[Cell meets silicon--biomedical sensing for diagnosis and therapy].
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Animals↗
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Exploring the impact of plasticity-related recovery after brain damage in a connectionist model of single-word reading.
The effect of retraining a damaged connectionist model of single-word reading was investigated with the aim of establishing whether plasticity-related changes occurring during the recovery process can contribute to our understanding of the pattern of dissociations found in brain-damaged patients. In particular, we sought to reproduce the strong frequency x consistency interactions found in surface dyslexia. A replication of Plaut, McClelland, Seidenberg, and Patterson's (1996) model of word reading was damaged and then retrained, using a standard backpropagation algorithm. Immediately after damage, there was only a small frequency x consistency interaction. Retraining the damaged model crystallized out these small differences into a strong dissociation, very similar to the pattern found in surface dyslexic patients. What is more, the percentage of regularization errors, always high in surface dyslexics, increased greatly over the retraining period, moving from under 10% to over 80% in some simulations. These results suggest that the performance patterns of brain-damaged patients can owe as much to the substantial changes in the pattern of connectivity occurring during recovery as to the original premorbid structure. This finding is discussed in relation to the traditional cognitive neuropsychological assumptions of subtractivity and transparency.
Algorithms↗
An intelligent system for the tracking--localization of changes & exploratory analysis--of long-term ECG.
An interactive methodology for the analysis of long-term ECG is introduced. It is an anthropomimetic technique and consists of three parts. At the first stage, the clinicians' scan of the ECG traces is imitated and changes in the shape of QRS are quantified. In the sequel, a clinician involves in the interpretation of the most prominent changes providing the patient-dependent prototypes for the subsequent machine learning procedure. Finally, a classification scheme incorporates the portion of medical knowledge needed to explore the whole patient's ECG. This scheme, being very robust to noise, presents excellent generalization properties and can serve as a reliable automation in a future examination of the certain subject.
Arrhythmias, Cardiac↗
Different roles for simple-cell and complex-cell inhibition in V1.
Previously, we proposed a model of the circuitry underlying simple-cell responses in cat primary visual cortex (V1) layer 4. We argued that the ordered arrangement of lateral geniculate nucleus inputs to a simple cell must be supplemented by a component of feedforward inhibition that is untuned for orientation and responds to high temporal frequencies to explain the sharp contrast-invariant orientation tuning and low-pass temporal frequency tuning of simple cells. The temporal tuning also requires a significant NMDA component in geniculocortical synapses. Recent experiments have revealed cat V1 layer 4 inhibitory neurons with two distinct types of receptive fields (RFs): complex RFs with mixed ON/OFF responses lacking in orientation tuning, and simple RFs with normal, sharp-orientation tuning (although, some respond to all orientations). We show that complex inhibitory neurons can provide the inhibition needed to explain simple-cell response properties. Given this complex cell inhibition, antiphase or "push-pull" inhibition from tuned simple inhibitory neurons acts to sharpen spatial frequency tuning, lower responses to low temporal frequency stimuli, and increase the stability of cortical activity.
Animals↗
The effects of spontaneous activity, background noise, and the stimulus ensemble on information transfer in neurons.
Information theory is playing an increasingly important role in the analysis of neural data as it can precisely quantify the reliability of stimulus-response functions. Estimating the mutual information between a neural spike train and a time varying stimulus is, however, not trivial in practice and requires assumptions about the specific computations being performed by the neuron under study. Consequently, estimates of the mutual information depend on these assumptions and their validity must be ascertained in the particular physiological context in which experiments are carried out. Here we compare results obtained using different information measures that make different assumptions about the neural code (i.e. the way information is being encoded and decoded) and the stimulus ensemble (i.e. the set of stimuli that the animal can encounter in nature). Our comparisons are carried out in the context of spontaneously active neurons. However, some of our results are also applicable to neurons that are not spontaneously active. We first show conditions under which a single stimulus provides a good sample of the entire stimulus ensemble. Furthermore, we use a recently introduced information measure that is based on the spontaneous activity of the neuron rather than on the stimulus ensemble. This measure is compared to the Shannon information and it is shown that the two differ only by a constant. This constant is shown to represent the information that the neuron's spontaneous activity transmits about the fact that no stimulus is present in the animal's environment. As a consequence, the mutual information measure based on spontaneous activity is easily applied to stimuli that mimic those seen in nature, as it does not require a priori knowledge of the stimulus ensemble. Finally, we consider the effect of noise in the animal's environment on information transmission about sensory stimuli. Our results show that, as expected, such 'background' noise will increase the trial-to-trial variability of the neural response to repeated presentations of a stimulus. However, the same background noise can also increase the variability of the spike train and hence can lead to increased information transfer in the presence of background noise. Our study emphasizes how different assumptions can lead to different predictions for the information transmission of a neuron. Assumptions about the computations being performed by the system under study as well as the stimulus ensemble and background noise should therefore be carefully considered when applying information theory.
Action Potentials↗
Edge encoding mechanisms in Musca domestica.
Musca domestica, the common house fly, has a simple yet powerful and accessible vision system. Cajal indicated in 1885 the vision system is the same as in the human retina [1]. We have modeled the intracellular connections of the vision system and have applied high resolution images to the model. Even though the animal does not have what we would consider a high resolution imaging system, it is still capable of high resolution and we are using the same processing principles to extract high resolution information from the visual input. In this paper we will discuss how we are using a purely local process from a single cartridge within the vision system to encode edge information: magnitude, orientation, displacement, and tilt. We also investigate a cooperative approach to link cartridges that have common numerical features in their field of view to share their information for potential edge connection and feature extraction.
Animals↗
[The testing and verification for interconnect faults based on cluster FPGA configuration].
We have developed a hierarchical approach to define a set of FPGA configurations to solve the interconnect testing problem. This technique enables the detection, testing and verification of bridging faults involving intracluster interconnect and extracluster interconnect to be done easily.
Algorithms↗
Spatio-temporal influences at the neural level of object recognition.
In late 1988, Miyashita published work reporting recordings of single cells in the inferotemporal cortex of the macaque monkey (Miyashita 1988 Nature 335 817-20). He described the responses of neurons to a sequence of random fractal pattern images, and how many of the neurons tested were seen to respond strongly to a subset of the images on the basis of sequence presentation order, i.e. appearance in time, rather than their spatial similarity. In this work, I describe a local Hebb-like learning rule which in conjunction with a simple feedforward neural architecture is capable of replicating the type of temporal-order association apparent in the cells from which he made recordings. The paper also advances reasons for requiring such learning by describing its possible role in establishing transformation invariant representations of objects.
Animals↗
Cardiovascular regulation: a modelling approach.
A detailed analysis of autonomic cardiovascular control (ACVC) may provide a key to a better understanding of the mechanisms underlying postflight orthostatic hypotension. The central substrate of human ACVC is not directly accessible to measurements and observation in space research. Modelling--supporting inference and physiological reasoning--is a valuable tool to disclose its involvement We are currently determining the suitability of artificial neural networks (ANN's) as a model of the central substrate of ACVC. Having conducted a number of experiments with simulated tilt test data to clarify the choice of input coding and of architectural biases in network training we will now report on the approximation of data obtained from human subjects during preparation of the German MIR'97 and D-2 missions.
Adaptation, Physiological↗
Model-free functional MRI analysis based on unsupervised clustering.
Conventional model-based or statistical analysis methods for functional MRI (fMRI) are easy to implement, and are effective in analyzing data with simple paradigms. However, they are not applicable in situations in which patterns of neural response are complicated and when fMRI response is unknown. In this paper the "neural gas" network is adapted and rigourosly studied for analyzing fMRI data. The algorithm supports spatial connectivity aiding in the identification of activation sites in functional brain imaging. A comparison of this new method with Kohonen's self-organizing map and with a fuzzy clustering scheme based on deterministic annealing is done in a systematic fMRI study showing comparative quantitative evaluations. The most important findings in this paper are: (1) both "neural gas" and the fuzzy clustering technique outperform Kohonen's map in terms of identifying signal components with high correlation to the fMRI stimulus, (2) the "neural gas" outperforms the two other methods with respect to the quantization error, and (3) Kohonen's map outperforms the two other methods in terms of computational expense. The applicability of the new algorithm is demonstrated on experimental data.
Adult↗
Density codes and shape spaces.
This paper presents an algorithm that allows for encoding probability density functions associated to samples of points of R(n). The resulting code is a sequence of points of R(n) whose density function approximates that of the set of data points. However, contrarily to sampled data points, code points associated to two different density functions can be matched, which allows to efficiently compare such functions. Moreover, the comparison of two codes can be made invariant to a wide variety of geometrical transformations of the support coordinates, provided that the Jacobian matrix of the transformation be everywhere triangular, with a strictly positive diagonal. Such invariances are commonly encountered in visual shape recognition, for example. Thus, using this tool, one can build spaces of shapes that are suitable input spaces for pattern recognition and pattern analysis neural networks. Moreover, a parallel neural implementation of the encoding algorithm is available for 2D image data.
Algorithms↗
A global optimization strategy for predicting alpha-helical protein tertiary structure.
We present a global optimization strategy that incorporates predicted restraints in both a local optimization context and as directives for global optimization approaches, to predict protein tertiary structure for alpha-helical proteins. Specifically, neural networks are used to predict the secondary structure of a protein, restraints are defined as manifestations of the network with a predicted secondary structure and the secondary structure is formed using local minimizations on a protein energy surface, in the presence of the restraints. Those residues predicted to be coil, by the network, define a conformational sub-space that is subject to optimization using a global approach known as stochastic perturbation that has been found to be effective for Lennard-Jones clusters and homo-polypeptides. Our energy surface is an all-atom 'gas phase' molecular mechanics force field, that is combined with a new solvation energy function that penalizes hydrophobic group exposure. This energy function gives the crystal structure of four different alpha-helical proteins as the lowest energy structure relative to other conformations, with correct secondary structure but incorrect tertiary structure. We demonstrate this global optimization strategy by determining the tertiary structure of the A-chain of the alpha-helical protein, uteroglobin and of a four-helix bundle, DNA binding protein.
Algorithms↗
[The modelling of the process of image-sequence fixation and reproduction in a projective-associative network made up of excitatory neuron-like elements].
On mathematic model of several interconnected networks of excitatory neurone-like elements realized in the form of program on computer "Nord-100", a study of conditions of fixation and reproduction of symbols (words) succession was conducted. Connections between the receptive (C1 and C2) and associative (A1 and A2) networks were by the principle "one to one", connections between the networks A1 and A2 with reinforcing general activating network (GAN) were by the principle "all with all". Possibility was shown of restoration of images succession fixed in the network on the basis of the principle of chain conditioned reflexes provided a successive change of reinforcing GAN elements by means of the decrease of the threshold of their activation. It was found that contacts transferring the influences of the reinforcing network at learning by the Hebb principle, must either initially exert a subthreshold action or be "unlearning" for the elimination of the process of overexcitation.
Animals↗
Biological systems drug infusion controller using FREN with sliding bounds.
In this paper, a direct adaptive control for drug infusion of biological systems is presented. The proposed controller is accomplished using our adaptive network called Fuzzy Rules Emulated Network (FREN). The structure of FREN resembles the human knowledge in the form of fuzzy IF-THEN rules. After selecting the initial value of network's parameters, an on-line adaptive process based on Lyapunov's criteria is performed to improve the controller performance. The control signal from FREN is designed to keep in the region which is calculated by the modified Sliding Mode Control (SMC). The simulation results indicate that the proposed algorithm can satisfy the setting point and the robust performance.
Algorithms↗
A new formalism for temporal modeling in medical decision-support systems.
We present a new mathematical formalism, which we call modifiable temporal belief networks (MTBNs) that extends the concept of an ordinary belief network (BN) to incorporate a dynamic causal structure and explicit temporal semantics. An important feature of MTBNs is that they allow portions of the model to be abstract and portions of it to be temporally explicit. We show how this property can lead to substantial knowledge acquisition and computational complexity savings. In addition to temporal modeling, the language of MTBNs can be an important analytical tool, as well as temporal language for causal discovery.
Decision Making, Computer-Assisted↗
Simulation of complex movements using artificial neural networks.
A simulated network for controlling a six-legged, insect-like walking system is proposed. The network contains internal recurrent connections, but important recurrent connections utilize the loop through the environment. This approach leads to a subnet for controlling the three joints of a leg during its swing which is arguably the simplest possible solution. The task for the stance subnet appears more difficult because the movements of a larger and varying number of joints (9-18: three for each leg in stance) have to be controlled such that each leg contributes efficiently to support and propulsion and legs do not work at cross purposes. Already inherently non-linear, this task is further complicated by four factors: 1) the combination of legs in stance varies continuously. 2) during curve walking, legs must move at different speeds, 3) on compliant substrates, the speed of the individual legs may vary unpredictably, and 4) the geometry of the system may vary through growth and injury or due to non-rigid suspension of the joints. This task appears to require some kind of "motor intelligence". We show that an extremely decentralized, simple controller, based on a combination of negative and positive feedback at the joint level, copes with all these problems by exploiting the physical properties of the system.
Animals↗
Network modeling of adult neurogenesis: shifting rates of neuronal turnover optimally gears network learning according to novelty gradient.
Apoptotic and neurogenic events in the adult hippocampus are hypothesized to play a role in cognitive responses to new contexts. Corticosteroid-mediated stress responses and other neural processes invoked by substantially novel contextual changes may regulate these processes. Using elementary three-layer neural networks that learn by incremental synaptic plasticity, we explored whether the cognitive effects of differential regimens of neuronal turnover depend on the environmental context in terms of the degree of novelty in the new information to be learned. In "adult" networks that had achieved mature synaptic connectivity upon prior learning of the Roman alphabet, imposition of apoptosis/neurogenesis before learning increasingly novel information (alternate Roman < Russian < Hebrew) reveals optimality of informatic cost benefits when rates of turnover are geared in proportion to the degree of novelty. These findings predict that flexible control of rates of apoptosis-neurogenesis within plastic, mature neural systems optimizes learning attributes under varying degrees of contextual change, and that failures in this regulation may define a role for adult hippocampal neurogenesis in novelty- and stress-responsive psychiatric disorders.
Analysis of Variance↗
Factors that predict better synchronizability on complex networks.
While shorter characteristic path length has in general been believed to enhance synchronizability of a coupled oscillator system on a complex network, the suppressing tendency of the heterogeneity of the degree distribution, even for shorter characteristic path length, has also been reported. To see this, we investigate the effects of various factors such as the degree, characteristic path length, heterogeneity, and betweenness centrality on synchronization, and find a consistent trend between the synchronization and the betweenness centrality. The betweenness centrality is thus proposed as a good indicator for synchronizability.
Computer Simulation↗