Anesthesia, neural information processing, and conscious awareness

Possible systemic effects of general anesthetic agents on neural information processing are discussed in the context of the thalamocortical suppression hypothesis presented by Drs. Alkire, Haier, and Fallon (this issue) in their PET study of the anesthetized state. Accounts of the neural requisites of consciousness fall into two broad categories. Neuronal-specificity theories postulate that activity in particular neural populations is sufficient for conscious awareness, while process-coherence theories postulate that particular organizations of neural activity are sufficient. Accounts of anesthetic narcosis, on the other hand, explain losses of consciousness in terms of neural signal-suppressions, transmission blocks, and the disruptions of signal interpretation. While signal-suppression may account for the actions of some anesthetic agents, the existence of anesthetics, such as choralose, that cause both loss of consciousness and elevated discharge rates, is problematic for a general theory of narcosis that is based purely on signal suppression and transmission-block. However, anesthetic agents also alter relative firing rates and temporal discharge patterns that may disrupt the coherence of neural signals and the functioning of the neural networks that interpret them. It is difficult at present, solely on the basis of regional brain metabolic rates, to test process-coherence hypotheses regarding organizational requisites for conscious awareness. While these pioneering PET studies have great merit as panoramic windows of mind-brain correlates, wider ranges of theory and empirical evidence need to be brought into the formulation of truly comprehensive theories of consciousness and anesthesia.     

The use of artificial neural networks and multiple linear regression in modelling work–health relationships: Translating theory into analytical practice

Although psychological theory acknowledges the existence of complex systems and the importance of nonlinear effects, linear statistical models have been traditionally used to examine relationships between environmental stimuli and outcomes. The way we analyse these relationships does not seem to reflect the way we conceptualize them. The present study investigated the application of connectionism (artificial neural networks) to modelling the relationships between work characteristics and employee health by comparing it with a more conventional statistical linear approach (multiple linear regression) on a sample of 1003 individuals in employment. Comparisons of performance metrics indicated differences in model fit, with neural networks to some extent outperforming the linear regression models, such that R ² for worn-out and job satisfaction were significantly higher in the neural networks. Most importantly, comparisons revealed that the predictors in the two approaches differed in their relative importance for predicting outcomes. The improvement is attributed to the ability of the neural networks to model complex nonlinear relationships. Being unconstrained by assumptions of linearity, they can provide a better approximation of such psychosocial phenomena. Nonlinear approaches are often better fitted for purpose, as they conform to the need for correspondence between theory, method, and data.     

Kohonen neural networks and language.

Kohonen neural networks are a type of self-organizing network that recognizes the statistical characteristics of input datasets. The application of this type of neural network to language theory is demonstrated in the present note by showing three brief applications: recognizing word borders, learning the limited phonemes of one's native tongue, and category-specific naming impairments.     

On simulating one-trial learning using morphological neural networks

“Learning once, remembering forever”, this wonderful cognitive phenomenon sometimes occurs in the learning process of human beings. Psychologists call this psychological phenomenon “one-trial learning”. The traditional artificial neural networks can simulate the psychological phenomenon of “implicit learning”, but can’t simulate the cognitive phenomenon of “one-trial learning”. Therefore, cognitive psychology gives a challenge to the traditional artificial neural networks. From two aspects of theory and practice in this paper, the possibility of simulating this kind of psychological phenomenon was explored by using morphological neural networks. This paper takes advantage of morphological associative memory networks to realize the simulation of “one-trial learning” for the first time, and gives 5 simulating practical examples. Theoretical analysis and simulation experiments show that the morphological associative memory networks are a higher effective machine learning method, and can better simulate the cognitive phenomenon of “one-trial learning”, therefore provide a theoretical basis and technological support for the study of intelligent science and cognitive science.     

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.     

THE NEUROBIOLOGY OF LEARNING AND MEMORY: William James in Retrospect

William James did much to set the stage for psychobiology. Beyond insisting that brain structures and processes must be the basis of explanations of mental phenomena, he expressed ideas about brain localization and plasticity in neural networks that foreshadowed many aspects of current neurobiology of learning and even connectionist theory.     

粗糙集和神经网络在心理测量中的应用

探讨当因素分析和多元回归方法的使用条件未得到满足时,是否可采用粗糙集方法进行观察变量的精简,以及是否可采用神经网络方法进行预测效度检验。理论分析了粗糙集和神经网络在心理测量中应用的可能性,并运用粗糙集对于人事干部胜任力评估数据进行分析,比较了7种离散化方法和2种约简算法构成的14种组合,发现当采用Manual方法进行离散化、遗传算法进行约简时,能够很好地对观测变量进行精简;运用概率神经网络能够比等级回归方法更好地进行预测效度检验。研究结果表明对于处理心理测量中的非等距变量,粗糙集和神经网络是非常有用的方法     

Rule extraction using ensemble of neural network ensembles

Neural networks are well-known for their impressive classification performance, and the ensemble learning technique acts as a catalyst to improve this performance even further by integrating multiple networks.However, neural network ensembles, like neural networks, are regarded as a black box because they cannot explain their decision-making process. As a result, despite their high classification performance, neural networks and their ensembles are unsuitable for some applications that require explainable decisions. However, the rule extraction technique can overcome this drawback by representing the knowledge learned by a neural network in the guise of interpretable decision rules. A rule extraction algorithm provides neural networks the ability to justify their classification responses using explainable classification rules. There are several rule extraction algorithms for extracting classification rules from neural networks, but only a few of them use neural network ensembles to generate rules. As a result, this paper proposes a rule extraction algorithm called Rule Extraction Using Ensemble of Neural Network Ensembles (RE-E-NNES) to demonstrate the high performance of neural network ensembles.RE-E-NNES extracts classification rules by ensembling several neural network ensembles. The results demonstrate the efficacy of the proposed RE-E-NNES algorithm in comparison to other existing rule extraction algorithms.     

Causality, Propensity, and Bayesian Networks

This paper investigates the relations between causality and propensity. Aparticular version of the propensity theory of probability is introduced, and it is argued that propensities in this sense are not causes. Some conclusions regarding propensities can, however, be inferred from causal statements, but these hold only under restrictive conditions which prevent cause being defined in terms of propensity. The notion of a Bayesian propensity network is introduced, and the relations between such networks and causal networks is investigated. It is argued that causal networks cannot be identified with Bayesian propensity networks, but that causal networks can be a valuable heuristic guide for the construction of Bayesian propensity networks.     

The First Computational Theory of Mind and Brain: A Close Look at Mcculloch and Pitts's “Logical Calculus of Ideas Immanent in Nervous Activity”

Despite its significance in neuroscience and computation, McCulloch and Pitts's celebrated 1943 paper has received little historical and philosophical attention. In 1943 there already existed a lively community of biophysicists doing mathematical work on neural networks. What was novel in McCulloch and Pitts's paper was their use of logic and computation to understand neural, and thus mental, activity. McCulloch and Pitts's contributions included (i) a formalism whose refinement and generalization led to the notion of finite automata (an important formalism in computability theory), (ii) a technique that inspired the notion of logic design (a fundamental part of modern computer design), (iii) the first use of computation to address the mind–body problem, and (iv) the first modern computational theory of mind and brain.     

The utility of connectionism for motor learning: a reinterpretation of contextual interference in movement schemas

Using the general framework of schema theory, and building on it, the present article takes a connectionist approach to motor learning and to contextual interference effects. These phenomena were simulated in an exploratory manner in neural networks. The outcome closely reflects previous research with humans. In a simulated ballistic movement task, networks performed worse during practice but showed better transfer when target movement distances were presented in a random rather than a blocked fashion. Connectionism provides a parsimonious account of the effect in terms of properties inherent in the parallel distributed network.     

The Utility of Connectionism for Motor Learning: A Reinterpretation of Contextual Interference in Movement Schemas

Using the general framework of schema theory, and building on it, the present article takes a connectionist approach to motor learning and to contextual interference effects. These phenomena were simulated in an exploratory manner in neural networks. The outcome closely reflects previous research with humans. In a simulated ballistic movement task, networks performed worse during practice but showed better transfer when target movement distances were presented in a random rather than a blocked fashion. Connectionism provides a parsimonious account of the effect in terms of properties inherent in the parallel distributed network.     

Birth of an Abstraction: A Dynamical Systems Account of the Discovery of an Elsewhere Principle in a Category Learning Task

Human participants and recurrent (“connectionist”) neural networks were both trained on a categorization system abstractly similar to natural language systems involving irregular (“strong”) classes and a default class. Both the humans and the networks exhibited staged learning and a generalization pattern reminiscent of the Elsewhere Condition (Kiparsky, 1973). Previous connectionist accounts of related phenomena have often been vague about the nature of the networks’ encoding systems. We analyzed our network using dynamical systems theory, revealing topological and geometric properties that can be directly compared with the mechanisms of non‐connectionist, rule‐based accounts. The results reveal that the networks “contain” structures related to mechanisms posited by rule‐based models, partly vindicating the insights of these models. On the other hand, they support the one mechanism (OM), as opposed to the more than one mechanism (MOM), view of symbolic abstraction by showing how the appearance of MOM behavior can arise emergently from one underlying set of principles. The key new contribution of this study is to show that dynamical systems theory can allow us to explicitly characterize the relationship between the two perspectives in implemented models.     

The global workspace (GW) theory of consciousness and epilepsy

The global workspace (GW) theory proposes that conscious processing results from coherent neuronal activity between widely distributed brain regions, with fronto-parietal associative cortices as key elements. In this model, transition between conscious and non conscious states are predicted to be caused by abrupt non-linear massive changes of the level of coherence within this distributed neural space. Epileptic seizures offer a unique model to explore the validity of this central hypothesis. Seizures are often characterized by the occurrence of brutal alterations of consciousness (AOC) which are largely negatively impacting patients' lives. Recently, we have shown that these sudden AOC are contemporary to non-linear increases of neural synchrony within distant cortico-cortical and cortico-thalamic networks. We interpreted these results in the light of GW theory, and suggested that excessive synchrony could prevent this distributed network to reach the minimal level of differentiation and complexity necessary to the coding of conscious representations. These observations both confirm some predictions of the GW model, and further specify the physiological window of neural coherence (minimum and maximum) associated with conscious processing.     

Artificial life and Piaget

Artificial life provides important theoretical and methodological tools for the investigation of Piaget's developmental theory. This new method uses artificial neural networks to simulate living phenomena in a computer. A recent study by Parisi and Schlesinger suggests that artificial life might reinvigorate the Piagetian framework. We contrast artificial life with traditional cognitivist approaches, discuss the role of innateness in development, and examine the relation between physiological and psychological explanations of intelligent behaviour.     

Simple games as dynamic, coupled systems: randomness and other emergent properties

From a game theory perspective the ability to generate random behaviors is critical. However, psychological studies have consistently found that individuals are poor at behaving randomly. In this paper we investigated the possibility that the randomness mechanism lies not within the individual players but in the interaction between the players. Provided that players are influenced by their opponent’s past behavior, their relationship may constitute a state of reciprocal causation [Cognitive Science 21 (1998) 461], in which each player simultaneously affects and is affected by the other player. The result of this would be a dynamic, coupled system. Using neural networks to represent the individual players in a game of paper, rock, and scissors, a model of this process was developed and shown to be capable of generating chaos-like behaviors as an emergent property. In addition, it was found that by manipulating the control parameters of the model, corresponding to the amount of working memory and the perceived values of different outcomes, that the game could be biased in favor of one player over the other, an outcome not predicted by game theory. Human data was collected and the results show that the model accurately describes human behavior. The results and the model are discussed in light of recent theoretical advances in dynamic systems theory and cognition.     

A constructive neural-network approach to modeling psychological development

This article reviews a particular computational modeling approach to the study of psychological development – that of constructive neural networks. This approach is applied to a variety of developmental domains and issues, including Piagetian tasks, shift learning, language acquisition, number comparison, habituation of visual attention, concept learning, and theory of mind. Implications of this modeling for theoretical understanding of psychological development are considered.     

Is it worth generating rules from neural network ensembles?

Although many authors generated comprehensible models from individual networks, much less work has been done in the explanation of ensembles. DIMLP is a special neural network model from which rules are generated at the level of a single network and also at the level of an ensemble of networks. We applied ensembles of 25 DIMLP networks to several datasets of the public domain and a classification problem related to post-translational modifications of proteins. For the classification problems of the public domain, the average predictive accuracy of rulesets extracted from ensembles of neural networks was significantly better than the average predictive accuracy of rulesets generated from ensembles of decision trees. By varying the architectures of DIMLP networks we found that the average predictive accuracy of rules, as well as their complexity were quite stable. The comparison to other rule extraction techniques applied to neural networks showed that rules generated from DIMLP ensembles gave very good results. In the last problem related to bioinformatics, the best result obtained by ensembles of DIMLP networks was also significantly better than the best result obtained by ensembles of decision trees. Thus, although neural networks take much longer to train than decision trees and also rules are generated at a greater computational cost (however, still polynomial), at least for several classification problems it was worth using neural network ensembles, as extracted rules were more accurate, on average. The DIMLP software is available for PC-Linux under http://us.expasy.org/people/Guido.Bologna.html.     

Nonconscious perception, conscious awareness and attention

Because it is unclear how a nonconscious stimulus is cognitively processed, there is uncertainty concerning variables that modulate the processing. In this context recent findings of a set of neuroimaging experiments are important. These findings suggest that conscious and nonconscious stimuli activate same areas of the brain during performance of a similar task. Further, different areas are activated when a task is performed with or without awareness of processing. It appears that the neural network involved in cognitive processing depends on the awareness of processing rather than awareness of perception. Since conscious and nonconscious cognitive processing use separate neural networks, each processing is modulated by different variables. Attention modulates most conscious cognitive processing and most, but not all, nonconscious processing is attention dependent. Nonconscious tasks that require attentional resources, with or without conscious awareness, are processed using the attention dependent system. Further, because attention dependent and attention independent tasks are processed by separate neural networks, the cognitive processing and modulating variables can be understood better if cognitive tasks are defined as attention dependent or attention independent, rather than conscious or nonconscious.     

If we compute faster, do we understand better?

Practitioners of cognitive science, “theoretical” neuroscience, and psychology have made less use of high-performance computing for testing theories than have those in many other areas of science. Why is this? In high-performance scientific computation, potentially billions of operations must lead to a trustable conclusion. Technical problems with the stability of algorithms aside, this requirement also places extremely rigorous constraints on the accuracy of the underlying theory. For example, electromagnetic interactions seem to hold accurately from atomic to galactic scales. Large-scale computations using elementary principles are possible and useful. Many have commented that the behavioral and neural sciences are largely pretheoretical. One consequence is that we cannot trust our few theories to scale well for a very good reason: They don’t. We have some quite good computational theories for single neurons and some large-scale aspects of behavior seem to be surprisingly lawful. However, we have little idea about how to go from the behavior of a single neuron to the behavior of the 10¹¹ neurons involved when the brain actually does something. Neural networks have offered one potential way to leap this enormous gap in scale, since many elementary units cooperate in a neural network computation. As currently formulated, however, neural networks seem to lack essential mechanisms that are required for flexible control of the computation, and they also neglect structure at intermediate scales of organization. We will present some speculations related to controllability and scaling in neural networks.