Computational discovery of instructionless self-replicating structures in cellular automata

Cellular automata models have historically been a major approach to studying the information-processing properties of self-replication. Here we explore the feasibility of adopting genetic programming so that, when it is given a fairly arbitrary initial cellular automata configuration, it will automatically generate a set of rules that make the given configuration replicate. We found that this approach works surprisingly effectively for structures as large as 50 components or more. The replication mechanisms discovered by genetic programming work quite differently than those of many past manually designed replicators: There is no identifiable instruction sequence or construction arm, the replicating structures generally translate and rotate as they reproduce, and they divide via a fissionlike process that involves highly parallel operations. This makes replication very fast, and one cannot identify which descendant is the parent and which is the child. The ability to automatically generate self-replicating structures in this fashion allowed us to examine the resulting replicators as their properties were systematically varied. Further, it proved possible to produce replicators that simultaneously deposited secondary structures while replicating, as in some past manually designed models. We conclude that genetic programming is a powerful tool for studying self-replication that might also be profitably used in contexts other than cellular spaces.

An oscillatory Hebbian network model of short-term memory

Recurrent neural architectures having oscillatory dynamics use rhythmic network activity to represent patterns stored in short-term memory. Multiple stored patterns can be retained in memory over the same neural substrate because the network's state persistently switches between them. Here we present a simple oscillatory memory that extends the dynamic threshold approach of Horn and Usher (1991) by including weight decay. The modified model is able to match behavioral data from human subjects performing a running memory span task simply by assuming appropriate weight decay rates. The results suggest that simple oscillatory memories incorporating weight decay capture at least some key properties of human short-term memory. We examine the implications of the results for theories about the relative role of interference and decay in forgetting, and hypothesize that adjustments of activity decay rate may be an important aspect of human attentional mechanisms.

A theory of the visual system biology underlying development of spatial frequency lateralization

The spatial frequency hypothesis contends that performance differences between the hemispheres on various visuospatial tasks are attributable to lateralized processing of the spatial frequency content of visual stimuli. Hellige has proposed that such lateralization could arise during infant development from the earlier maturation of the right hemisphere combined with the increasing sensitivity of the visual system to high spatial frequencies. This proposal is intuitively appealing but lacks an explicit theory with respect to the underlying visual system biology. In this paper, we develop such a theory based on knowledge of visual system processing and development. We then translate our theory into a computational model that serves as the basis for a series of development simulations. We find that the simulations produce spatial frequency lateralization effects consistent with those observed empirically. We relate the nature of the neural asymmetry implied by our theory to empirical findings on visual pathway bias and the relative spatial frequency lateralization effect.

Functional connectivity in fMRI: A modeling approach for estimation and for relating to local circuits

Although progress has been made in relating neuronal events to changes in brain metabolism and blood flow, the interpretation of functional neuroimaging data in terms of the underlying brain circuits is still poorly understood. Computational modeling of connection patterns both among and within regions can be helpful in this interpretation. We present a neural network model of the ventral visual pathway and its relevant functional connections. This includes a new learning method that adjusts the magnitude of interregional connections in order to match experimental results of an arbitrary functional magnetic resonance imaging (fMRI) data set. We demonstrate that this method finds the appropriate connection strengths when trained on a model system with known, randomly chosen connection weights. We then use the method for examining fMRI results from a one-back matching task in human subjects, both healthy and those with schizophrenia. The results discovered by the learning method support previous findings of a disconnection between left temporal and frontal cortices in the group with schizophrenia and a concomitant increase of right-sided temporo-frontal connection strengths. We then demonstrate that the disconnection may be explained by reduced local recurrent circuitry in frontal cortex. This method extends currently available methods for estimating functional connectivity from human imaging data by including both local circuits and features of interregional connections, such as topography and sparseness, in addition to total connection strengths. Furthermore, our results suggest how fronto-temporal functional disconnection in schizophrenia can result from reduced local synaptic connections within frontal cortex rather than compromised interregional connections.

Simulating single word processing in the classic aphasia syndromes based on the Wernicke-Lichtheim-Geschwind theory

The Wernicke-Lichtheim-Geschwind (WLG) theory of the neurobiological basis of language is of great historical importance, and it continues to exert a substantial influence on most contemporary theories of language in spite of its widely recognized limitations. Here, we suggest that neurobiologically grounded computational models based on the WLG theory can provide a deeper understanding of which of its features are plausible and where the theory fails. As a first step in this direction, we created a model of the interconnected left and right neocortical areas that are most relevant to the WLG theory, and used it to study visual-confrontation naming, auditory repetition, and auditory comprehension performance. No specific functionality is assigned a priori to model cortical regions, other than that implicitly present due to their locations in the cortical network and a higher learning rate in left hemisphere regions. Following learning, the model successfully simulates confrontation naming and word repetition, and acquires a unique internal representation in parietal regions for each named object. Simulated lesions to the language-dominant cortical regions produce patterns of single word processing impairment reminiscent of those postulated historically in the classic aphasia syndromes. These results indicate that WLG theory, instantiated as a simple interconnected network of model neocortical regions familiar to any neuropsychologist/neurologist, captures several fundamental "low-level" aspects of neurobiological word processing and their impairment in aphasia.

Mirror Symmetric Topographic Maps Can Arise from Activity-Dependent Synaptic Changes

Multiple adjacent, roughly mirror-image topographic maps are commonly observed in the sensory neocortex of many species. The cortical regions occupied by these maps are generally believed to be determined initially by genetically controlled chemical markers during development, with thalamocortical afferent activity subsequently exerting a progressively increasing influence over time. Here we use a computational model to show that adjacent topographic maps with mirror-image symmetry can arise from activity-dependent synaptic changes whenever the distribution radius of afferents sufficiently exceeds that of horizontal intracortical interactions. Which map edges become adjacent is strongly influenced by the probability distribution of input stimuli during map formation. Our results suggest that activity-dependent synaptic changes may play a role in influencing how adjacent maps become oriented following the initial establishment of cortical areas via genetically determined chemical markers. Further, the model unexpectedly predicts the occasional occurrence of adjacent maps with a different rotational symmetry. We speculate that such atypically oriented maps, in the context of otherwise normally interconnected cortical regions, might contribute to abnormal cortical information processing in some neuro developmental disorders.

Post-lesion lateralisation shifts in a computational model of single-word reading

The mechanisms underlying lateralisation of language are incompletely understood. Existing data is inconclusive, for example, in determining which underlying asymmetries in hemispheric anatomy/physiology lead to lateralisation, the precise role of interhemispheric connections in this process, and exactly how and why lateralisation can shift following focal brain damage. Although these issues will ultimately be settled by experimentation, it is likely that computational modelling can be used to suggest, focus, and even interpret such empirical work. We have recently studied the emergence of lateralisation in an artificial neural network model having paired cerebral hemispheric regions, as the model learned to generate the correct pronunciation for simple words. In this paper we extend this previous work by examining the immediate and longer-term changes in lateralisation that occur following simulated acute hemispheric lesions. Among other things, the results demonstrate that the extent to which the non-lesioned model hemispheric region contributes to recovery is a function of lesion size, prelesion lateralisation, and assumptions about the excitatory/inhibitory influences of the corpus callosum. The relevance of these results to the currently controversial suggestion that language lateralisation shifts following focal damage to language areas, and that the unlesioned hemisphere contributes to recovery from stroke-induced aphasia in adults, is discussed.

Neurocomputational models of the remote effects of focal brain damage

Sudden localized brain damage, such as occurs in stroke, produces neurological deficits directly attributable to the damaged site. In addition, other clinical deficits occur due to secondary "remote" effects that functionally impair the remaining intact brain regions (e.g., due to their sudden disconnection from the damaged area), a phenomenon known as diaschisis. The underlying mechanisms of these remote effects, particularly those involving interactions between the left and right cerebral hemispheres, have proven somewhat difficult to understand in the context of current theories of hemispheric specialization. This article describes some recent neurocomputational models done in the author's research group that try to explain diaschisis qualitatively. These studies show that both specialization and diaschisis can be accounted for with a single model of hemispheric interactions. Further, the results suggest that left-right subcortical influences may be much more important in influencing hemispheric specialization than is generally recognized.

Using distributed partial memories to improve self-organizing collective movements

Past self-organizing models of collectively moving "particles" (simulated bird flocks, fish schools, etc.) have typically been based on purely reflexive agents that have no significant memory of past movements. We hypothesized that giving such individual particles a limited distributed memory of past obstacles they encountered could lead to significantly faster travel between goal destinations. Systematic computational experiments using six terrains that had different arrangements of obstacles demonstrated that, at least in some domains, this conjecture is true. Furthermore, these experiments demonstrated that improved performance over time came not only from the avoidance of previously seen obstacles, but also (surprisingly) immediately after first encountering obstacles due to decreased delays in circumventing those obstacles. Simulations also showed that, of the four strategies we tested for removal of remembered obstacles when memory was full and a new obstacle was to be saved, none was better than random selection. These results may be useful in interpreting future experimental research on group movements in biological populations, and in improving existing methodologies for control of collective movements in computer graphics, robotic teams, particle swarm optimization, and computer games.

Hemispheric specialization and independence for word recognition: a comparison of three computational models

Two findings serve as the hallmark for hemispheric specialization during lateralized lexical decision. First is an overall word advantage, with words being recognized more quickly and accurately than non-words (the effect being stronger in response latency). Second, a right visual field advantage is observed for words, with little or no hemispheric differences in the ability to identify non-words. Several theories have been proposed to account for this difference in word and non-word recognition, some by suggesting dual routes of lexical access and others by incorporating separate, and potentially independent, word and non-word detection mechanisms. We compare three previously proposed cognitive theories of hemispheric interactions (callosal relay, direct access, and cooperative hemispheres) through neural network modeling, with each network incorporating different means of interhemispheric communication. When parameters were varied to simulate left hemisphere specialization for lexical decision, only the cooperative hemispheres model showed both a consistent left hemisphere advantage for word recognition but not non-word recognition, as well as an overall word advantage. These results support the theory that neural representations of words are more strongly established in the left hemisphere through prior learning, despite open communication between the hemispheres during both learning and recall.

Temporally Asymmetric Learning Supports Sequence Processing in Multi-Winner Self-Organizing Maps

We examine the extent to which modified Kohonen self-organizing maps (SOMs) can learn unique representations of temporal sequences while still supporting map formation. Two biologically inspired extensions are made to traditional SOMs: selection of multiple simultaneous rather than single “winners” and the use of local intramap connections that are trained according to a temporally asymmetric Hebbian learning rule. The extended SOM is then trained with variable-length temporal sequences that are composed of phoneme feature vectors, with each sequence corresponding to the phonetic transcription of a noun. The model transforms each input sequence into a spatial representation (final activation pattern on the map). Training improves this transformation by, for example, increasing the uniqueness of the spatial representations of distinct sequences, while still retaining map formation based on input patterns. The closeness of the spatial representations of two sequences is found to correlate significantly with the sequences' similarity. The extended model presented here raises the possibility that SOMs may ultimately prove useful as visualization tools for temporal sequences and as preprocessors for sequence pattern recognition systems.

Extending self-organizing particle systems to problem solving

Self-organizing particle systems consist of numerous autonomous, purely reflexive agents ("particles") whose collective movements through space are determined primarily by local influences they exert upon one another. Inspired by biological phenomena (bird flocking, fish schooling, etc.), particle systems have been used not only for biological modeling, but also increasingly for applications requiring the simulation of collective movements such as computer-generated animation. In this research, we take some first steps in extending particle systems so that they not only move collectively, but also solve simple problems. This is done by giving the individual particles (agents) a rudimentary intelligence in the form of a very limited memory and a top-down, goal-directed control mechanism that, triggered by appropriate conditions, switches them between different behavioral states and thus different movement dynamics. Such enhanced particle systems are shown to be able to function effectively in performing simulated search-and-collect tasks. Further, computational experiments show that collectively moving agent teams are more effective than similar but independently moving ones in carrying out such tasks, and that agent teams of either type that split off members of the collective to protect previously acquired resources are most effective. This work shows that the reflexive agents of contemporary particle systems can readily be extended to support goal-directed problem solving while retaining their collective movement behaviors. These results may prove useful not only for future modeling of animal behavior, but also in computer animation, coordinated movement control in robotic teams, particle swarm optimization, and computer games.

Effects of callosal lesions in a model of letter perception

During cognitive tasks, the cerebral hemispheres cooperate, compete, and in general, interact via the corpus callosum. Although behavioral studies in normal and split-brain subjects have revealed a great deal about the transcallosal exchange of information, a fundamental question remains unanswered and controversial: Are transcallosal interhemispheric influences primarily excitatory or inhibitory? In this context, we examined the effects of simulating sectioning of the corpus callosum in a computational model of visual letter recognition. Differences were found, following simulated callosal sectioning, in the performance of each individual hemisphere, in the mean activation levels of hemispheres, and in the specific patterns of activity, depending on the nature of the callosal influences. Together with other recent computational modeling results, the findings are most consistent with the hypothesis that transcallosal influences are predominantly excitatory, and they suggest measures that could be examined in future experimental studies to help resolve this issue.

Predicting nearest agent distances in artificial worlds

In a number of multi-agent artificial life studies where agents interact over limited distances, the emergence and/or evolution of a specific behavior may depend critically upon interagent distances. Little theoretical analysis has been done previously concerning how to predict such distances. In this paper, we derive a probabilistic method that, for an agent at an arbitrary location in a two-dimensional cellular world, predicts the expected distance to a nearest other agent. Our method works for many world topologies, and we apply it to determine the expected distance for six commonly used ones. Further, the method is readily adapted to handle special restrictions. Over a wide variety of agent densities we show that the theoretically predicted distances are largely in agreement with the distances measured in computational experiments with randomly placed agents. We then utilize our prediction method to interpret recent observations that an imprecise threshold in the density of agents exists for the evolution of communication. We thus illustrate that, despite its conceptual simplicity, our method can aid the analysis and even the design of complex artificial environments populated by agents that have the potential to interact with one another.

Cortical spreading depression and the pathogenesis of brain disorders: a computational and neural network-based investigation

This paper reviews our recent studies of the role of cortical spreading depression (CSD) in the pathogenesis of brain disorders. Our investigation is a computational one, involving the development and utilization of a complex neuro-metabolic model of the interactions assumed to occur in the cortex during the passage of multiple CSD waves. Incorporating these neuro-metabolic changes of CSD within a neural network model of normoxic cortex produces cortical activation patterns during the passage of a CSD wave that, projected onto the visual fields, resemble the visual hallucinations observed during the migraine aura. When focal ischemia is simulated with the model, the evoked CSD waves are found to affect the expansion of the infarction into the ischemic penumbra. Our findings support the hypothesis that CSD does play an important pathogenic role in these and other neurological disorders, and suggest additional experimental studies that may further substantiate it.

The callosal dilemma: explaining diaschisis in the context of hemispheric rivalry via a neural network model

It is often suggested that a major factor in diaschisis is the loss of transcallosal excitation to the intact hemisphere from the lesioned one. However, there is long-standing disagreement in the broader experimental literature about whether transcallosal interhemispheric influences in the human brain are primarily excitatory or inhibitory. Some experimental data are apparently better explained by assuming inhibitory callosal influences. Past neural network models attempting to explore this issue have encountered the same dilemma: in intact models, inhibitory callosal influences best explain strong cerebral lateralization like that occurring with language, but in lesioned models, excitatory callosal influences best explain experimentally observed hemispheric activation patterns following brain damage. We have now developed a single neural network model that can account for both types of data, i.e., both diaschisis and strong hemisphere specialization in the normal brain, by combining excitatory callosal influences with subcortical cross-midline inhibitory interactions. The results suggest that subcortical competitive processes may be a more important factor in cerebral specialization than is generally recognized.

Cortical map asymmetries in the context of transcallosal excitatory influences

There is long-standing disagreement among experimentalists about whether transcallosal interhemispheric influences are primarily excitatory or inhibitory. Past computational models exploring this issue have encountered a similar dilemma: inhibitory callosal influences best explain hemispheric functional asymmetries, but excitatory callosal influences best explain transcallosal diaschisis. We recently hypothesized that this dilemma might be resolved by assuming excitatory callosal influences and a subcortical mechanism for cross-midline inhibition. Here we explore the feasibility of this hypothesis by examining a model of map formation in corresponding left and right cortical regions. The results show for the first time that both map asymmetries and diaschisis-like changes can be produced in a single model, suggesting that subcortical inhibitory processes may contribute more to asymmetric cortical functionality than is generally recognized.

Conditions enabling the evolution of inter-agent signaling in an artificial world

In the research described here we extend past computational investigations of animal signaling by studying an artificial world in which a population of initially noncommunicating agents evolves to communicate about food sources and predators. Signaling in this world can be either beneficial (e.g., warning of nearby predators) or costly (e.g., attracting predators or competing agents). Our goals were twofold: to examine systematically environmental conditions under which grounded signaling does or does not evolve, and to determine how variations in assumptions made about the evolutionary process influence the outcome. Among other things, we found that agents warning of nearby predators were a common occurrence whenever predators had a significant impact on survival and signaling could interfere with predator success. The setting most likely to lead to food signaling was found to be difficult-to-locate food sources that each have relatively large amounts of food. Deviations from the selection methods typically used in traditional genetic algorithms were also found to have a substantial impact on whether communication evolved. For example, constraining parent selection and child placement to physically neighboring areas facilitated evolution of signaling in general, whereas basing parent selection upon survival alone rather than survival plus fitness measured as success in food acquisition was more conducive to the emergence of predator alarm signals. We examine the mechanisms underlying these and other results, relate them to existing experimental data about animal signaling, and discuss their implications for artificial life research involving evolution of communication.

Interhemispheric effects of simulated lesions in a neural model of letter identification

Experimental studies have produced conflicting results about the extent to which the intact, nonlesioned cerebral hemisphere is responsible for recovery from cognitive deficits following focal brain damage such as a stroke. To obtain a better theoretical understanding of interhemispheric interactions during recovery, we examined the effects of simulated lesions to a bihemispheric neural model of letter identification under various assumptions about hemispheric asymmetries, corpus callosum influence, and lesion size. Among other results, the model demonstrates that the intact hemispheric region's participation in the recovery process is a function of preexisting lateralization and lesion size, indicating that interpretation of experimental work should take these factors into account.

A computational model of lateralization and asymmetries in cortical maps

While recent experimental work has defined asymmetries and lateralization in left and right cortical maps, the mechanisms underlying these phenomena are currently not established. In order to explore some possible mechanisms in theory, we studied a neural model consisting of paired cerebral hemispheric regions interacting via a simulated corpus callosum. Starting with random synaptic strengths, unsupervised (Hebbian) synaptic modifications led to the emergence of a topographic map in one or both hemispheric regions. Because of uncertainties concerning the nature of hemispheric interactions, both excitatory and inhibitory callosal influences were examined independently. A sharp transition in model behavior was observed depending on callosal strength. For excitatory or weakly inhibitory callosal interactions, complete and symmetric mirror-image maps generally appeared in both hemispheric regions. In contrast, with stronger inhibitory callosal interactions, partial to complete map lateralization tended to occur, and the maps in each hemispheric region often became complementary. Lateralization occurred readily toward the side having a larger cortical region or higher excitability. Asymmetric synaptic plasticity, however, had only a transitory effect on lateralization. These results support the hypotheses that interhemispheric competition occurs, that multiple underlying asymmetries may lead to function lateralization, and that the effects of asymmetric synaptic plasticity may vary depending on whether supervised or unsupervised learning is involved. To our knowledge, this is the first computational model to demonstrate the emergence of topographic map lateralization and asymmetries.

Interhemispheric effects of simulated lesions in a neural model of single-word reading

A neural model consisting of paired cerebral hemispheric regions interacting via homotopic callosal connections was trained to generate pronunciations for 50 monosyllabic words. Lateralization of this task occurred readily when different underlying cortical asymmetries were present. Following simulated focal cortical lesions of systematically varied sizes, acute changes in the distribution of cortical activation were found to be most consistent with experimental data when interhemispheric interactions were assumed to be excitatory. During subsequent recovery, the contribution of the unlesioned hemispheric region to performance improvement was a function of both the amount of preexisting lateralization and the side and size of the lesion. These results are discussed in the context of unresolved issues concerning the mechanisms underlying language lateralization, the nature of interhemispheric interactions, and the role of the nondominant hemisphere in recovery from adult aphasia.