research. Despite the application of time–frequency analysis (TFA) to extract the temporal and spectral characteristics of non-phase-locked component (NPLC) of interest simultaneously, the statistical results are not always expectedly satisfying, in that the spatial information is not considered. Complex Morlet wavelet transform is widely applied to TFA of event-related-potential (ERP) data, and mother wavelet (which should be firstly defined by center frequency and bandwidth (CFBW) before using the method to TFA of ERP data) influences the time–frequency results. In this study, an optimal set of CFBW was firstly selected from the number sets of CFBW, to further analyze for TFA of the ERP data in a cognitive experiment paradigm of emotion (Anger and Neutral) and task (Go and Nogo). Then tensor decomposition algorithm was introduced to investigate the NPLC of interest from the fourth-order tensor. Compared with the TFA results which only revealed a significant difference between Go and Nogo task condition, the tensor-based analysis showed significant interaction effect between emotion and task. Moreover, significant differences were found in both emotion and task conditions through tensor decomposition. In addition, the statistical results of TFA would be affected by the selected region of interest (ROI), whereas those of the proposed method were not subject to ROI. Hence, this study demonstrated that tensor decomposition method was effective in extracting NPLC, by considering spatial information simultaneously as the potential to explore the brain mechanisms related to experimental design.

Portraits of communication in neuronal networks

The brain is organized as a network of highly specialized networks of spiking neurons. To exploit such a modular architecture for computation, the brain has to be able to regulate the flow of spiking activity between these specialized networks. In this Opinion article, we review various prominent mechanisms that may underlie communication between neuronal networks. We show that communication between neuronal networks can be understood as trajectories in a two-dimensional state space, spanned by the properties of the input. Thus, we propose a common framework to understand neuronal communication mediated by seemingly different mechanisms. We also suggest that the nesting of slow (for example, alpha-band and theta-band) oscillations and fast (gamma-band) oscillations can serve as an important control mechanism that allows or prevents spiking signals to be routed between specific networks. We argue that slow oscillations can modulate the time required to establish network resonance or entrainment and, thereby, regulate communication between neuronal networks.

A Distinct Class of Bursting Neurons with Strong Gamma Synchronization and Stimulus Selectivity in Monkey V1

Cortical computation depends on interactions between excitatory and inhibitory neurons. The contributions of distinct neuron types to sensory processing and network synchronization in primate visual cortex remain largely undetermined. We show that in awake monkey V1, there exists a distinct cell type (››30% of neurons) that has narrow-waveform (NW) action potentials and high spontaneous discharge rates and fires in high-frequency bursts. These neurons are more stimulus selective and phase locked to 30- to 80-Hz gamma oscillations than other neuron types. Unlike other neuron types, their gamma-phase locking is highly predictive of orientation tuning. We find evidence for strong rhythmic inhibition in these neurons, suggesting that they interact with interneurons to act as excitatory pacemakers for the V1 gamma rhythm. We did not find a similar class of NW bursting neurons in L2-L4 of mouse V1. Given its properties, this class of NW bursting neurons should be pivotal for the encoding and transmission of stimulus information.

Working Memory: Delay Activity, Yes! Persistent Activity? Maybe Not

Persistent spiking has been thought to underlie working memory (WM). However, virtually all of the evidence for this comes from studies that averaged spiking across time and across trials, which masks the details. On single trials, activity often occurs in sparse transient bursts. This has important computational and functional advantages. In addition, examination of more complex tasks reveals neural coding in WM is dynamic over the course of a trial. All this suggests that spiking is important for WM, but that its role is more complex than simply persistent spiking.Dual Perspectives Companion Paper:Persistent Spiking Activity Underlies Working Memory, by Christos Constantinidis, Shintaro Funahashi, Daeyeol Lee, John D. Murray, Xue-Lian Qi, Min Wang, and Amy F.T. Arnsten.

Diverse Neuron Properties and Complex Network Dynamics in the Cerebellar Cortical Inhibitory Circuit

Neuronal inhibition can be defined as a spatiotemporal restriction or suppression of local microcircuit activity. The importance of inhibition relies in its fundamental role in shaping signal processing in single neurons and neuronal circuits. In this context, the activity of inhibitory interneurons proved the key to endow networks with complex computational and dynamic properties. In the last 50 years, the prevailing view on the functional role of cerebellar cortical inhibitory circuits was that excitatory and inhibitory inputs sum spatially and temporally in order to determine the motor output through Purkinje cells (PCs). Consequently, cerebellar inhibition has traditionally been conceived in terms of restricting or blocking excitation. This assumption has been challenged, in particular in the cerebellar cortex where all neurons except granule cells (and unipolar brush cells in specific lobules) are inhibitory and fire spontaneously at high rates. Recently, a combination of electrophysiological recordings in vitro and in vivo, imaging, optogenetics and computational modeling, has revealed that inhibitory interneurons play a much more complex role in regulating cerebellar microcircuit functions: inhibition shapes neuronal response dynamics in the whole circuit and eventually regulate the PC output. This review elaborates current knowledge on cerebellar inhibitory interneurons [Golgi cells, Lugaro cells (LCs), basket cells (BCs) and stellate cells (SCs)], starting from their ontogenesis and moving up to their morphological, physiological and plastic properties, and integrates this knowledge with that on the more renown granule cells and PCs. We will focus on the circuit loops in which these interneurons are involved and on the way they generate feed-forward, feedback and lateral inhibition along with complex spatio-temporal response dynamics. In this perspective, inhibitory interneurons emerge as the real controllers of cerebellar functioning.

Elements of qualitative cognition: An information topology perspective

Elementary quantitative and qualitative aspects of consciousness are investigated conjointly from the biology, neuroscience, physic and mathematic point of view, by the mean of a theory written with Bennequin that derives and extends information theory within algebraic topology. Information structures, that accounts for statistical dependencies within n-body interacting systems are interpreted a la Leibniz as a monadic-panpsychic framework where consciousness is information and physical, and arise from collective interactions. The electrodynamic intrinsic nature of consciousness, sustained by an analogical code, is illustrated by standard neuroscience and psychophysic results. It accounts for the diversity of the learning mechanisms, including adaptive and homeostatic processes on multiple scales, and details their expression within information theory. The axiomatization and logic of cognition are rooted in measure theory expressed within a topos intrinsic probabilistic constructive logic. Information topology provides a synthesis of the main models of consciousness (Neural Assemblies, Integrated Information, Global Neuronal Workspace, Free Energy Principle) within a formal Gestalt theory, an expression of information structures and patterns in correspondence with Galois cohomology and discrete symmetries. The methods provide new formalization of deep neural network with homologicaly imposed architecture applied to challenges in AI-machine learning.

Functionally Distinct Gamma Range Activity Revealed by Stimulus Tuning in Human Visual Cortex

Neocortical gamma activity has long been hypothesized as a mechanism for synchronizing brain regions to support visual perception and cognition more broadly. Although early studies focused on narrowband gamma oscillations (∼20-60 Hz), recent work has emphasized a more broadband "high-gamma" response (∼70-150+ Hz). These responses are often conceptually or analytically treated as synonymous markers of gamma activity. Using high-density intracranial recordings from the human visual cortex, we challenge this view by showing distinct spectral, temporal, and functional properties of narrow and broadband gamma. Across four experiments, narrowband gamma was strongly selective for gratings and long-wavelength colors, displaying a delayed response onset, sustained temporal profile, and contrast-dependent peak frequency. In addition, induced narrowband gamma oscillations lacked phase consistency across stimulus repetitions and displayed highly focal inter-site synchronization. In contrast, broadband gamma was consistently observed for all presented stimuli, displaying a rapid response onset, transient temporal profile, and invariant spectral properties. We exploited stimulus tuning to highlight the functional dissociation of these distinct signals, reconciling prior inconsistencies across species and stimuli regarding the ubiquity of visual gamma oscillations during natural vision. The occurrence of visual narrowband gamma oscillations, unlike broadband high gamma, appears contingent on specific structural and chromatic stimulus attributes intersecting with the receptive field. Together, these findings have important implications for the study, analysis, and functional interpretation of neocortical gamma-range activity.

Regulation of gamma-frequency oscillation by feedforward inhibition: A computational modeling study

Throughout the brain, reciprocally connected excitatory and inhibitory neurons interact to produce gamma-frequency oscillations. The emergent gamma rhythm synchronizes local neural activity and helps to select which cells should fire in each cycle. We previously found that such excitation-inhibition microcircuits, however, have a potentially significant caveat: the frequency of the gamma oscillation and the level of selection (i.e., the percentage of cells that are allowed to fire) vary with the magnitude of the input signal. In networks with varying levels of brain activity, such a feature may produce undesirable instability on the time and spatial structure of the neural signal with a potential for adversely impacting important neural processing mechanisms. Here we propose that feedforward inhibition solves the latter instability problem of the excitation-inhibition microcircuit. Using computer simulations, we show that the feedforward inhibitory signal reduces the dependence of both the frequency of population oscillation and the level of selection on the magnitude of the input excitation. Such a mechanism can produce stable gamma oscillations with its frequency determined only by the properties of the feedforward network, as observed in the hippocampus. As feedforward and feedback inhibition motifs commonly appear together in the brain, we hypothesize that their interaction underlies a robust implementation of general computational principles of neural processing involved in several cognitive tasks, including the formation of cell assemblies and the routing of information between brain areas.

Persistent Gamma Spiking in SI Nonsensory Fast Spiking Cells Predicts Perceptual Success

Gamma oscillations (30-55 Hz) are hypothesized to temporally coordinate sensory encoding, enabling perception. However, fast spiking interneurons (FS), key gamma generators, can be highly sensory responsive, as is the gamma band local field potential (LFP). How can FS-mediated gamma act as an impartial temporal reference for sensory encoding, when the sensory drive itself presumably perturbs the pre-established rhythm? Combining tetrode recording in SI barrel cortex with controlled psychophysics, we found a unique FS subtype that was not sensory responsive and spiked regularly at gamma range intervals (gamma regular nonsensory FS [grnsFS]). Successful detection was predicted by a further increase in gamma regular spiking of grnsFS, persisting from before to after sensory onset. In contrast, broadband LFP power, including gamma, negatively predicted detection and did not cohere with gamma band spiking by grnsFS. These results suggest that a distinct FS subtype mediates perceptually relevant oscillations, independent of the LFP and sensory drive.

Brain Responses to Passive Sensory Stimulation Correlate With Intelligence

This study investigates the association between intelligence and brain power responses to a passive audiovisual stimulation. We measure the power of gamma-range steady-state responses (SSRs) as well as intelligence and other aspects of neurocognitive function in 40 healthy males born in 1953. The participants are a part of a Danish birth cohort study and the data therefore include additional information measured earlier in life. Our main power measure is the difference in power between a visual stimulation and a combined audiovisual stimulation. We hypothesize and establish empirically that the power measure is associated with intelligence. In particular, we find a highly significant correlation between the power measure and present intelligence scores. The association is robust to controlling for size-at-birth measures, length of education, speed of processing as well as a range of other potentially confounding factors. Interestingly, we find that intelligence scores measured earlier in life (childhood, youth, late midlife), are also correlated with the present-day power measure, suggesting a deep connection between intelligence and the power measure. Finally, we find that the power measure has a high sensitivity for detection of an intelligence score below the average.

Neural dynamics of spreading attentional labels in mental contour tracing

Behavioral and neural data suggest that visual attention spreads along contour segments to bind them into a unified object representation. Such attentional labeling segregates the target contour from distractors in a process known as mental contour tracing. A recurrent competitive map is developed to simulate the dynamics of mental contour tracing. In the model, local excitation opposes global inhibition and enables enhanced activity to propagate on the path offered by the contour. The extent of local excitatory interactions is modulated by the output of the multi-scale contour detection network, which constrains the speed of activity spreading in a scale-dependent manner. Furthermore, an L-junction detection network enables tracing to switch direction at the L-junctions, but not at the X- or T-junctions, thereby preventing spillover to a distractor contour. Computer simulations reveal that the model exhibits a monotonic increase in tracing time as a function of the distance to be traced. Also, the speed of tracing increases with decreasing proximity to the distractor contour and with the reduced curvature of the contours. The proposed model demonstrated how an elaborated version of the winner-takes-all network can implement a complex cognitive operation such as contour tracing.

The causal role of α-oscillations in feature binding

The binding problem-how to integrate features into objects-poses a fundamental challenge for the brain. Neural oscillations, especially γ-oscillations, have been proposed as a potential mechanism to solve this problem. However, since γ-oscillations usually reflect local neural activity, how to implement feature binding involving a large-scale brain network remains largely unknown. Here, combining electroencephalogram (EEG) and transcranial alternating current stimulation (tACS), we employed a bistable color-motion binding stimulus to probe the role of neural oscillations in feature binding. Subjects' perception of the stimulus switched between its physical binding and its illusory (active) binding. The active binding has been shown to involve a large-scale network consisting of spatially distant brain areas. α-Oscillations presumably reflect the dynamics of such large-scale networks, especially due to volume conduction effects in EEG. We found that, relative to the physical binding, the α-power decreased during the active binding. Additionally, individual α-power was negatively correlated with the time proportion of the active binding. Subjects' perceptual switch rate between the 2 bindings was positively correlated with their individual α-frequency. Furthermore, applying tACS at individual α-frequency decreased the time proportion of the active binding. Moreover, delivering tACS at different temporal frequencies in the α-band changed subjects' perceptual switch rate through affecting the active binding process. Our findings provide converging evidence for the causal role of α-oscillations in feature binding, especially in active feature binding, thereby uncovering a function of α-oscillations in human cognition.

Resolving the Electroencephalographic Correlates of Rapid Goal-Directed Chunking in the Frontal-Parietal Network

Previous studies have revealed a specific role of the prefrontal-parietal network in rapid goal-directed chunking (RGDC), which dissociates prefrontal activity related to chunking from parietal working memory demands. However, it remains unknown how the prefrontal and parietal cortices collaborate to accomplish RGDC. To this end, a novel experimental design was used that presented Chinese characters in a chunking task, testing eighteen undergraduate students (9 females, mean age = 22.4 years) while recoding the electroencephalogram (EEG). In the experiment, radical-level chunking was accomplished in a timely stringent way (RT = 1485 ms, SD = 371 ms), whereas the stroke-level chunking was accomplished less coherently (RT = 3278 ms, SD = 1083 ms). By comparing the differences between radical-level chunking vs. stroke-level chunking, we were able to dissociate the chunking processes in the radical-level chunking condition within the analyzed time window (-200 to 1300 ms). The chunking processes resulted in an early increase of gamma band synchronization over parietal and occipital cortices, followed by enhanced power in the beta-gamma band (25-38 Hz) over frontal areas. We suggest that the posterior rhythmic activities in the gamma band may underlie the processes that are directly associated with perceptual manipulations of chunking, while the subsequent beta-gamma activation over frontal areas appears to reflect a post-evaluation process such as reinforcement of the selected rules over alternative solutions, which may be an important characteristic of goal-directed chunking.

High-order coordination of cortical spiking activity modulates perceptual accuracy

The accurate relay of electrical signals within cortical networks is key to perception and cognitive function. Theoretically, it has long been proposed that temporal coordination of neuronal spiking activity controls signal transmission and behavior. However, whether and how temporally precise neuronal coordination in population activity influences perception are unknown. Here, we recorded populations of neurons in early and mid-level visual cortex (areas V1 and V4) simultaneously to discover that the precise temporal coordination between the spiking activity of three or more cells carries information about visual perception in the absence of firing rate modulation. The accuracy of perceptual responses correlated with high-order spiking coordination within V4, but not V1, and with feedforward coordination between V1 and V4. These results indicate that while visual stimuli are encoded in the discharge rates of neurons, perceptual accuracy is related to temporally precise spiking coordination within and between cortical networks.

Human visual cortical gamma reflects natural image structure

Many studies have reported visual cortical gamma-band activity related to stimulus processing and cognition. Most respective studies used artificial stimuli, and the few studies that used natural stimuli disagree. Electrocorticographic (ECoG) recordings from awake macaque areas V1 and V4 found gamma to be abundant during free viewing of natural images. In contrast, a study using ECoG recordings from V1 of human patients reported that many natural images induce no gamma and concluded that it is not necessary for seeing. To reconcile these apparently disparate findings, we reanalyzed those same human ECoG data recorded during presentation of natural images. We find that the strength of gamma is positively correlated with different image-computable metrics of image structure. This holds independently of the precise metric used to quantify gamma. In fact, an average of previously used gamma metrics reflects image structure most robustly. Gamma was sufficiently diagnostic of image structure to differentiate between any possible pair of images with >70% accuracy. Thus, while gamma might be weak for some natural images, the graded strength of gamma reflects the graded degree of image structure, and thereby conveys functionally relevant stimulus properties.

Effects of Inducing Gamma Oscillations in Hippocampal Subregions DG, CA3, and CA1 on the Potential Alleviation of Alzheimer's Disease-Related Pathology: Computer Modeling and Simulations

The aim of this study was to evaluate the possibility of the gamma oscillation function (40–130 Hz) to reduce Alzheimer’s disease related pathology in a computer model of the hippocampal network dentate gyrus, CA3, and CA1 (DG-CA3-CA1) regions. Methods: Computer simulations were made for a pathological model in which Alzheimer’s disease was simulated by synaptic degradation in the hippocampus. Pathology modeling was based on sequentially turning off the connections with entorhinal cortex layer 2 (EC2) and the dentate gyrus on CA3 pyramidal neurons. Gamma induction modeling consisted of simulating the oscillation provided by the septo-hippocampal pathway with band frequencies from 40–130 Hz. Pathological models with and without gamma induction were compared with a control. Results: In the hippocampal regions of DG, CA3, and CA1, and jointly DG-CA3-CA1 and CA3-CA1, gamma induction resulted in a statistically significant improvement in terms of increased numbers of spikes, spikes per burst, and burst duration as compared with the model simulating Alzheimer’s disease (AD). The positive maximal Lyapunov exponent was negative in both the control model and the one with gamma induction as opposed to the pathological model where it was positive within the DG-CA3-CA1 region. Gamma induction resulted in decreased transfer entropy in accordance with the information flow in DG → CA3 and CA3 → CA1. Conclusions: The results of simulation studies show that inducing gamma oscillations in the hippocampus may reduce Alzheimer’s disease related pathology. Pathologically higher transfer entropy values after gamma induction returned to values comparable to the control model.

SAE+LSTM: A New Framework for Emotion Recognition From Multi-Channel EEG

EEG-based automatic emotion recognition can help brain-inspired robots in improving their interactions with humans. This paper presents a novel framework for emotion recognition using multi-channel electroencephalogram (EEG). The framework consists of a linear EEG mixing model and an emotion timing model. Our proposed framework considerably decomposes the EEG source signals from the collected EEG signals and improves classification accuracy by using the context correlations of the EEG feature sequences. Specially, Stack AutoEncoder (SAE) is used to build and solve the linear EEG mixing model and the emotion timing model is based on the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). The framework was implemented on the DEAP dataset for an emotion recognition experiment, where the mean accuracy of emotion recognition achieved 81.10% in valence and 74.38% in arousal, and the effectiveness of our framework was verified. Our framework exhibited a better performance in emotion recognition using multi-channel EEG than the compared conventional approaches in the experiments.

A Role for Electrotonic Coupling Between Cortical Pyramidal Cells

Many brain regions communicate information through synchronized network activity. Electrical coupling among the dendrites of interneurons in the cortex has been implicated in forming and sustaining such activity in the cortex. Evidence for the existence of electrical coupling among cortical pyramidal cells, however, has been largely absent. A recent experimental study measured properties of electrical connections between pyramidal cells in the cortex deemed “electrotonic couplings.” These junctions were seen to occur pair-wise, sparsely, and often coexist with electrically-coupled interneurons. Here, we construct a network model to investigate possible roles for these rare, electrotonically-coupled pyramidal-cell pairs. Through simulations, we show that electrical coupling among pyramidal-cell pairs significantly enhances coincidence-detection capabilities and increases network spike-timing precision. Further, a network containing multiple pairs exhibits large variability in its firing pattern, possessing a rich coding structure.

Dynamic Computation in Visual Thalamocortical Networks

Contemporary neurodynamical frameworks, such as coordination dynamics and winnerless competition, posit that the brain approximates symbolic computation by transitioning between metastable attractive states. This article integrates these accounts with electrophysiological data suggesting that coherent, nested oscillations facilitate information representation and transmission in thalamocortical networks. We review the relationship between criticality, metastability, and representational capacity, outline existing methods for detecting metastable oscillatory patterns in neural time series data, and evaluate plausible spatiotemporal coding schemes based on phase alignment. We then survey the circuitry and the mechanisms underlying the generation of coordinated alpha and gamma rhythms in the primate visual system, with particular emphasis on the pulvinar and its role in biasing visual attention and awareness. To conclude the review, we begin to integrate this perspective with longstanding theories of consciousness and cognition.

Electrophysiological Brain Connectivity: Theory and Implementation

We review the theory and algorithms of electrophysiological brain connectivity analysis. This tutorial is aimed at providing an introduction to brain functional connectivity from electrophysiological signals, including electroencephalography (EEG), magnetoencephalography (MEG), electrocorticography (ECoG), stereoelectroencephalography (SEEG). Various connectivity estimators are discussed, and algorithms introduced. Important issues for estimating and mapping brain functional connectivity with electrophysiology are discussed.

Dissociating neural activity associated with the subjective phenomenology of monocular stereopsis: An EEG study

The subjective phenomenology associated with stereopsis, of solid tangible objects separated by a palpable negative space, is conventionally thought to be a by-product of the derivation of depth from binocular disparity. However, the same qualitative impression has been reported in the absence of disparity, e.g., when viewing pictorial images monocularly through an aperture. Here we aimed to explore if we could identify dissociable neural activity associated with the qualitative impression of stereopsis in the absence of the processing of binocular disparities. We measured EEG activity while subjects viewed pictorial (non-stereoscopic) images of 2D and 3D geometric forms under four different viewing conditions (binocular, monocular, binocular aperture, monocular aperture). EEG activity was analysed by oscillatory source localization (beamformer technique) to examine power change in occipital and parietal regions across viewing and stimulus conditions in targeted frequency bands (alpha: 8-13 Hz & gamma: 60-90 Hz). We observed expected event-related gamma synchronization and alpha desynchronization in occipital cortex and predominant gamma synchronization in parietal cortex across viewing and stimulus conditions. However, only the viewing condition predicted to generate the strongest impression of stereopsis (monocular aperture) revealed significantly elevated gamma synchronization within the parietal cortex for the critical contrasts (3D vs. 2D form). These findings suggest dissociable neural processes specific to the qualitative impression of stereopsis as distinguished from disparity processing.

The influence of retinal image motion on the perceptual grouping of temporally asynchronous stimuli

Briefly presented stimuli can reveal the lower limit of retinal-based perceptual stabilization mechanisms. This is demonstrated in perceptual grouping of temporally asynchronous stimuli, in which alternate row or column elements of a regular grid are presented over two successive display frames with an imperceptible temporal offset. The grouping phenomenon results from a subtle shift between alternate grid elements due to incomplete compensation of small, fixational eye movements occurring between the two presentation frames. This suggests that larger retinal shifts should amplify the introduced shifts between alternate grid elements and improve grouping performance. However, large shifts are necessarily absent in small eye movements. Furthermore, shifts follow a random walk, making the relationship between shift magnitude and performance difficult to explore systematically. Here, we established a systematic relationship between retinal image motion and perceptual grouping by presenting alternate grid elements (untracked) during smooth pursuit of known velocities. Our results show grouping performance to improve in direct proportion to pursuit velocity. Any potential compensation by extraretinal signals (e.g., efference copy) does not seem to occur.

Frequency-specific coupling in fronto-parieto-occipital cortical circuits underlie active tactile discrimination

Processing of tactile sensory information in rodents is critically dependent on the communication between the primary somatosensory cortex (S1) and higher-order integrative cortical areas. Here, we have simultaneously characterized single-unit activity and local field potential (LFP) dynamics in the S1, primary visual cortex (V1), anterior cingulate cortex (ACC), posterior parietal cortex (PPC), while freely moving rats performed an active tactile discrimination task. Simultaneous single unit recordings from all these cortical regions revealed statistically significant neuronal firing rate modulations during all task phases (anticipatory, discrimination, response, and reward). Meanwhile, phase analysis of pairwise LFP recordings revealed the occurrence of long-range synchronization across the sampled fronto-parieto-occipital cortical areas during tactile sampling. Causal analysis of the same pairwise recorded LFPs demonstrated the occurrence of complex dynamic interactions between cortical areas throughout the fronto-parietal-occipital loop. These interactions changed significantly between cortical regions as a function of frequencies (i.e. beta, theta and gamma) and according to the different phases of the behavioral task. Overall, these findings indicate that active tactile discrimination by rats is characterized by much more widespread and dynamic complex interactions within the fronto-parieto-occipital cortex than previously anticipated.

Visual Feature Integration of Three Attributes in Stimulus-Response Mapping Is Distinct From That of Two

In the human visual system, different attributes of an object are processed separately and are thought to be then temporarily bound by attention into an integrated representation to produce a specific response. However, if such representations existed in the brain for arbitrary multi-attribute objects, a combinatorial explosion problem would be unavoidable. Here, we show that attention may bind features of different attributes only in pairs and that bound feature pairs, rather than integrated object representations, are associated with responses for unfamiliar objects. We found that in a mapping task from three-attribute stimuli to responses, presenting three attributes in pairs (two attributes in each window) did not significantly complicate feature integration and response selection when the stimuli were not very familiar. We also found that repeated presentation of the same triple conjunctions significantly improved performance on the stimulus-response task when the correct responses were determined by the combination of three attributes, but this familiarity effect was not observed when the response could be determined by two attributes. These findings indicate that integration of three or more attributes is a distinct process from that of two, requiring long-term learning or some serial process. This suggests that integrated object representations are not formed or are formed only for a limited number of very familiar objects, which resolves the computational difficulty of the binding problem.