Static and dynamic organization of motor cortex.
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Animals↗
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Dynamics of single neuron activity in monkey primary motor cortex related to sensorimotor transformation.
We investigated the dynamics of neuronal activity related to sensorimotor transformation during single experimental trials of a given stimulus-response (S-R) association task. A monkey was trained to perform wrist extension/flexion movements in the horizontal plane to align a pointer with a visual target while single unit activity in the primary motor cortex (MI) was being recorded. The stimulus was a colored light-emitting diode (LED) presented to either the left or right of a central reference point. The monkey had to point directly at the target ("compatible" S-R mapping) or point to the opposite side of the target position ("incompatible" S-R mapping), with the mapping rule specified by the color of the LED. Single neuron activities on the four correct trials (left/right stimulus x compatible/incompatible S-R mapping) were compared to determine whether such activities were more related to stimulus encoding and representation, to response preparation and execution, or to the "decision" processes translating the stimulus representation into a response representation. A novel mathematical technique, called LOCUS ANALYSIS, has been developed to quantitatively analyze and visualize the contribution of neuronal activity toward the sensory, motor, or sensorimotor (i.e., decisional) aspects of the task. Our data show that as a trial evolves, neuronal activity in MI, at a population level, is first correlated with the representation of the specific stimulus (the side of LED), then with the representation of the S-R mapping rule (the color of LED) as well as trial-specific S-R association (the conjunction of stimulus side and stimulus color), and finally with the representation of the behavioral response (extension or flexion wrist movement). Immediately after the issuance of the movement command, the populational activity in MI remains correlated with the trial-specific stimulus-response conjunctions, i.e., the context of the motor decision that the monkey has just made. Cells recorded successively in a single penetration tend to resemble each other in their pattern of firing on the four correct trials, suggesting a modular organization of neurons based on their functional role in the processing of the S-R association task. Our results indicate that MI belongs to a distributed network such that its neuronal activity reflects the underlying network dynamics that translate a stimulus representation into a response representation via the activation and application of appropriate S-R mapping rule.
Animals↗
Emulating the visual receptive-field properties of MST neurons with a template model of heading estimation.
We have proposed previously a computational neural-network model by which the complex patterns of retinal image motion generated during locomotion (optic flow) can be processed by specialized detectors acting as templates for specific instances of self-motion. The detectors in this template model respond to global optic flow by sampling image motion over a large portion of the visual field through networks of local motion sensors with properties similar to those of neurons found in the middle temporal (MT) area of primate extrastriate visual cortex. These detectors, arranged within cortical-like maps, were designed to extract self-translation (heading) and self-rotation, as well as the scene layout (relative distances) ahead of a moving observer. We then postulated that heading from optic flow is directly encoded by individual neurons acting as heading detectors within the medial superior temporal (MST) area. Others have questioned whether individual MST neurons can perform this function because some of their receptive-field properties seem inconsistent with this role. To resolve this issue, we systematically compared MST responses with those of detectors from two different configurations of the model under matched stimulus conditions. We found that the characteristic physiological properties of MST neurons can be explained by the template model. We conclude that MST neurons are well suited to support self-motion estimation via a direct encoding of heading and that the template model provides an explicit set of testable hypotheses that can guide future exploration of MST and adjacent areas within the superior temporal sulcus.
Head Movements↗
Contextual quick-learning and generalization by humans and machines.
In a previous study (1994 Network: Comput. Neural Syst. 5 203-27) we compared human quick-learning and generalization (quick modelling) with that of neural nets (feedforward architectures), symbolic algorithms (decision tree procedures), and pattern classifiers (truth-set descriptors). Those studies raised the question of the role of context in the nature and rapidity of human learning. Here we address that issue in the setting of the same basic experiment (Quinlan classification problem) used for the previous studies. A major implication of our findings is that humans overwhelmingly seek, create, or imagine context in order to provide meaning when presented with abstract or apparently incomplete or contradictory or otherwise untenable situations.
Adolescent↗
Cryptococcus neoformans chemotyping by quantitative analysis of 1H nuclear magnetic resonance spectra of glucuronoxylomannans with a computer-simulated artificial neural network.
The complete assignment of the proton chemical shifts obtained by nuclear magnetic resonance (NMR) spectroscopy of de-O-acetylated glucuronoxylomannans (GXMs) from Cryptococcus neoformans permitted the high-resolution determination of the total structure of any GXM. Six structural motifs based on an alpha-(1-->3)-mannotriose substituted with variable quantities of 2-O-beta- and 4-O-beta-xylopyranosyl and 2-O-beta-glucopyranosyluronic acid were identified. The chemical shifts of only the anomeric protons of the mannosyl residues served as structure reporter groups (SRG) for the identification and quantitation of the six triads present in any GXM. The assigned protons for the mannosyl residues resonated at clearly distinguishable positions in the spectrum and supplied all the information essential for the assignment of the complete GXM structure. This technique for assigning structure is referred to as the SRG concept. The SRG concept was used to analyze the distribution of the six mannosyl triads of GXMs obtained from 106 isolates of C. neoformans. The six mannosyl triads occurred singularly or in combination with one or more of the other triads. The identification and quantitation of the SRG were simplified by using a computer-simulated artificial neural network (ANN) to automatically analyze the SRG region of the one-dimensional proton NMR spectra. The occurrence and relative distribution of the six mannosyl triads were used to chemotype C. neoformans on the basis of subtle variations in GXM structure determined by analysis of the SRG region of the proton NMR spectrum by the ANN. The data for the distribution of the six SRGs from GXMs of 106 isolates of C. neoformans yielded eight chemotypes, Chem1 through Chem8.
Bacterial Typing Techniques↗
Neural network based optimal routing algorithm for communication networks.
This paper presents the capability of the neural networks as a computational tool for solving constrained optimization problem, arising in routing algorithms for the present day communication networks. The application of neural networks in the optimum routing problem, in case of packet switched computer networks, where the goal is to minimize the average delays in the communication have been addressed. The effectiveness of neural network is shown by the results of simulation of a neural design to solve the shortest path problem. Simulation model of neural network is shown to be utilized in an optimum routing algorithm known asflow deviation algorithm. It is also shown that the model will enable the routing algorithm to be implemented in real time and also to be adaptive to changes in link costs and network topology.
Algorithms↗
Personal computer system for ECG ST-segment recognition based on neural networks.
A personal computer system for electrocardiogram (ECG) ST-segment recognition is developed based on neural networks. The system consists of a preprocessor, neural networks and a recogniser. The adaptive resonance theory (ART) is employed to implement the neural networks in the system, which self-organise in response to the input ECG. Competitive and co-operative interaction among neurons in the neural networks makes the system robust to noise. The preprocessor detects the R points and divides the ECG into cardiac cycles. Each cardiac cycle is fed into the neural networks. The neural networks then address the approximate locations of the J point and the onset of the T-wave (T(on)). The recogniser determines the respective ranges in which the J and T(on) points lie, based on the locations addressed. Within those ranges, the recogniser finds the exact locations of the J and T(on) points either by a change in the sign of the slope of the ECG, a zero slope or a significant change in the slope. The ST-segment is thus recognised as the portion of the ECG between the J and T(on) points. Finally, the appropriateness of the length of the ST-segment is evaluated by an evaluation rule. As the process goes on, the neural networks self-organise and learn the characteristics of the ECG patterns which vary with each patient.(ABSTRACT TRUNCATED AT 250 WORDS)
Computer Systems↗
Peptide design by artificial neural networks and computer-based evolutionary search.
A technique for systematic peptide variation by a combination of rational and evolutionary approaches is presented. The design scheme consists of five consecutive steps: (i) identification of a "seed peptide" with a desired activity, (ii) generation of variants selected from a physicochemical space around the seed peptide, (iii) synthesis and testing of this biased library, (iv) modeling of a quantitative sequence-activity relationship by an artificial neural network, and (v) de novo design by a computer-based evolutionary search in sequence space using the trained neural network as the fitness function. This strategy was successfully applied to the identification of novel peptides that fully prevent the positive chronotropic effect of anti-beta1-adrenoreceptor autoantibodies from the serum of patients with dilated cardiomyopathy. The seed peptide, comprising 10 residues, was derived by epitope mapping from an extracellular loop of human beta1-adrenoreceptor. A set of 90 peptides was synthesized and tested to provide training data for neural network development. De novo design revealed peptides with desired activities that do not match the seed peptide sequence. These results demonstrate that computer-based evolutionary searches can generate novel peptides with substantial biological activity.
Amino Acid Sequence↗
Application of neural networks to pharmacodynamics.
Neural networks (NN) are computational systems implemented in software or hardware that attempt to simulate the neurological processing abilities of biological systems. A synopsis is presented of the operational characteristics, structures, and applications of NN. The NN technology has primarily been aimed at recognition science (e.g., handwriting, voice, signal, picture, image, pattern, etc.). It is pointed out that NN may also be particularly suitable to deal with pharmacokinetic (PK) and pharmacodynamic (PD) systems, especially in cases such as multivariate PK/PD population kinetics when the systems are so complex that modeling by a conventional structured model building technique is very troublesome. The main practical advantage of NN is the intrinsic ability to closely emulate virtually any multivariate system, including nonlinear systems, independently of structural/physiologic relevance. Thus, NN are most suitable to model the behavior of complex kinetic systems and unsuitable to model the structure. In a practical sense, this structure limitation may be inconsequential because NN in its multivariate formulation may consider any physiologic, clinical, or population variable that may influence the kinetic behavior. The application of NN in PD is demonstrated in terms of the ability of an NN to predict, by extrapolation, the central nervous system (CNS) activity of alfentanil. The drug was infused by a complex computer-controlled infusion scheme over 180 min with simultaneous recording of the CNS effect quantified by a fast Fourier transform power spectrum analysis. The NN was trained to recognize (emulate) the drug input-drug effect behavior of the PD system with the input-effect data for the 180 min as a training set.(ABSTRACT TRUNCATED AT 250 WORDS)
Alfentanil↗
On the complexity of computing and learning with multiplicative neural networks.
In a great variety of neuron models, neural inputs are combined using the summing operation. We introduce the concept of multiplicative neural networks that contain units that multiply their inputs instead of summing them and thus allow inputs to interact nonlinearly. The class of multiplicative neural networks comprises such widely known and well-studied network types as higher-order networks and product unit networks. We investigate the complexity of computing and learning for multiplicative neural networks. In particular, we derive upper and lower bounds on the Vapnik-Chervonenkis (VC) dimension and the pseudo-dimension for various types of networks with multiplicative units. As the most general case, we consider feedforward networks consisting of product and sigmoidal units, showing that their pseudo-dimension is bounded from above by a polynomial with the same order of magnitude as the currently best-known bound for purely sigmoidal networks. Moreover, we show that this bound holds even when the unit type, product or sigmoidal, may be learned. Crucial for these results are calculations of solution set components bounds for new network classes. As to lower bounds, we construct product unit networks of fixed depth with super-linear VC dimension. For sigmoidal networks of higher order, we establish polynomial bounds that, in contrast to previous results, do not involve any restriction of the network order. We further consider various classes of higher-order units, also known as sigma-pi units, that are characterized by connectivity constraints. In terms of these, we derive some asymptotically tight bounds. Multiplication plays an important role in both neural modeling of biological behavior and computing and learning with artificial neural networks. We briefly survey research in biology and in applications where multiplication is considered an essential computational element. The results we present here provide new tools for assessing the impact of multiplication on the computational power and the learning capabilities of neural networks.
Artificial Intelligence↗
3-D components of a biological neural network visualized in computer generated imagery. I. Macular receptive field organization.
Computer-assisted, 3-dimensional reconstructions of macular receptive fields and of their linkages into a neural network have revealed new information about macular functional organization. Both type I and type II hair cells are included in the receptive fields. The fields are rounded, oblong, or elongated, but gradations between categories are common. Cell polarizations are divergent. Morphologically, each calyx of oblong and elongated fields appears to be an information processing site. Intrinsic modulation of information processing is extensive and varies with the kind of field. Each reconstructed field differs in detail from every other, suggesting that an element of randomness is introduced developmentally and contributes to endorgan adaptability.
Acoustic Maculae↗
Selection of an optimal neural network architecture for computer-aided detection of microcalcifications--comparison of automated optimization techniques.
Many computer-aided diagnosis (CAD) systems use neural networks (NNs) for either detection or classification of abnormalities. Currently, most NNs are "optimized" by manual search in a very limited parameter space. In this work, we evaluated the use of automated optimization methods for selecting an optimal convolution neural network (CNN) architecture. Three automated methods, the steepest descent (SD), the simulated annealing (SA), and the genetic algorithm (GA), were compared. We used as an example the CNN that classifies true and false microcalcifications detected on digitized mammograms by a prescreening algorithm. Four parameters of the CNN architecture were considered for optimization, the numbers of node groups and the filter kernel sizes in the first and second hidden layers, resulting in a search space of 432 possible architectures. The area Az under the receiver operating characteristic (ROC) curve was used to design a cost function. The SA experiments were conducted with four different annealing schedules. Three different parent selection methods were compared for the GA experiments. An available data set was split into two groups with approximately equal number of samples. By using the two groups alternately for training and testing, two different cost surfaces were evaluated. For the first cost surface, the SD method was trapped in a local minimum 91% (392/432) of the time. The SA using the Boltzman schedule selected the best architecture after evaluating, on average, 167 architectures. The GA achieved its best performance with linearly scaled roulette-wheel parent selection; however, it evaluated 391 different architectures, on average, to find the best one. The second cost surface contained no local minimum. For this surface, a simple SD algorithm could quickly find the global minimum, but the SA with the very fast reannealing schedule was still the most efficient. The same SA scheme, however, was trapped in a local minimum on the first cost surface. Our CNN study demonstrated that, if optimization is to be performed on a cost surface whose characteristics are not known a priori, it is advisable that a moderately fast algorithm such as a SA using a Boltzman cooling schedule be used to conduct an efficient and thorough search, which may offer a better chance of reaching the global minimum.
Algorithms↗
Informational Capacity and Recall Quality in Sparsely Encoded Hopfield-like Neural Network: Analytical Approaches and Computer Simulation.
A sparsely encoded Hopfield-like attractor neural network is investigated analytically and by computer simulation. Informational capacity and recall quality are evaluated. Three analytical approaches are used: replica method (RM); method of statistical neurodynamics (SN); and single-step approximation (SS). Computer simulation confirmed the good accuracy of RM and SN for all levels of network activity. SS is accurate only for large sparseness. It is shown that informational capacity monotonically increases when sparseness increases, while recall quality changes nonmonotonically: initially it decreases and then increases. Computer simulation revealed the main features of network behaviour near the saturation which are not predicted by the used analytical approaches. Copyright 1997 Elsevier Science Ltd.
Journal Article↗
Artificial neural networks for molecular sequence analysis.
Artificial neural networks provide a unique computing architecture whose potential has attracted interest from researchers across different disciplines. As a technique for computational analysis, neural network technology is very well suited for the analysis of molecular sequence data. It has been applied successfully to a variety of problems, ranging from gene identification, to protein structure prediction and sequence classification. This article provides an overview of major neural network paradigms, discusses design issues, and reviews current applications in DNA/RNA and protein sequence analysis.
Algorithms↗
A neural network regression model for relative dose computation.
This paper describes a neural network (NN) regression model for relative dose computation. The input signal of the NN model includes depths and field sizes and the output is the relative dose, for example, percentage depth dose (%DD) or tissue-air ratio (TAR) in this paper. After a functional link has been created, the expressing ability of input patterns and the resolution of neural networks are enhanced. The trained neural network exhibits a good generalization and interpolation ability, and it can be easily extended to other relative dose regressions. We present two examples to verify the fitness of this model. The first calculates the percentage depth dose of a 14 MV x-ray beam for field sizes of 5 cm x 5 cm to 28 cm x 28 cm. The average error computed from the NN is less than 0.47% comparing with the original measured %DD. The second example calculates the TAR of a 4 MV x-ray beam for field sizes of 5 cm x 5 cm to 20 cm x 20 cm. In this example, the training data show that the average error computed from the NN is less than 0.48%, whereas that from the testing data is less than 0.37% after training. Such an NN model can be generalized to fit data for treatment planning with any linear accelerator and can also fit data stored as tissue-maximum ratio (TMR) and tissue-phantom ratio (TPR).
Linear Models↗
Neural network control of functional neuromuscular stimulation systems: computer simulation studies.
A neural network control system has been designed for the control of cyclic movements in Functional Neuromuscular Stimulation (FNS) systems. The design directly addresses three major problems in FNS control systems: customization of control system parameters for a particular individual, adaptation during operation to account for changes in the musculoskeletal system, and attaining resistance to mechanical disturbances. The control system was implemented by a two-stage neural network that utilizes a combination of adaptive feedforward and feedback control techniques. A new learning algorithm was developed to provide rapid customization and adaptation. The control system was evaluated in a series of studies on a computer simulated musculoskeletal model. The model of electrically stimulated muscle used in the study included nonlinear recruitment, linear dynamics, and multiplicative nonlinear torque-angle and torque-velocity scaling factors. The skeletal model consisted of a one-segment planar system with passive constraints on joint movement. Results of the evaluation have demonstrated that the control system can provide automated customization of the feedforward controller parameters for a given musculoskeletal system. It can account for changes in the musculoskeletal system by adapting the feedforward controller parameters on-line and it can resist the effects of mechanical disturbances. These results suggest that this design may be suitable for the control of FNS systems and other dynamic systems.
Adaptation, Physiological↗
Neural networks: models and neurons show hybrid vigor in real time.
Hybrid networks in which living neurons interact with digital or analog model neurons are providing insights into the role of neural and synaptic properties in shaping neural network activity.
Animals↗
The cerebellum and VOR/OKR learning models.
Although one particular model of the cerebellum, as proposed by Marr and Albus, provides a formal framework for understanding how heterosynaptic plasticity of Purkinje cells might be used for motor learning, the physiological details remain largely an engima. Developments in computational neuroscience and artificial neural networks applied to real control problems are essential to understand fully how workspace errors associated with movement performances can be converted into motor-command errors, and how these errors can then be used as one kind of synaptic input by motor-learning algorithms that are based on biologically plausible rules involving heterosynaptic plasticity. These developments, as well as recent advances in the study of cellular mechanisms of synaptic plasticity, form the basis for the detailed computational models of cerebellar motor learning that have been proposed. These models provide hints toward resolving a long-standing controversy in the oculomotor literature regarding the sites of adaptive changes in the vestibuloocular reflex (VOR) and the optokinetic eye movement response (OKR), and suggest new experiments to elucidate general mechanisms of sensory motor learning.
Animals↗