Somatosensory-Motor Adaptation of Orofacial Actions in Posterior Parietal and Ventral Premotor Cortices

Recent studies have provided evidence for sensory-motor adaptive changes and action goal coding of visually guided manual action in premotor and posterior parietal cortices. To extend these results to orofacial actions, devoid of auditory and visual feedback, we used a repetition suppression paradigm while measuring neural activity with functional magnetic resonance imaging during repeated intransitive and silent lip, jaw and tongue movements. In the motor domain, this paradigm refers to decreased activity in specific neural populations due to repeated motor acts and has been proposed to reflect sensory-motor adaptation. Orofacial movements activated a set of largely overlapping, common brain areas forming a core neural network classically involved in orofacial motor control. Crucially, suppressed neural responses during repeated orofacial actions were specifically observed in the left ventral premotor cortex, the intraparietal sulcus, the inferior parietal lobule and the superior parietal lobule. Since no visual and auditory feedback were provided during orofacial actions, these results suggest somatosensory-motor adaptive control of intransitive and silent orofacial actions in these premotor and parietal regions.     

The interplay of attention and consciousness in visual search,attentional blink and working memory consolidation

Despite the acknowledged relationship between consciousness and attention, theories of the two have mostly been developed separately. Moreover, these theories have independently attempted to explain phenomena in which both are likely to interact, such as the attentional blink (AB) and working memory (WM) consolidation. Here, we make an effort to bridge the gap between, on the one hand, a theory of consciousness based on the notion of global workspace (GW) and, on the other, a synthesis of theories of visual attention. We offer a theory of attention and consciousness (TAC) that provides a unified neurocognitive account of several phenomena associated with visual search, AB and WM consolidation. TAC assumes multiple processing stages between early visual representation and conscious access, and extends the dynamics of the global neuronal workspace model to a visual attentional workspace (VAW). The VAW is controlled by executive routers, higher-order representations of executive operations in the GW, without the need for explicit saliency or priority maps. TAC leads to newly proposed mechanisms for illusory conjunctions, AB, inattentional blindness and WM capacity, and suggests neural correlates of phenomenal consciousness. Finally, the theory reconciles the all-or-none and graded perspectives on conscious representation.     

A neurodynamic account of spontaneous behaviour

The current article suggests that deterministic chaos self-organized in cortical dynamics could be responsible for the generation of spontaneous action sequences. Recently, various psychological observations have suggested that humans and primates can learn to extract statistical structures hidden in perceptual sequences experienced during active environmental interactions. Although it has been suggested that such statistical structures involve chunking or compositional primitives, their neuronal implementations in brains have not yet been clarified. Therefore, to reconstruct the phenomena, synthetic neuro-robotics experiments were conducted by using a neural network model, which is characterized by a generative model with intentional states and its multiple timescales dynamics. The experimental results showed that the robot successfully learned to imitate tutored behavioral sequence patterns by extracting the underlying transition probability among primitive actions. An analysis revealed that a set of primitive action patterns was embedded in the fast dynamics part, and the chaotic dynamics of spontaneously sequencing these action primitive patterns was structured in the slow dynamics part, provided that the timescale was adequately set for each part. It was also shown that self-organization of this type of functional hierarchy ensured robust action generation by the robot in its interactions with a noisy environment. This article discusses the correspondence of the synthetic experiments with the known hierarchy of the prefrontal cortex, the supplementary motor area, and the primary motor cortex for action generation. We speculate that deterministic dynamical structures organized in the prefrontal cortex could be essential because they can account for the generation of both intentional behaviors of fixed action sequences and spontaneous behaviors of pseudo-stochastic action sequences by the same mechanism.     

Attending to the possibilities of action

Actions taking place in the environment are critical for our survival. We review evidence on attention to action, drawing on sets of converging evidence from neuropsychological patients through to studies of the time course and neural locus of action-based cueing of attention in normal observers. We show that the presence of action relations between stimuli helps reduce visual extinction in patients with limited attention to the contralesional side of space, while the first saccades made by normal observers and early perceptual and attentional responses measured using electroencephalography/event-related potentials are modulated by preparation of action and by seeing objects being grasped correctly or incorrectly for action. With both normal observers and patients, there is evidence for two components to these effects based on both visual perceptual and motor-based responses. While the perceptual responses reflect factors such as the visual familiarity of the action-related information, the motor response component is determined by factors such as the alignment of the objects with the observer''s effectors and not by the visual familiarity of the stimuli. In addition to this, we suggest that action relations between stimuli can be coded pre-attentively, in the absence of attention to the stimulus, and action relations cue perceptual and motor responses rapidly and automatically. At present, formal theories of visual attention are not set up to account for these action-related effects; we suggest ways that theories could be expected to enable action effects to be incorporated.     

Emergence of symmetric, modular, and reciprocal connections in recurrent networks with Hebbian learning

While learning and development are well characterized in feedforward networks, these features are more difficult to analyze in recurrent networks due to the increased complexity of dual dynamics – the rapid dynamics arising from activation states and the slow dynamics arising from learning or developmental plasticity. We present analytical and numerical results that consider dual dynamics in a recurrent network undergoing Hebbian learning with either constant weight decay or weight normalization. Starting from initially random connections, the recurrent network develops symmetric or near-symmetric connections through Hebbian learning. Reciprocity and modularity arise naturally through correlations in the activation states. Additionally, weight normalization may be better than constant weight decay for the development of multiple attractor states that allow a diverse representation of the inputs. These results suggest a natural mechanism by which synaptic plasticity in recurrent networks such as cortical and brainstem premotor circuits could enhance neural computation and the generation of motor programs. Received: 27 April 1998 / Accepted in revised form: 16 March 1999     

Attention driven phantom vision: measuring the sensory strength of attentional templates and their relation to visual mental imagery and aphantasia

When we search for an object in an array or anticipate attending to a future object, we create an ‘attentional template'' of the object. The definitions of attentional templates and visual imagery share many similarities as well as many of the same neural characteristics. However, the phenomenology of these attentional templates and their neural similarities to visual imagery and perception are rarely, if ever discussed. Here, we investigate the relationship between these two forms of non-retinal phantom vision through the use of the binocular rivalry technique, which allows us to measure the sensory strength of attentional templates in the absence of concurrent perceptual stimuli. We find that attentional templates correlate with both feature-based attention and visual imagery. Attentional templates, like imagery, were significantly disrupted by the presence of irrelevant visual stimuli, while feature-based attention was not. We also found that a special population who lack the ability to visualize (aphantasia), showed evidence of feature-based attention when measured using the binocular rivalry paradigm, but not attentional templates. Taken together, these data suggest functional similarities between attentional templates and visual imagery, advancing the theory of visual imagery as a general simulation tool used across cognition.This article is part of the theme issue ‘Offline perception: voluntary and spontaneous perceptual experiences without matching external stimulation’.     

Emergence of functional hierarchy in a multiple timescale neural network model: a humanoid robot experiment

It is generally thought that skilled behavior in human beings results from a functional hierarchy of the motor control system, within which reusable motor primitives are flexibly integrated into various sensori-motor sequence patterns. The underlying neural mechanisms governing the way in which continuous sensori-motor flows are segmented into primitives and the way in which series of primitives are integrated into various behavior sequences have, however, not yet been clarified. In earlier studies, this functional hierarchy has been realized through the use of explicit hierarchical structure, with local modules representing motor primitives in the lower level and a higher module representing sequences of primitives switched via additional mechanisms such as gate-selecting. When sequences contain similarities and overlap, however, a conflict arises in such earlier models between generalization and segmentation, induced by this separated modular structure. To address this issue, we propose a different type of neural network model. The current model neither makes use of separate local modules to represent primitives nor introduces explicit hierarchical structure. Rather than forcing architectural hierarchy onto the system, functional hierarchy emerges through a form of self-organization that is based on two distinct types of neurons, each with different time properties ("multiple timescales"). Through the introduction of multiple timescales, continuous sequences of behavior are segmented into reusable primitives, and the primitives, in turn, are flexibly integrated into novel sequences. In experiments, the proposed network model, coordinating the physical body of a humanoid robot through high-dimensional sensori-motor control, also successfully situated itself within a physical environment. Our results suggest that it is not only the spatial connections between neurons but also the timescales of neural activity that act as important mechanisms leading to functional hierarchy in neural systems.     

Spiking behavior and epileptiform oscillations in a discrete model of cortical neural networks

Summary We investigate the phenomenon of epileptiform activity using a discrete model of cortical neural networks. Our model is reduced to the elementary features of neurons and assumes simplified dynamics of action potentials and postsynaptic potentials. The discrete model provides a comparably high simulation speed which allows the rendering of phase diagrams and simulations of large neural networks in reasonable time. Further the reduction to the basic features of neurons provides insight into the essentials of a possible mechanism of epilepsy. Our computer simulations suggest that the detailed dynamics of postsynaptic and action potentials are not indispensable for obtaining epileptiform behavior on the system level. The simulation results of autonomously evolving networks exhibit a regime in which the network dynamics spontaneously switch between fluctuating and oscillating behavior and produce isolated network spikes without external stimulation. Inhibitory neurons have been found to play an important part in the synchronization of neural firing: an increased number of synapses established by inhibitory neurons onto other neurons induces a transition to the spiking regime. A decreased frequency accompanying the hypersynchronous population activity has only occurred with slow inhibitory postsynaptic potentials.     

Coherent interaction of dynamical attractors for object-based selective attention

I investigate essential neuronal mechanisms of visual attention based on object-based theory and a biased-competition scheme. A neural network model is proposed that consists of two feature networks, FI and FII, and one object network, OJ. The FI and FII networks send feedforward projections to the OJ network and receive feedback projections from the OJ network in a convergent/divergent manner. The OJ network integrates information about sensory features originated from the FI and FII networks into information about objects. I let the feature networks and the object network memorize individual features and objects according to the Hebbian learning rule and create the point attractors corresponding to these features and objects as long-term memories in the network dynamics. When the model tries to attend to objects that are superimposed, the point attractors relevant to the two objects emerge in each network. After a short interval (hundreds of milliseconds), the point attractors relevant to one of the two objects are selected and the other point attractors are completely suppressed. I suggest that coherent interactions of dynamical attractors relevant to the selected object may be the neuronal substrate for object-based selective attention. Bottom-up (FI-to-OJ and FI-to-OJ) neuronal mechanisms separate candidate objects from the background, and top-down (OJ-to-FI and OJ-to-FII) mechanisms resolve object-competition by which one relevant object is selected from candidate objects.     

Neural network modelling of the influence of channelopathies on reflex visual attention

This paper introduces a model of Emergent Visual Attention in presence of calcium channelopathy (EVAC). By modelling channelopathy, EVAC constitutes an effort towards identifying the possible causes of autism. The network structure embodies the dual pathways model of cortical processing of visual input, with reflex attention as an emergent property of neural interactions. EVAC extends existing work by introducing attention shift in a larger-scale network and applying a phenomenological model of channelopathy. In presence of a distractor, the channelopathic network’s rate of failure to shift attention is lower than the control network’s, but overall, the control network exhibits a lower classification error rate. The simulation results also show differences in task-relative reaction times between control and channelopathic networks. The attention shift timings inferred from the model are consistent with studies of attention shift in autistic children.     

Reconstructing feedback representations in the ventral visual pathway with a generative adversarial autoencoder

While vision evokes a dense network of feedforward and feedback neural processes in the brain, visual processes are primarily modeled with feedforward hierarchical neural networks, leaving the computational role of feedback processes poorly understood. Here, we developed a generative autoencoder neural network model and adversarially trained it on a categorically diverse data set of images. We hypothesized that the feedback processes in the ventral visual pathway can be represented by reconstruction of the visual information performed by the generative model. We compared representational similarity of the activity patterns in the proposed model with temporal (magnetoencephalography) and spatial (functional magnetic resonance imaging) visual brain responses. The proposed generative model identified two segregated neural dynamics in the visual brain. A temporal hierarchy of processes transforming low level visual information into high level semantics in the feedforward sweep, and a temporally later dynamics of inverse processes reconstructing low level visual information from a high level latent representation in the feedback sweep. Our results append to previous studies on neural feedback processes by presenting a new insight into the algorithmic function and the information carried by the feedback processes in the ventral visual pathway.     

Bottom�Cup attention: pulsed PCA transform and pulsed cosine transform

In this paper we propose a computational model of bottom–up visual attention based on a pulsed principal component analysis (PCA) transform, which simply exploits the signs of the PCA coefficients to generate spatial and motional saliency. We further extend the pulsed PCA transform to a pulsed cosine transform that is not only data-independent but also very fast in computation. The proposed model has the following biological plausibilities. First, the PCA projection vectors in the model can be obtained by using the Hebbian rule in neural networks. Second, the outputs of the pulsed PCA transform, which are inherently binary, simulate the neuronal pulses in the human brain. Third, like many Fourier transform-based approaches, our model also accomplishes the cortical center-surround suppression in frequency domain. Experimental results on psychophysical patterns and natural images show that the proposed model is more effective in saliency detection and predict human eye fixations better than the state-of-the-art attention models.     

Resting-State Temporal Synchronization Networks Emerge from Connectivity Topology and Heterogeneity

Spatial patterns of coherent activity across different brain areas have been identified during the resting-state fluctuations of the brain. However, recent studies indicate that resting-state activity is not stationary, but shows complex temporal dynamics. We were interested in the spatiotemporal dynamics of the phase interactions among resting-state fMRI BOLD signals from human subjects. We found that the global phase synchrony of the BOLD signals evolves on a characteristic ultra-slow (<0.01Hz) time scale, and that its temporal variations reflect the transient formation and dissolution of multiple communities of synchronized brain regions. Synchronized communities reoccurred intermittently in time and across scanning sessions. We found that the synchronization communities relate to previously defined functional networks known to be engaged in sensory-motor or cognitive function, called resting-state networks (RSNs), including the default mode network, the somato-motor network, the visual network, the auditory network, the cognitive control networks, the self-referential network, and combinations of these and other RSNs. We studied the mechanism originating the observed spatiotemporal synchronization dynamics by using a network model of phase oscillators connected through the brain’s anatomical connectivity estimated using diffusion imaging human data. The model consistently approximates the temporal and spatial synchronization patterns of the empirical data, and reveals that multiple clusters that transiently synchronize and desynchronize emerge from the complex topology of anatomical connections, provided that oscillators are heterogeneous.     

A Spike-Timing Pattern Based Neural Network Model for the Study of Memory Dynamics

It is well accepted that the brain''s computation relies on spatiotemporal activity of neural networks. In particular, there is growing evidence of the importance of continuously and precisely timed spiking activity. Therefore, it is important to characterize memory states in terms of spike-timing patterns that give both reliable memory of firing activities and precise memory of firing timings. The relationship between memory states and spike-timing patterns has been studied empirically with large-scale recording of neuron population in recent years. Here, by using a recurrent neural network model with dynamics at two time scales, we construct a dynamical memory network model which embeds both fast neural and synaptic variation and slow learning dynamics. A state vector is proposed to describe memory states in terms of spike-timing patterns of neural population, and a distance measure of state vector is defined to study several important phenomena of memory dynamics: partial memory recall, learning efficiency, learning with correlated stimuli. We show that the distance measure can capture the timing difference of memory states. In addition, we examine the influence of network topology on learning ability, and show that local connections can increase the network''s ability to embed more memory states. Together theses results suggest that the proposed system based on spike-timing patterns gives a productive model for the study of detailed learning and memory dynamics.     

视觉注意与神经振荡

人脑每时每刻都要接收大量视觉信息，由于人脑加工信息的能力有限，所以在较大视野内将注意分配给相关信息，同时抑制引起注意分散的不相关信息，对执行目标导向的行为至关重要。这种对视觉信息的选择性和主动性加工以适应当前目标的过程被称作视觉注意（visual attention），且视觉注意可分为自上而下的注意与自下而上的注意两种不同功能。由于来自大脑电信号的神经振荡活动在认知加工中发挥重要作用，已有研究综述了视觉注意与神经振荡（neural oscillation）的密切关系，但并未涉及不同的注意功能与神经振荡的关系。本文系统性调查了不同注意功能与神经振荡的关系，发现额-顶区域的theta频带振荡活动反映了自上而下的认知控制，而后部脑区的theta振荡与自下而上的注意相关。顶-枕区域alpha振荡的偏侧化有助于注意分配，而alpha频带的大规模同步促成了注意对视皮层自上而下的影响。Beta振荡介导了自上而下的信息与自下而上的信息之间的互动，作为信息载体促进了视觉信息处理。Gamma振荡则可能与自上而下和自下而上的注意间整合相关。本文就视觉注意功能与神经振荡关系的研究现状展开综述，旨在揭示不同的神经振荡活动在特定的视觉注意功能中的作用。     

The Wisdom of Networks: A General Adaptation and Learning Mechanism of Complex Systems

I hypothesize that re‐occurring prior experience of complex systems mobilizes a fast response, whose attractor is encoded by their strongly connected network core. In contrast, responses to novel stimuli are often slow and require the weakly connected network periphery. Upon repeated stimulus, peripheral network nodes remodel the network core that encodes the attractor of the new response. This “core‐periphery learning” theory reviews and generalizes the heretofore fragmented knowledge on attractor formation by neural networks, periphery‐driven innovation, and a number of recent reports on the adaptation of protein, neuronal, and social networks. The core‐periphery learning theory may increase our understanding of signaling, memory formation, information encoding and decision‐making processes. Moreover, the power of network periphery‐related “wisdom of crowds” inventing creative, novel responses indicates that deliberative democracy is a slow yet efficient learning strategy developed as the success of a billion‐year evolution. Also see the video abstract here: https://youtu.be/IIjP7zWGjVE .     

Motor Imagery Learning Modulates Functional Connectivity of Multiple Brain Systems in Resting State

Background

Learning motor skills involves subsequent modulation of resting-state functional connectivity in the sensory-motor system. This idea was mostly derived from the investigations on motor execution learning which mainly recruits the processing of sensory-motor information. Behavioral evidences demonstrated that motor skills in our daily lives could be learned through imagery procedures. However, it remains unclear whether the modulation of resting-state functional connectivity also exists in the sensory-motor system after motor imagery learning.

Methodology/Principal Findings

We performed a fMRI investigation on motor imagery learning from resting state. Based on previous studies, we identified eight sensory and cognitive resting-state networks (RSNs) corresponding to the brain systems and further explored the functional connectivity of these RSNs through the assessments, connectivity and network strengths before and after the two-week consecutive learning. Two intriguing results were revealed: (1) The sensory RSNs, specifically sensory-motor and lateral visual networks exhibited greater connectivity strengths in precuneus and fusiform gyrus after learning; (2) Decreased network strength induced by learning was proved in the default mode network, a cognitive RSN.

Conclusions/Significance

These results indicated that resting-state functional connectivity could be modulated by motor imagery learning in multiple brain systems, and such modulation displayed in the sensory-motor, visual and default brain systems may be associated with the establishment of motor schema and the regulation of introspective thought. These findings further revealed the neural substrates underlying motor skill learning and potentially provided new insights into the therapeutic benefits of motor imagery learning.     

FMRI supports the sensorimotor theory of motor resonance

The neural mechanisms mediating the activation of the motor system during action observation, also known as motor resonance, are of major interest to the field of motor control. It has been proposed that motor resonance develops in infants through Hebbian plasticity of pathways connecting sensory and motor regions that fire simultaneously during imitation or self movement observation. A fundamental problem when testing this theory in adults is that most experimental paradigms involve actions that have been overpracticed throughout life. Here, we directly tested the sensorimotor theory of motor resonance by creating new visuomotor representations using abstract stimuli (motor symbols) and identifying the neural networks recruited through fMRI. We predicted that the network recruited during action observation and execution would overlap with that recruited during observation of new motor symbols. Our results indicate that a network consisting of premotor and posterior parietal cortex, the supplementary motor area, the inferior frontal gyrus and cerebellum was activated both by new motor symbols and by direct observation of the corresponding action. This tight spatial overlap underscores the importance of sensorimotor learning for motor resonance and further indicates that the physical characteristics of the perceived stimulus are irrelevant to the evoked response in the observer.     

Global asymptotic stability of a class of dynamical neural networks

The dynamics of cortical cognitive maps developed by self-organization must include the aspects of long and short-term memory. The behavior of the network is such characterized by an equation of neural activity as a fast phenomenon and an equation of synaptic modification as a slow part of the neural biologically relevant system. We present new stability conditions for analyzing the dynamics of a biological relevant system with different time scales based on the theory of flow invariance. We prove the existence and uniqueness of the equilibrium, and give a quadratic-type Lyapunov function for the flow of a competitive neural system with fast and slow dynamic variables and thus prove the global stability of the equilibrium point.     

An Attractor-Based Complexity Measurement for Boolean Recurrent Neural Networks

We provide a novel refined attractor-based complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics. This complexity measurement is achieved by first proving a computational equivalence between Boolean recurrent neural networks and some specific class of -automata, and then translating the most refined classification of -automata to the Boolean neural network context. As a result, a hierarchical classification of Boolean neural networks based on their attractive dynamics is obtained, thus providing a novel refined attractor-based complexity measurement for Boolean recurrent neural networks. These results provide new theoretical insights to the computational and dynamical capabilities of neural networks according to their attractive potentialities. An application of our findings is illustrated by the analysis of the dynamics of a simplified model of the basal ganglia-thalamocortical network simulated by a Boolean recurrent neural network. This example shows the significance of measuring network complexity, and how our results bear new founding elements for the understanding of the complexity of real brain circuits.