Biomedical subjects

James A Reggia

Publications and source records attributed to James A Reggia.

11 recordsLinked to original sources

PubMed2006

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.

Brain↗

PubMed2006

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.

Aphasia↗

PubMed2005

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 neurodevelopmental disorders.

Action Potentials↗

PubMed2004

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.

Dominance, Cerebral↗

PubMed2004

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.

Animals↗

PubMed2004

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.

Cerebral Cortex↗

PubMed2004

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.

Algorithms↗

PubMed2004

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.

Computer Simulation↗

PubMed2004

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.

Animals↗

PubMed2002

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.

Artificial Intelligence↗

PubMed2002

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.

Brain Injuries↗