The Parieto-Frontal Integration Theory (P-FIT) of intelligence: converging neuroimaging evidence

"Is there a biology of intelligence which is characteristic of the normal human nervous system?" Here we review 37 modern neuroimaging studies in an attempt to address this question posed by Halstead (1947) as he and other icons of the last century endeavored to understand how brain and behavior are linked through the expression of intelligence and reason. Reviewing studies from functional (i.e., functional magnetic resonance imaging, positron emission tomography) and structural (i.e., magnetic resonance spectroscopy, diffusion tensor imaging, voxel-based morphometry) neuroimaging paradigms, we report a striking consensus suggesting that variations in a distributed network predict individual differences found on intelligence and reasoning tasks. We describe this network as the Parieto-Frontal Integration Theory (P-FIT). The P-FIT model includes, by Brodmann areas (BAs): the dorsolateral prefrontal cortex (BAs 6, 9, 10, 45, 46, 47), the inferior (BAs 39, 40) and superior (BA 7) parietal lobule, the anterior cingulate (BA 32), and regions within the temporal (BAs 21, 37) and occipital (BAs 18, 19) lobes. White matter regions (i.e., arcuate fasciculus) are also implicated. The P-FIT is examined in light of findings from human lesion studies, including missile wounds, frontal lobotomy/leukotomy, temporal lobectomy, and lesions resulting in damage to the language network (e.g., aphasia), as well as findings from imaging research identifying brain regions under significant genetic control. Overall, we conclude that modern neuroimaging techniques are beginning to articulate a biology of intelligence. We propose that the P-FIT provides a parsimonious account for many of the empirical observations, to date, which relate individual differences in intelligence test scores to variations in brain structure and function. Moreover, the model provides a framework for testing new hypotheses in future experimental designs.     

Interactions between astrocytes and the reward-attention circuit: A model for attention focusing in the presence of nicotine

Recent studies in neurophysiology suggest that astrocytes—a specific type of glial cells in the central nervous system—perform dynamical signaling, integrating neural inputs and regulating synaptic transmissions. This work presents a mathematical model for bidirectional signaling between astrocytes and neurons, investigating the functional role of such glial cells in a neural network that simulates the influence of nicotine on attentional focus. Considering the neurons’ firing frequency as an indicator of analysis, our results indicate that the tripartite synaptic transmission substantially changes the network activity, in comparison to the bipartite synapse. In addition, we show that this effect occurs specifically due to inclusion of astrocytes, corroborating experimental findings that show astrocytes improve of transmission performance in neural networks. Moreover, our simulations contribute to a better understanding of the astrocytary role in brain function and of synaptic transmission in a neuroglia network.     

Brain network interactions in auditory, visual and linguistic processing

In the paper, we discuss the importance of network interactions between brain regions in mediating performance of sensorimotor and cognitive tasks, including those associated with language processing. Functional neuroimaging, especially PET and fMRI, provide data that are obtained essentially simultaneously from much of the brain, and thus are ideal for enabling one to assess interregional functional interactions. Two ways to use these types of data to assess network interactions are presented. First, using PET, we demonstrate that anterior and posterior perisylvian language areas have stronger functional connectivity during spontaneous narrative production than during other less linguistically demanding production tasks. Second, we show how one can use large-scale neural network modeling to relate neural activity to the hemodynamically-based data generated by fMRI and PET. We review two versions of a model of object processing - one for visual and one for auditory objects. The regions comprising the models include primary and secondary sensory cortex, association cortex in the temporal lobe, and prefrontal cortex. Each model incorporates specific assumptions about how neurons in each of these areas function, and how neurons in the different areas are interconnected with each other. Each model is able to perform a delayed match-to-sample task for simple objects (simple shapes for the visual model; tonal contours for the auditory model). We find that the simulated electrical activities in each region are similar to those observed in nonhuman primates performing analogous tasks, and the absolute values of the simulated integrated synaptic activity in each brain region match human fMRI/PET data. Thus, this type of modeling provides a way to understand the neural bases for the sensorimotor and cognitive tasks of interest.     

The biological efficacy of the transfer function

The most popular neural network strategy is back propagation. This strategy initiated general interest in neural networks among researchers. While back propagation can solve nonlinear problems, it is considered to be a poor example of neuron functioning. Recently, Gardner (1993) has made a strong case for a back propagating phenomenon in networks of living neurons. In this paper, we present a few simple computational examples that investigate another component of the typical back propagation network. The effects of varying transfer functions are illustrated along with the resulting variations in possible synaptic weights. Graphic presentations in 3-D space of the relationship between transfer functions and synaptic weights suggest neural analogies of cell-firing rate and network control.     

Evoked potentials and stages of information processing: Methods of analysis

The use of principal components analysis (PCA) for the study of evoked-response data may be complicated by variations from one trial to another in the latency of underlying brain events. Such variation can come from either random intra-and intersubject variability or from the effects of independent variables that are manipulated between conditions. The effect of such variability is investigated by simulation of these latency-varying events and by analysis of evoked responses in a behavioral task, the Sternberg memory search task, which is well known to generate variation in the latency of brain events. The results of PCA of within-subjects differences in these two situations are plausibly related to underlying stages of information processing, and the technique may augment reaction time data by providing information on the time of occurrence as well as the duration of stages of information processing.     

智能发育与退化的神经网络基础

智能是人类最有特色的行为,正是通过智能活动,人类对自然环境进行了杰出的控制。智能与大脑皮层中大量的神经回路有关。本文讨论了智能赖以存在的基础-神经系统的发育,形成构筑和退化的过程,及其与智能的关系。神经生长锥的延长,分支,迁移到射靶运动,导致突触的形成和可塑性到神经联系的建立,关注局部神经回路到功能神经网络的形态构筑和功能特点,以及神经网络损伤对智能的影响。     

From Blickets to Synapses: Inferring Temporal Causal Networks by Observation

How do human infants learn the causal dependencies between events? Evidence suggests that this remarkable feat can be achieved by observation of only a handful of examples. Many computational models have been produced to explain how infants perform causal inference without explicit teaching about statistics or the scientific method. Here, we propose a spiking neuronal network implementation that can be entrained to form a dynamical model of the temporal and causal relationships between events that it observes. The network uses spike‐time dependent plasticity, long‐term depression, and heterosynaptic competition rules to implement Rescorla–Wagner‐like learning. Transmission delays between neurons allow the network to learn a forward model of the temporal relationships between events. Within this framework, biologically realistic synaptic plasticity rules account for well‐known behavioral data regarding cognitive causal assumptions such as backwards blocking and screening‐off. These models can then be run as emulators for state inference. Furthermore, this mechanism is capable of copying synaptic connectivity patterns between neuronal networks by observing the spontaneous spike activity from the neuronal circuit that is to be copied, and it thereby provides a powerful method for transmission of circuit functionality between brain regions.     

Cellular memory in spinal nociceptive circuitry

Besides transmitting and processing, neurons may also store information for prolonged periods of time (e.g. by use-dependent change in synaptic strength). In 1966 long-term potentiation (LTP) of synaptic transmission was discovered in the hippocampus, an area implicated in learning and memory. Recent studies show that similar mechanisms apply to pain pathways, at least in the spinal cord, and may account for some forms of clinical problems like hyperalgesia, allodynia, and deafferentation pain states, such as phantom pain. In this review, we briefly summarize key aspects of synaptic plasticity known from the brain and in the spinal cord. Then we describe and discuss related changes in spinal nociceptive neurons based on results from our own laboratory.     

Research on intelligent cognitive function enhancement of intelligent robot based on ant colony algorithm

In this paper, ant colony algorithm is studied to improve the visual cognitive function of intelligent robots. Based on the detailed understanding of the research status in this field at home and abroad, and learning from cognitive science and neurobiology research results, a solution is proposed from the perspective of ant colony algorithm based on human brain structure and function. By simulating the process of autonomous learning controlled by human long-term memory and its working memory, a visual strangeness-driven growth long-term memory autonomous learning algorithm is proposed. This method takes incremental self-organizing network as long-term memory structure, and combines with visual strangeness internal motivation Q learning method in working memory. The visual knowledge acquired by self-learning is accumulated into long-term memory continuously, thus realizing the ability of self-learning, memory and intelligence development similar to human beings. The experimental results show that the robot can learn visual knowledge independently, store and update knowledge incrementally, and improve its intelligence development, classification and recognition ability compared with the method without long-term memory. At the same time, the generalization ability and knowledge expansion ability are also improved.     

Model for a flexible motor memory based on a self-active recurrent neural network

Using recent recurrent network architecture based on the reservoir computing approach, we propose and numerically simulate a model that is focused on the aspects of a flexible motor memory for the storage of elementary movement patterns into the synaptic weights of a neural network, so that the patterns can be retrieved at any time by simple static commands. The resulting motor memory is flexible in that it is capable to continuously modulate the stored patterns. The modulation consists in an approximately linear inter- and extrapolation, generating a large space of possible movements that have not been learned before. A recurrent network of thousand neurons is trained in a manner that corresponds to a realistic exercising scenario, with experimentally measured muscular activations and with kinetic data representing proprioceptive feedback. The network is “self-active” in that it maintains recurrent flow of activation even in the absence of input, a feature that resembles the “resting-state activity” found in the human and animal brain. The model involves the concept of “neural outsourcing” which amounts to the permanent shifting of computational load from higher to lower-level neural structures, which might help to explain why humans are able to execute learned skills in a fluent and flexible manner without the need for attention to the details of the movement.     

Correlations between intelligence and components of serial timing variability

Psychometric intelligence correlates with reaction time in elementary cognitive tasks, as well as with performance in time discrimination and judgment tasks. It has remained unclear, however, to what extent these correlations are due to top–down mechanisms, such as attention, and bottom–up mechanisms, i.e. basic neural properties that influence both temporal accuracy and cognitive processes. Here, we assessed correlations between intelligence (Raven SPM Plus) and performance in isochronous serial interval production, a simple, automatic timing task where participants first make movements in synchrony with an isochronous sequence of sounds and then continue with self-paced production to produce a sequence of intervals with the same inter-onset interval (IOI). The target IOI varied across trials. A number of different measures of timing variability were considered, all negatively correlated with intelligence. Across all stimulus IOIs, local interval-to-interval variability correlated more strongly with intelligence than drift, i.e. gradual changes in response IOI. The strongest correlations with intelligence were found for IOIs between 400 and 900 ms, rather than above 1 s, which is typically considered a lower limit for cognitive timing. Furthermore, poor trials, i.e. trials arguably most affected by lapses in attention, did not predict intelligence better than the most accurate trials. We discuss these results in relation to the human timing literature, and argue that they support a bottom–up model of the relation between temporal variability of neural activity and intelligence.     

Localized disruption of Narp in medial prefrontal cortex blocks reinforcer devaluation performance

Neuronal activity regulated pentraxin (Narp) is a secreted protein that regulates α-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate receptors (AMPAR) aggregation and synaptogenesis. Mapping of Narp-positive neurons in brain has revealed it is prominently expressed in several limbic system projection pathways. Consistent with this localization pattern, Narp knockout mice show deficits in using the current value of a reinforcer to guide behavior, a critical function of the limbic system. To help assess whether this behavioral deficit is due to impairment of synaptogenesis during development or in modulating synaptic signaling in the mature brain, we have used a dominant negative Narp viral construct which blocks trafficking of endogenous Narp to axons. Focal injection of this viral construct into the medial prefrontal cortex (mPFC) of adult mice, a region containing Narp-positive projection neurons, blocked reinforcer devaluation. Thus, these results indicate that Narp released from mPFC neurons plays a key role in mediating synaptic changes underlying instrumental reinforcer devaluation.     

Endogenous BDNF is required for long-term memory formation in the rat parietal cortex

Information storage in the brain is a temporally graded process involving different memory phases as well as different structures in the mammalian brain. Cortical plasticity seems to be essential to store stable long-term memories, although little information is available at the moment regarding molecular and cellular events supporting memory consolidation in the neocortex. Brain-derived neurotrophic factor (BDNF) modulates both short-term synaptic function and activity-dependent synaptic plasticity in hippocampal and cortical neurons. We have recently demonstrated that endogenous BDNF in the hippocampus is involved in memory formation. Here we examined the role of BDNF in the parietal cortex (PCx) in short-term (STM) and long-term memory (LTM) formation of a one-trial fear-motivated learning task in rats. Bilateral infusions of function-blocking anti-BDNF antibody into the PCx impaired both STM and LTM retention scores and decreased the phosphorylation state of cAMP response element-binding protein (CREB). In contrast, intracortical administration of recombinant human BDNF facilitated LTM and increased CREB activation. Moreover, inhibitory avoidance training is associated with a rapid and transient increase in phospho-CREB/total CREB ratio in the PCx. Thus, our results indicate that endogenous BDNF is required for both STM and LTM formation of inhibitory avoidance learning, possibly involving CREB activation-dependent mechanisms. The present data support the idea that early sensory areas constitute important components of the networks subserving memory formation and that information processing in neocortex plays an important role in memory formation.     

Précis of Neural organization: structure, function, and dynamics

NEURAL ORGANIZATION: Structure, function, and dynamics shows how theory and experiment can supplement each other in an integrated, evolving account of the brain's structure, function, and dynamics. (1) STRUCTURE: Studies of brain function and dynamics build on and contribute to an understanding of many brain regions, the neural circuits that constitute them, and their spatial relations. We emphasize Szentágothai's modular architectonics principle, but also stress the importance of the microcomplexes of cerebellar circuitry and the lamellae of hippocampus. (2) FUNCTION: Control of eye movements, reaching and grasping, cognitive maps, and the roles of vision receive a functional decomposition in terms of schemas. Hypotheses as to how each schema is implemented through the interaction of specific brain regions provide the basis for modeling the overall function by neural networks constrained by neural data. Synthetic PET integrates modeling of primate circuitry with data from human brain imaging. (3) DYNAMICS: Dynamic system theory analyzes spatiotemporal neural phenomena, such as oscillatory and chaotic activity in both single neurons and (often synchronized) neural networks, the self-organizing development and plasticity of ordered neural structures, and learning and memory phenomena associated with synaptic modification. Rhythm generation involves multiple levels of analysis, from intrinsic cellular processes to loops involving multiple brain regions. A variety of rhythms are related to memory functions. The Précis presents a multifaceted case study of the hippocampus. We conclude with the claim that language and other cognitive processes can be fruitfully studied within the framework of neural organization that the authors have charted with John Szentágothai.     

Mechanisms underlying working memory for novel information

In this Opinion article we describe a theory that the brain mechanisms underlying working memory for novel information include a buffer in parahippocampal cortices. Computational modeling indicates that mechanisms for maintaining novel information in working memory could differ from mechanisms for maintaining familiar information. Electrophysiological data suggest that the buffer for novel information depends on acetylcholine. Acetylcholine activates single-cell mechanisms that underlie persistent spiking of neurons in the absence of synaptic transmission, allowing maintenance of information without prior synaptic modification. fMRI studies and lesion studies suggest that parahippocampal regions mediate working memory for novel stimuli, and the effects of cholinergic blockade impair this function. These intrinsic mechanisms in parahippocampal cortices provide an important alternative to theories of working memory based on recurrent synaptic excitation.     

多巴胺系统基因对注意网络的调控作用

多巴胺是脑内重要的神经递质之一,与注意活动紧密相关。本文选取作用于突触前膜、间隙和后膜的多巴胺系统基因——多巴胺转运蛋白基因、儿茶酚氧化甲基转移酶基因和多巴胺受体基因,整合影像遗传学研究,探讨多巴胺基因对注意网络的调控。元分析发现背侧和腹侧注意网络的主要脑区均有较大的基因调控效应,且腹侧网络的效应值显著大于背侧网络,表明多巴胺系统基因在全脑范围内调控注意网络,且对腹侧网络的调控作用更强于背侧网络。     

ELF: A new measure of response capture

Response capture is a widespread and extensively studied phenomenon, in particular in decision tasks involving response conflict. Its intensity is routinely quantified by conditional accuracy function (CAF). We argue that this method might be misleading, and propose an alternative approach, the error location function (ELF). While CAF provides the error rate by bins of reaction time (RT), ELF represents the share of total errors below each quantile of RT. We derive from ELF an index of response capture, the error location index (ELI), which represents the area below the ELF. Using simulations of computational models, we show that ELF and ELI specifically quantify variations in response capture. Finally, we illustrate the usefulness of ELF and ELI through experimental data and show that ELF and CAF can yield to contradictory conclusions.     

ECNN: Enhanced convolutional neural network for efficient diagnosis of autism spectrum disorder

This paper aims to apply deep learning to identify autism spectrum disorder (ASD) patients from a large brain imaging dataset based on the patients’ brain activation patterns. The brain images are collected from the ABIDE (Autism Brain Imaging Data Exchange) database. The proposed convolutional neural network (CNN) architecture investigates functional connectivity patterns between different brain areas to identify specifics patterns to diagnose ASD. The enhanced CNN uses blocks of temporal convolutional layers that employ casual convolutions and dilations; hence, it is suitable for sequential data with temporality large receptive fields. Experimental results show that the proposed ECNN achieves an accuracy of up to 80% accuracy. These patterns show an anticorrelation of brain function between anterior and posterior areas of the brain; that is, the disruption in brain connectivity is one primary evidence of ASD.     

The translation and introduction of Mo Yan's works based on neurolinguistics

As an advanced function of the cognitive neural mechanism of human brain, inductive reasoning is an important skill in language communication. Under the background of the development of information intelligence, it is a new research field to effectively display the cognitive neural function of inductive reasoning with the advantage of the logic operation of artificial intelligence algorithm. Therefore, in this paper, based on the neurolinguistics, the translation and introduction of Mo Yan's works were studied. And on the basis of the analysis of the characteristics of the cognitive neural mechanism of sentence inductive reasoning, by using fMR and ERP techniques, the narrowing characteristics of the semantic integrated components in the induction were investigated, and the dual processing model of inductive reasoning was discussed. After that, artificial intelligence particle swarm optimization (PSO) algorithm was introduced, and the problem of alignment in the translation of English and Chinese sentences in Mo Yan's works was transformed into the problem of finding the optimal solution for the corresponding fitness function in Chinese and English sentences in bilingual space. Thus, a scientific mathematical model was used to improve the accuracy of translation. The simulation experiments show that this study can effectively improve the accuracy of the translation and introduction of Mo Yan's works.     

Programmed to learn? The ontogeny of mirror neurons

Mirror neurons are increasingly recognized as a crucial substrate for many developmental processes, including imitation and social learning. Although there has been considerable progress in describing their function and localization in the primate and adult human brain, we still know little about their ontogeny. The idea that mirror neurons result from Hebbian learning while the child observes/hears his/her own actions has received remarkable empirical support in recent years. Here we add a new element to this proposal, by suggesting that the infant's perceptual‐motor system is optimized to provide the brain with the correct input for Hebbian learning, thus facilitating the association between the perception of actions and their corresponding motor programs. We review evidence that infants (1) have a marked visual preference for hands, (2) show cyclic movement patterns with a frequency that could be in the optimal range for enhanced Hebbian learning, and (3) show synchronized theta EEG (also known to favour synaptic Hebbian learning) in mirror cortical areas during self‐observation of grasping. These conditions, taken together, would allow mirror neurons for manual actions to develop quickly and reliably through experiential canalization. Our hypothesis provides a plausible pathway for the emergence of mirror neurons that integrates learning with genetic pre‐programming, suggesting new avenues for research on the link between synaptic processes and behaviour in ontogeny.