Mechanisms of developmental regression in autism and the broader phenotype: a neural network modeling approach

Loss of previously established behaviors in early childhood constitutes a markedly atypical developmental trajectory. It is found almost uniquely in autism and its cause is currently unknown (Baird et al., 2008). We present an artificial neural network model of developmental regression, exploring the hypothesis that regression is caused by overaggressive synaptic pruning and identifying the mechanisms involved. We used a novel population-modeling technique to investigate developmental deficits, in which both neurocomputational parameters and the learning environment were varied across a large number of simulated individuals. Regression was generated by the atypical setting of a single pruning-related parameter. We observed a probabilistic relationship between the atypical pruning parameter and the presence of regression, as well as variability in the onset, severity, behavioral specificity, and recovery from regression. Other neurocomputational parameters that varied across the population modulated the risk that an individual would show regression. We considered a further hypothesis that behavioral regression may index an underlying anomaly characterizing the broader autism phenotype. If this is the case, we show how the model also accounts for several additional findings: shared gene variants between autism and language impairment (Vernes et al., 2008); larger brain size in autism but only in early development (Redcay & Courchesne, 2005); and the possibility of quasi-autism, caused by extreme environmental deprivation (Rutter et al., 1999). We make a novel prediction that the earliest developmental symptoms in the emergence of autism should be sensory and motor rather than social and review empirical data offering preliminary support for this prediction.     

Animal Learning and Cognition: A neural network approach

by Nestor A. Schmajuk, Cambridge University Press, 1997. ￡29.95 (xii+340 pages) ISBN 0 521 45086 1.     

A neural network simulator for supercomputers

Connectionist modeling is computationally intensive. Until parallel computers become more widely available, supercomputing resources can be exploited. This paper describes a neural network simulator (NNS) written in FORTRAN for supercomputers. The present simulation engine consists of code for the backpropagation method of changing weights in connectionist models. A file interface reports simulation results in a variety of formats. The file interface also contains an interpreter for an input file through which the network structure is defined, the problem is represented, and various parameters are set. The input-file syntax is described in detail. NNS has been used both as an instructional aid and as a research tool. A simulation of “recovery of unrehearsed associations” is used to illustrate the use of the input file and to demonstrate the performance of NNS. Versions of NNS have been written for the Cray X-MP/48 and for the IBM 3090.     

A neural network model of retrieval-induced forgetting

Retrieval-induced forgetting (RIF) refers to the finding that retrieving a memory can impair subsequent recall of related memories. Here, the authors present a new model of how the brain gives rise to RIF in both semantic and episodic memory. The core of the model is a recently developed neural network learning algorithm that leverages regular oscillations in feedback inhibition to strengthen weak parts of target memories and to weaken competing memories. The authors use the model to address several puzzling findings relating to RIF, including why retrieval practice leads to more forgetting than simply presenting the target item, how RIF is affected by the strength of competing memories and the strength of the target (to-be-retrieved) memory, and why RIF sometimes generalizes to independent cues and sometimes does not. For all of these questions, the authors show that the model can account for existing results, and they generate novel predictions regarding boundary conditions on these results.     

Connecting neural coding to number cognition: a computational account

The current study presents a series of computational simulations that demonstrate how the neural coding of numerical magnitude may influence number cognition and development. This includes behavioral phenomena cataloged in cognitive literature such as the development of numerical estimation and operational momentum. Though neural research has begun to describe neural coding of number, it is unclear how specific characteristics of the neural coding may relate to the expansive list of behavioral phenomena in the development of number cognition. The following study considers several possibilities.     

McCollough effect: A neural network model based on source separation

McCollough effects (MEs) are a group of visual contingent aftereffects that involve colour and contour. These effects have been the subject of a large body of literature concerning their properties and theoretical accounts, but the mechanisms underlying the ME have never been fully clarified. We make the assumption that a general adaptive neural process tending to maintain independent dimensions in visual perception could account for the ME. The proposed neural network model generating the ME, though of minimal complexity, can reproduce various detailed experimental results (such as the tilt effect contingent to colour) and above all it accounts for the distinctive long temporal persistence of this aftereffect.     

A neural network approach to movement pattern analysis

Movements are time-dependent processes and so can be modelled by time-series of coordinates: E.g., each articulation has geometric coordinates; the set of the coordinates of the relevant articulations build a high-dimensional configuration. These configurations--or "patterns"--give reason for analysing movements by means of neural networks: The Kohonen Feature Map (KFM) is a special type of neural network, which (after having been coined by training with appropriate pattern samples) is able to recognize single patterns as members of pattern clusters. This way, for example, the particular configurations of a given movement can be identified as belonging to respective configuration clusters, where the sequence of clusters to which the time-depending configurations belong, characterizes the process as a 2-dimensional trajectory. The advantages of this method are that: the high dimensionality of the original processes is reduced to two dimensional trajectories, the clusters are automatically determined by the network, and all data for further analyses can automatically be transferred into a data base. Thus, the processes can either be visualized and analysed by an expert or again processed by further automatic analysing tools, as has been done with similarity matrices. The disadvantage is that a KFM-training needs a huge amount of information, which normally is not available from experiments. However, the Dynamically Controlled Network DyCoN (a special type of KFM) makes it possible to reduce the amount of original training data substantially--e.g., by adding stochastically generated ones. Currently, DyCoN is used in several projects in order to generally support analyses of processes in sport. It should be emphasized that the presented approach is not meant to improve the understanding or to develop models of human movement but to give a survey of the advantages and methodological aspects of net-based movement analysis.     

A nonlinear dynamic artificial neural network model of memory

Nonlinearity and dynamics in psychology are found in various domains such as neuroscience, cognitive science, human development, etc. However, the models that have been proposed are mostly of a computational nature and ignore dynamics. In those models that do include dynamic properties, only fixed points are used to store and retrieve information, leaving many principles of nonlinear dynamic systems (NDS) aside; for instance, chaos is often perceived as a nuisance. This paper considers a nonlinear dynamic artificial neural network (NDANN) that implements NDS principles while also complying with general neuroscience constraints. After a theoretical presentation, simulation results will show that the model can exhibit multi-valued, fixed-point, region-constrained attractors and aperiodic (including chaotic) behaviors. Because the capabilities of NDANN include the modeling of spatiotemporal chaotic activities, it may be an efficient tool to help bridge the gap between biological memory neural models and behavioral memory models.     

Semantic information and the network theory of account

The article addresses the problem of how semantic information can be upgraded to knowledge. The introductory section explains the technical terminology and the relevant background. Section 2 argues that, for semantic information to be upgraded to knowledge, it is necessary and sufficient to be embedded in a network of questions and answers that correctly accounts for it. Section 3 shows that an information flow network of type A fulfils such a requirement, by warranting that the erotetic deficit, characterising the target semantic information t by default, is correctly satisfied by the information flow of correct answers provided by an informational source s. Section 4 illustrates some of the major advantages of such a Network Theory of Account (NTA) and clears the ground of a few potential difficulties. Section 5 clarifies why NTA and an informational analysis of knowledge, according to which knowledge is accounted semantic information, is not subject to Gettier-type counterexamples. A concluding section briefly summarises the results obtained.     

A neural network approach to identifying adolescent adjustment.

This study examined the relationship between the quality of adjustment in adolescents and a set of psychiatric diagnoses, personality traits, parental bonding, and social support variables. One hundred fifty adolescents were administered the Millon Adolescent Personality Inventory, the Parental Bonding Questionnaire, the Social Support Questionnaire, and the Diagnostic Interview for Children and Adolescents. A neural network approach was then utilized, and it was found that several of the variables (e.g., Major Depressive Disorder, Conduct Disorder, and Societal Conformity) had a significant role in classifying adolescents into three groups: maladjusted, nominally adjusted, and well-adjusted.     

Prejudiced learning: A connectionist account

Connectionist simulation was employed to investigate processes that may underlie the relationships between prior expectancies or prejudices and the acquisition of attitudes, under conditions where learners can only discover the valence of attitude objects through directly experiencing them. We compared contexts analogous to learners holding either false negative expectancies (‘prejudices’) about a subclass of objects that were actually good or false positive expectancies about objects that were actually bad. We introduced expectancy‐related bias either by altering the probability of approach, or by varying the rate of learning following experience with good or bad objects. Where feedback was contingent on approach, the false positive expectancies were corrected by experience, but negative prejudices resisted change, since the network avoided objects deemed to be bad, and so received less corrective feedback. These findings are discussed in relation to the effects of intergroup contact and expectancy‐confirmation processes in reducing or sustaining prejudice.     

The I: A dimensional account

Phenomenology and the Cognitive Sciences - I have a clear idea of what it means that I have experiences in the past or future, and it does not seem to mean that experiences take place that possess...     

A neural network model of implicit memory for object recognition

People name well-known objects shown in pictures more quickly if they have studied them previously. The most common interpretation of this priming effect is that processing is facilitated by an implicit memory trace in a perceptual representation system. We show that object priming can be explained instead as a bias in information processing, without recourse to an implicit memory system. Assumptions about psychological decision-making processes and bias were added to a neural network model for object identification, and the model accounted for performance both qualitatively and quantitatively in four object identification experiments.     

Testing neural network models of personality

Attempts to develop neural network models of personality have not generally used empirical data for training and validating the models. Two illustrations are provided which demonstrate the incorporation of empirical data into the modeling of behavioral responses to situations varying in closeness and hierarchical role relationships. An event-contingent recording procedure is utilized to obtain data from the same participant in multiple events for multiple situations. This data is then used in the training and validation of the neural networks. The first illustration models dominant and submissive behaviors in response to situations varying in social role status. The second illustration models agreeable and quarrelsome behaviors in response to situations varying in closeness and gender of the interaction partner. The predictions from both neural network models are consistent with previous research.     

Evolution of the neural language network

The evolution of language correlates with distinct changes in the primate brain. The present article compares language-related brain regions and their white matter connectivity in the developing and mature human brain with the respective structures in the nonhuman primate brain. We will see that the functional specificity of the posterior portion of Broca’s area (Brodmann area [BA 44]) and its dorsal fiber connection to the temporal cortex, shown to support the processing of structural hierarchy in humans, makes a crucial neural difference between the species. This neural circuit may thus be fundamental for the human syntactic capacity as the core of language.     

A neural network model of foraging decisions made under predation risk

This article develops the cognitive—emotional forager (CEF) model, a novel application of a neural network to dynamical processes in foraging behavior. The CEF is based on a neural network known as the gated dipole, introduced by Grossberg, which is capable of representing short-term affective reactions in a manner similar to Solomon and Corbit’s (1974) opponent process theory. The model incorporates a trade-off between approach toward food and avoidance of predation under varying levels of motivation induced by hunger. The results of simulations in a simple patch selection paradigm, using a lifetime fitness criterion for comparison, indicate that the CEF model is capable of nearly optimal foraging and outperforms a run-of-luck rule-of-thumb model. Models such as the one presented here can illuminate the underlying cognitive and motivational components of animal decision making.     

Distributed representations of action sequences in anterior cingulate cortex: A recurrent neural network approach

Anterior cingulate cortex (ACC) has been the subject of intense debate over the past 2 decades, but its specific computational function remains controversial. Here we present a simple computational model of ACC that incorporates distributed representations across a network of interconnected processing units. Based on the proposal that ACC is concerned with the execution of extended, goal-directed action sequences, we trained a recurrent neural network to predict each successive step of several sequences associated with multiple tasks. In keeping with neurophysiological observations from nonhuman animals, the network yields distributed patterns of activity across ACC neurons that track the progression of each sequence, and in keeping with human neuroimaging data, the network produces discrepancy signals when any step of the sequence deviates from the predicted step. These simulations illustrate a novel approach for investigating ACC function.