Neural networks and neurology

Guest editorial: Neural networks and neurology This Special Issue of Neurological Research focussing on neural networks and neurology is concerned with the relations between artiŽcial neural networks (ANNs) and neurology and with the applications of neural networks to solving problems in the neurological sciences. ANNs were developed to take advantage of the body of knowledge on physiological neural networks (PNNs) due to work by neuroscientists in the areas of neurology, neurophysiology, neuroanatomy, neurosurgery, psychiatry, psychology, biophysics and biochemistry, in order to obtain mathematical/computational models, termed ANNs. The latter are to serve for improving computing machines and methods, and also for enhancing the physical and the computational understanding of PNN in terms of its networking structure and rules. From its early days, ANN basic research has been conducted through co-operation and interaction between the medical/physiological/psychological community and the mathematical/engineering/computerscience community. In this respect, it is interesting to observe that the paper that was recognized to have started ANN research (McCulloch WS, Pitts W, Bull Math Biophys 194 3; 5: 115–11 3) was authored by a psychiatrist (Warren McCulloch) and a mathematician (Walter Pitts). However, by their PNN (though gross) relation, ANNs have become very powerful tools for modeling and computing in situations which defy or partly defy analytical/mathematical tools, such as found in many medical problems and also in Žnance, in industrial and medical signal and image processing, in management and beyond. These tended to dominate applications to a degree that ANN became identiŽed more with computer science and electrical network engineering. Hence, in the medical/neurologicalsciences community, ANN often became considered as an ‘outside’, though a useful tool, and emphasis on its PNN foundations and relations became overshadowed even among many neurological scientists. The motivation for this Special Issue is to bring together authors whose background is medical with those having an engineering/computer-science background, when co-operating in joint work that aims at providing insight into relations between ANN and PNN or which describe ANN applications to the neurosciences. As such, this Special Issue is probably one of the Žrst such issues to appear in a neurological science journal and that concerns ANN and neurological sciences per se. We must emphasize that although ANNs are applicable to a wide range of problems well beyond neurology (or medicine in general), ANNs are very natural tools for neurological science applications due to the fact that these networks are based on neurological principles and on PNN models. # 2001 Forefront Publishing Group 0161–6412/01/050427–02

The following eight papers are published here. On the inter-relations between artiŽcial and physiological neural networks – (Graupe and Vern): This paper is a short review of the inter-relations between ANNs and PNNs. It explains the basic principles of ANNs and their PNN parallels and origins. Subsequently, it briey outlines several important variants of ANN designs. It also discusses how ANN designs that arose from mathematical necessity or efŽciency but with no proven PNN parallels, may result (and, at least in one important case, have already resulted in) new PNN Žndings. The Callosal Dilema: Explaining diaschisis in the context of hemispheric rivalry via a neural network model – (Reggia, Godall, Shkuro and Glezer): This paper considers the problem of loss of transcallosal excitation of the intact hemisphere from the lesioned one in diaschisis. For this purpose, it presents and employs a novel single ANN model for diaschisis that combines excitory callosal inuences with subcortical cross-midline inhibitory interactions. This ANN models left and right hemispheric regions, interconnected by a simulated excitory corpus callosum, to explore how laterization emerges and how diaschisis occurs following acute local damage. The ANN study suggests that subcortical competitive processes may be a more important factor in cerebral specialization than is generally recognized. EEG source localisation: A neural network approach – (Sclabassi, Sonmez and Sun): The paper studies the properties of current source (dipole) localization as observed in scalp electroencephalograms, by means of ANNs, to localize functional activity in the brain. The paper considers solutions to the localization problems in terms of (a) the Forward Problem (mapping solutions to the source-to-resultant-surface-potential equation), for a ‘true’ head shape, and (b) the Inverse Problem (Žtting EEG data to derive dipole localization). A Backpropagation (BP) ANN is employed to learn and then to obtain dipole localization via method (a) in a computationally efŽcient manner on a ‘true’ head model. The paper also examines analytical solutions via method (b) and uses an ANN as a subsequent decision making tool between such solutions. Estimation of intra-cranial neural activity by means of regularized neural-network-based inversion techniques – (Kosugi, Uemoto, Hayashi and He): In this paper a BPbased ANN is employed for a three-dipole localization situation, via solving the Inverse Problem (i.e., from EEG data to current source) for human visual evoked potential data. For the solution, a Regularized Network-Inversion technique is applied to the ANN. The Neurological Research, 2001, Volume 2 3, July 427

Guest editorial: Neural networks and neurology: Daniel Graupe

same ANN regularized network inversion technique is also applied to PET (positron emission tomography) data for image restoration. Cortical spreading depression and the pathogenesis of brain disorders: A computational and neural-networkbased investigation – (Ruppin and Reggia): The paper is concerned with the role of cortical spreading depression (CSD) in pathogenesis of brain disorders. It incorporates a model of neuro–metabolic changes of CSD within an ANN to allow explicit predictions of the patterns of ischemic damage following acute focal ischemic stroke. The ANN-based CSD model also yields simulation of normoxic migraine aura. The study supports the hypothesis that CSD does play an important role in these and other neurological disorders. Neural network classiŽcation of nerve activity recorded in a mixed nerve – (Jezernik, Grill and Sinkjaer): This paper is concerned with the electrical activity in peripheral nerves. It employs an ANN to discriminate between different nerve Žbers that are being superpositioned when recorded in a nerve cuff electrode such as used for chronic implantation in animals. Results are given for electrical activity in a cat’s S-1 sacral spinal root to indicate adequate discrimination between nerve activities arising from different neurons. Control of neuromuscular stimulation for ambulation by complete paraplegics via artiŽcial neural networks – (Kordylewski and Graupe): This paper presents an ART (Adaptive Resonance Theory)-based ANN for controlling functional neuromuscular stimulation (FNS) of peripheral nerves in traumatic, thoracic-level complete paraplegics, to allow them to ambulate independently via FNS. Since FNS triggers action potentials in the peripheral nerves involved (at below the spinal cord lesion), the ANN thus controls a (peripheral) PNN. The ANN performs its control functions by utilizing both above-lesion EMG signals and below-lesion response EMG (REMG) signals, noting that at below-lesion all neural activity is solely due to FNS. Neural models for auditory localization based on spectral cues – (Nandy and Ben-Arie): The paper discusses localization in a different sense than the two

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papers (Sclabassi et al. and Kosugi et al.) mentioned above. Here one is concerned with the localization (by the efferent nervous system) of an external sound source in the environment, and not with localization of (electrical activity) sources inside the brain. Several ANN models are employed and compared for solving the present localization problem. The ANNs utilize spectral information as extracted by the cochlear nucleus. It is hoped that these eight papers will provide insight into the nature of ANN in relation to neurology and to PNN. This Special Issue of Neurological Research speciŽcally aims at providing insights as to the power of ANNs in solving concrete and important problems in the neurosciences. As computational or control tools, ANNs are unique in their power of generalization and in their ability to yield good results in cases where analytical solutions fail or are not possible. As providers of understanding of the PNN, ANNs can serve to test hypotheses at the speed of an electronic computer. By their nature, ANNs should improve with advances in the understanding of the PNN, which is what the neurosciences are all about. The coming decades should see considerable advances in the understanding of the PNN via functional and Very-High Field MRI and via the progress in molecular biology. This should also enhance ANN capabilities. This, together with the expected wider availability of ANNs on microchips (rather than their use as special programs on regular computers), should lead to an expanded role of ANNs in the neurosciences and in the neurological practice and devices. Although a few ANNs on a chip are already manufactured, these are still very limited in their use and have no impact yet in medicine. However, the near future should see them available in devices, in instruments and for implantation in all Želds of the medical sciences and of medical practice. Finally, the Guest Editor wishes to express his deep gratitude to the authors and the co-authors of the papers of this issue for their important contributions. He is most grateful to Dr Manual Dujovny for his great efforts in making this Publication possible. Daniel Graupe University of Illinois at Chicago