Tunable Neural Encoding of a Symbolic Robotic Manipulation Algorithm

We present a neurocomputational controller for robotic manipulation based on the recently developed “neural virtual machine” (NVM). The NVM is a purely neural recurrent architecture that emulates a Turing-complete, purely symbolic virtual machine. We program the NVM with a symbolic algorithm that solves blocks-world restacking problems, and execute it in a robotic simulation environment. Our results show that the NVM-based controller can faithfully replicate the execution traces and performance levels of a traditional non-neural program executing the same restacking procedure. Moreover, after programming the NVM, the neurocomputational encodings of symbolic block stacking knowledge can be fine-tuned to further improve performance, by applying reinforcement learning to the underlying neural architecture.

NeuroLISP: High-level symbolic programming with attractor neural networks

Despite significant improvements in contemporary machine learning, symbolic methods currently outperform artificial neural networks on tasks that involve compositional reasoning, such as goal-directed planning and logical inference. This illustrates a computational explanatory gap between cognitive and neurocomputational algorithms that obscures the neurobiological mechanisms underlying cognition and impedes progress toward human-level artificial intelligence. Because of the strong relationship between cognition and working memory control, we suggest that the cognitive abilities of contemporary neural networks are limited by biologically-implausible working memory systems that rely on persistent activity maintenance and/or temporal nonlocality. Here we present NeuroLISP, an attractor neural network that can represent and execute programs written in the LISP programming language. Unlike previous approaches to high-level programming with neural networks, NeuroLISP features a temporally-local working memory based on itinerant attractor dynamics, top-down gating, and fast associative learning, and implements several high-level programming constructs such as compositional data structures, scoped variable binding, and the ability to manipulate and execute programmatic expressions in working memory (i.e., programs can be treated as data). Our computational experiments demonstrate the correctness of the NeuroLISP interpreter, and show that it can learn non-trivial programs that manipulate complex derived data structures (multiway trees), perform compositional string manipulation operations (PCFG SET task), and implement high-level symbolic AI algorithms (first-order unification). We conclude that NeuroLISP is an effective neurocognitive controller that can replace the symbolic components of hybrid models, and serves as a proof of concept for further development of high-level symbolic programming in neural networks.

Compositional memory in attractor neural networks with one-step learning

Compositionality refers to the ability of an intelligent system to construct models out of reusable parts. This is critical for the productivity and generalization of human reasoning, and is considered a necessary ingredient for human-level artificial intelligence. While traditional symbolic methods have proven effective for modeling compositionality, artificial neural networks struggle to learn systematic rules for encoding generalizable structured models. We suggest that this is due in part to short-term memory that is based on persistent maintenance of activity patterns without fast weight changes. We present a recurrent neural network that encodes structured representations as systems of contextually-gated dynamical attractors called attractor graphs. This network implements a functionally compositional working memory that is manipulated using top-down gating and fast local learning. We evaluate this approach with empirical experiments on storage and retrieval of graph-based data structures, as well as an automated hierarchical planning task. Our results demonstrate that compositional structures can be stored in and retrieved from neural working memory without persistent maintenance of multiple activity patterns. Further, memory capacity is improved by the use of a fast store-erase learning rule that permits controlled erasure and mutation of previously learned associations. We conclude that the combination of top-down gating and fast associative learning provides recurrent neural networks with a robust functional mechanism for compositional working memory.

Using Directional Fibers to Locate Fixed Points of Recurrent Neural Networks

We introduce mathematical objects that we call "directional fibers," and show how they enable a new strategy for systematically locating fixed points in recurrent neural networks. We analyze this approach mathematically and use computer experiments to show that it consistently locates many fixed points in many networks with arbitrary sizes and unconstrained connection weights. Comparison with a traditional method shows that our strategy is competitive and complementary, often finding larger and distinct sets of fixed points. We provide theoretical groundwork for further analysis and suggest next steps for developing the method into a more powerful solver.

Humanoid Cognitive Robots That Learn by Imitating: Implications for Consciousness Studies

While the concept of a conscious machine is intriguing, producing such a machine remains controversial and challenging. Here, we describe how our work on creating a humanoid cognitive robot that learns to perform tasks via imitation learning relates to this issue. Our discussion is divided into three parts. First, we summarize our previous framework for advancing the understanding of the nature of phenomenal consciousness. This framework is based on identifying computational correlates of consciousness. Second, we describe a cognitive robotic system that we recently developed that learns to perform tasks by imitating human-provided demonstrations. This humanoid robot uses cause–effect reasoning to infer a demonstrator’s intentions in performing a task, rather than just imitating the observed actions verbatim. In particular, its cognitive components center on top-down control of a working memory that retains the explanatory interpretations that the robot constructs during learning. Finally, we describe our ongoing work that is focused on converting our robot’s imitation learning cognitive system into purely neurocomputational form, including both its low-level cognitive neuromotor components, its use of working memory, and its causal reasoning mechanisms. Based on our initial results, we argue that the top-down cognitive control of working memory, and in particular its gating mechanisms, is an important potential computational correlate of consciousness in humanoid robots. We conclude that developing high-level neurocognitive control systems for cognitive robots and using them to search for computational correlates of consciousness provides an important approach to advancing our understanding of consciousness, and that it provides a credible and achievable route to ultimately developing a phenomenally conscious machine.

A cortically-inspired model for inverse kinematics computation of a humanoid finger with mechanically coupled joints

The human hand's versatility allows for robust and flexible grasping. To obtain such efficiency, many robotic hands include human biomechanical features such as fingers having their two last joints mechanically coupled. Although such coupling enables human-like grasping, controlling the inverse kinematics of such mechanical systems is challenging. Here we propose a cortical model for fine motor control of a humanoid finger, having its two last joints coupled, that learns the inverse kinematics of the effector. This neural model functionally mimics the population vector coding as well as sensorimotor prediction processes of the brain's motor/premotor and parietal regions, respectively. After learning, this neural architecture could both overtly (actual execution) and covertly (mental execution or motor imagery) perform accurate, robust and flexible finger movements while reproducing the main human finger kinematic states. This work contributes to developing neuro-mimetic controllers for dexterous humanoid robotic/prosthetic upper-extremities, and has the potential to promote human-robot interactions.

Lambda and the edge of chaos in recurrent neural networks

The idea that there is an edge of chaos, a region in the space of dynamical systems having special meaning for complex living entities, has a long history in artificial life. The significance of this region was first emphasized in cellular automata models when a single simple measure, λCA, identified it as a transitional region between order and chaos. Here we introduce a parameter λNN that is inspired by λCA but is defined for recurrent neural networks. We show through a series of systematic computational experiments that λNN generally orders the dynamical behaviors of randomly connected/weighted recurrent neural networks in the same way that λCA does for cellular automata. By extending this ordering to larger values of λNN than has typically been done with λCA and cellular automata, we find that a second edge-of-chaos region exists on the opposite side of the chaotic region. These basic results are found to hold under different assumptions about network connectivity, but vary substantially in their details. The results show that the basic concept underlying the lambda parameter can usefully be extended to other types of complex dynamical systems than just cellular automata.

Towards a multi-level neural architecture that unifies self-intended and imitated arm reaching performance

Dexterous arm reaching movements are a critical feature that allow human interactions with tools, the environment, and socially with others. Thus the development of a neural architecture providing unified mechanisms for actual, mental, observed and imitated actions could enhance robot performance, enhance human-robot social interactions, and inform specific human brain processes. Here we present a model, including a fronto-parietal network that implements sensorimotor transformations (inverse kinematics, workspace visuo-spatial rotations), for self-intended and imitation performance. Our findings revealed that this neural model can perform accurate and robust 3D actual/mental arm reaching while reproducing human-like kinematics. Also, using visuo-spatial remapping, the neural model can imitate arm reaching independently of a demonstrator-imitator viewpoint. This work is a first step towards providing the basis of a future neural architecture for combining cognitive and sensorimotor processing levels that will allow for multi-level mental simulation when executing actual, mental, observed, and imitated actions for dexterous arm movements.

Recurrent neural collective classification

With the recent surge in availability of data sets containing not only individual attributes but also relationships, classification techniques that take advantage of predictive relationship information have gained in popularity. The most popular existing collective classification techniques have a number of limitations-some of them generate arbitrary and potentially lossy summaries of the relationship data, whereas others ignore directionality and strength of relationships. Popular existing techniques make use of only direct neighbor relationships when classifying a given entity, ignoring potentially useful information contained in expanded neighborhoods of radius greater than one. We present a new technique that we call recurrent neural collective classification (RNCC), which avoids arbitrary summarization, uses information about relationship directionality and strength, and through recursive encoding, learns to leverage larger relational neighborhoods around each entity. Experiments with synthetic data sets show that RNCC can make effective use of relationship data for both direct and expanded neighborhoods. Further experiments demonstrate that our technique outperforms previously published results of several collective classification methods on a number of real-world data sets.

Modeling of visuospatial perspectives processing and modulation of the fronto-parietal network activity during action imitation

It has been suggested that the human mirror neuron system (MNS) plays a critical role in action observation and imitation. However, the transformation of perspective between the observed (allocentric) and the imitated (egocentric) actions has received little attention. We expand a previously proposed biologically plausible MNS model by incorporating general spatial transformation capabilities that are assumed to be encoded by the intraparietal sulcus (IPS) and the superior parietal lobule (SPL) as well as investigating their interactions with the inferior frontal gyrus and the inferior parietal lobule. The results reveal that the IPS/SPL could process the frame of reference and the viewpoint transformations, and provide invariant visual representations for the temporo-parieto-frontal circuit. This allows the imitator to imitate the action performed by a demonstrator under various perspectives while replicating results from the literatures. Our results confirm and extend the importance of perspective transformation processing during action observation and imitation.

A framework for the comparative study of language

Comparative studies of language are difficult because few language precursors are recognized. In this paper we propose a framework for designing experiments that test for structural and semantic patterns indicative of simple or complex grammars as originally described by Chomsky. We argue that a key issue is whether animals can recognize full recursion, which is the hallmark of context-free grammar. We discuss limitations of recent experiments that have attempted to address this issue, and point out that experiments aimed at detecting patterns that follow a Fibonacci series have advantages over other artificial context-free grammars. We also argue that experiments using complex sequences of behaviors could, in principle, provide evidence for fully recursive thought. Some of these ideas could also be approached using artificial life simulations, which have the potential to reveal the types of evolutionary transitions that could occur over time. Because the framework we propose has specific memory and computational requirements, future experiments could target candidate genes with the goal of revealing the genetic underpinnings of complex cognition.

A Framework for the Comparative Study of Language

Comparative studies of language are difficult because few language precursors are recognized. In this paper we propose a framework for designing experiments that test for structural and semantic patterns indicative of simple or complex grammars as originally described by Chomsky. We argue that a key issue is whether animals can recognize full recursion, which is the hallmark of context-free grammar. We discuss limitations of recent experiments that have attempted to address this issue, and point out that experiments aimed at detecting patterns that follow a Fibonacci series have advantages over other artificial context-free grammars. We also argue that experiments using complex sequences of behaviors could, in principle, provide evidence for fully recursive thought. Some of these ideas could also be approached using artificial life simulations, which have the potential to reveal the types of evolutionary transitions that could occur over time. Because the framework we propose has specific memory and computational requirements, future experiments could target candidate genes with the goal of revealing the genetic underpinnings of complex cognition.

The rise of machine consciousness: studying consciousness with computational models

Efforts to create computational models of consciousness have accelerated over the last two decades, creating a field that has become known as artificial consciousness. There have been two main motivations for this controversial work: to develop a better scientific understanding of the nature of human/animal consciousness and to produce machines that genuinely exhibit conscious awareness. This review begins by briefly explaining some of the concepts and terminology used by investigators working on machine consciousness, and summarizes key neurobiological correlates of human consciousness that are particularly relevant to past computational studies. Models of consciousness developed over the last twenty years are then surveyed. These models are largely found to fall into five categories based on the fundamental issue that their developers have selected as being most central to consciousness: a global workspace, information integration, an internal self-model, higher-level representations, or attention mechanisms. For each of these five categories, an overview of past work is given, a representative example is presented in some detail to illustrate the approach, and comments are provided on the contributions and limitations of the methodology. Three conclusions are offered about the state of the field based on this review: (1) computational modeling has become an effective and accepted methodology for the scientific study of consciousness, (2) existing computational models have successfully captured a number of neurobiological, cognitive, and behavioral correlates of conscious information processing as machine simulations, and (3) no existing approach to artificial consciousness has presented a compelling demonstration of phenomenal machine consciousness, or even clear evidence that artificial phenomenal consciousness will eventually be possible. The paper concludes by discussing the importance of continuing work in this area, considering the ethical issues it raises, and making predictions concerning future developments.

Symbolic representation of recurrent neural network dynamics

Simple recurrent error backpropagation networks have been widely used to learn temporal sequence data, including regular and context-free languages. However, the production of relatively large and opaque weight matrices during learning has inspired substantial research on how to extract symbolic human-readable interpretations from trained networks. Unlike feedforward networks, where research has focused mainly on rule extraction, most past work with recurrent networks has viewed them as dynamical systems that can be approximated symbolically by finite-state machine (FSMs). With this approach, the network's hidden layer activation space is typically divided into a finite number of regions. Past research has mainly focused on better techniques for dividing up this activation space. In contrast, very little work has tried to influence the network training process to produce a better representation in hidden layer activation space, and that which has been done has had only limited success. Here we propose a powerful general technique to bias the error backpropagation training process so that it learns an activation space representation from which it is easier to extract FSMs. Using four publicly available data sets that are based on regular and context-free languages, we show via computational experiments that the modified learning method helps to extract FSMs with substantially fewer states and less variance than unmodified backpropagation learning, without decreasing the neural networks' accuracy. We conclude that modifying error backpropagation so that it more effectively separates learned pattern encodings in the hidden layer is an effective way to improve contemporary FSM extraction methods.

Learning of spatial relationships between observed and imitated actions allows invariant inverse computation in the frontal mirror neuron system

It has been suggested that the human mirror neuron system can facilitate learning by imitation through coupling of observation and action execution. During imitation of observed actions, the functional relationship between and within the inferior frontal cortex, the posterior parietal cortex, and the superior temporal sulcus can be modeled within the internal model framework. The proposed biologically plausible mirror neuron system model extends currently available models by explicitly modeling the intraparietal sulcus and the superior parietal lobule in implementing the function of a frame of reference transformation during imitation. Moreover, the model posits the ventral premotor cortex as performing an inverse computation. The simulations reveal that: i) the transformation system can learn and represent the changes in extrinsic to intrinsic coordinates when an imitator observes a demonstrator; ii) the inverse model of the imitator's frontal mirror neuron system can be trained to provide the motor plans for the imitated actions.

Cortex inspired model for inverse kinematics computation for a humanoid robotic finger

In order to approach human hand performance levels, artificial anthropomorphic hands/fingers have increasingly incorporated human biomechanical features. However, the performance of finger reaching movements to visual targets involving the complex kinematics of multi-jointed, anthropomorphic actuators is a difficult problem. This is because the relationship between sensory and motor coordinates is highly nonlinear, and also often includes mechanical coupling of the two last joints. Recently, we developed a cortical model that learns the inverse kinematics of a simulated anthropomorphic finger. Here, we expand this previous work by assessing if this cortical model is able to learn the inverse kinematics for an actual anthropomorphic humanoid finger having its two last joints coupled and controlled by pneumatic muscles. The findings revealed that single 3D reaching movements, as well as more complex patterns of motion of the humanoid finger, were accurately and robustly performed by this cortical model while producing kinematics comparable to those of humans. This work contributes to the development of a bioinspired controller providing adaptive, robust and flexible control of dexterous robotic and prosthetic hands.

A CONNECTIONIST APPROACH TO VERTEX COVERING PROBLEMS

This paper describes a connectionist approach to solving computationally difficult minimum vertex covering problems. This approach uses the graph representing the vertex covering problem as the connectionist network without any modifications (nodes of the connectionist network represent vertices and links represent edges of the given graph). The activation rule governing node behavior is derived by breaking down the global constraints on a solution into local constraints on individual nodes. The resulting model uses a competitive activation mechanism to carry out the computation where vertices compete not by explicit inhibitory links but through common resources (edges). Convergence and other properties of this model are formally established by introducing a monotonically non-increasing global energy function. Simulation results show that this model yields very high accuracy, significantly outperforming a well-known sequential approximation algorithm.

A generalized LSTM-like training algorithm for second-order recurrent neural networks

The long short term memory (LSTM) is a second-order recurrent neural network architecture that excels at storing sequential short-term memories and retrieving them many time-steps later. LSTM's original training algorithm provides the important properties of spatial and temporal locality, which are missing from other training approaches, at the cost of limiting its applicability to a small set of network architectures. Here we introduce the generalized long short-term memory(LSTM-g) training algorithm, which provides LSTM-like locality while being applicable without modification to a much wider range of second-order network architectures. With LSTM-g, all units have an identical set of operating instructions for both activation and learning, subject only to the configuration of their local environment in the network; this is in contrast to the original LSTM training algorithm, where each type of unit has its own activation and training instructions. When applied to LSTM architectures with peephole connections, LSTM-g takes advantage of an additional source of back-propagated error which can enable better performance than the original algorithm. Enabled by the broad architectural applicability of LSTM-g, we demonstrate that training recurrent networks engineered for specific tasks can produce better results than single-layer networks. We conclude that LSTM-g has the potential to both improve the performance and broaden the applicability of spatially and temporally local gradient-based training algorithms for recurrent neural networks.

Guiding hidden layer representations for improved rule extraction from neural networks

The production of relatively large and opaque weight matrices by error backpropagation learning has inspired substantial research on how to extract symbolic human-readable rules from trained networks. While considerable progress has been made, the results at present are still relatively limited, in part due to the large numbers of symbolic rules that can be generated. Most past work to address this issue has focused on progressively more powerful methods for rule extraction (RE) that try to minimize the number of weights and/or improve rule expressiveness. In contrast, here we take a different approach in which we modify the error backpropagation training process so that it learns a different hidden layer representation of input patterns than would normally occur. Using five publicly available datasets, we show via computational experiments that the modified learning method helps to extract fewer rules without increasing individual rule complexity and without decreasing classification accuracy. We conclude that modifying error backpropagation so that it more effectively separates learned pattern encodings in the hidden layer is an effective way to improve contemporary RE methods.

The effects of multi‐task learning and time‐varying hemispheric asymmetry on lateralisation in a neural network model

While various functional and cognitive capabilities appear to differ in both degree and direction of lateralisation, the factors underlying these differences are poorly understood. It is hypothesised that time-varying asymmetry in plasticity between homologous regions in the cerebral hemispheres, coupled with asynchronous development of capabilities, may account for the lateralisation differences in two ways. First, the lateralisation of an earlier acquired behaviour may influence the lateralisation of a later developing behaviour. Second, temporal changes in the underlying plasticity asymmetry may also result in differences in lateralisation for functions acquired at different times. This study examines the plausibility of these hypotheses using a computational neural network model consisting of two interacting hemispheric regions and capable of learning two tasks. Lateralisation was measured while learning rates for the two hemispheres were changed independently over time to create a time-varying asymmetry in plasticity, and while the initiation of learning for each task was also varied over time. The results suggest that the lateralisation of one behaviour/function can affect the lateralisation of another in a fundamental way, and that experimentally observed temporal differences in hemispheric development can result in functional lateralisation differences, providing support for past theories of lateralisation based on asymmetric hemispheric growth and maturation.

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.