A NOTE ON THE ELEMENTARY ALPHA-PERCEPTRON.
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Computers↗
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Conditions of activity bubble uniqueness in dynamic neural fields.
Dynamic neural fields (DNFs) offer a rich spectrum of dynamic properties like hysteresis, spatiotemporal information integration, and coexistence of multiple attractors. These properties make DNFs more and more popular in implementations of sensorimotor loops for autonomous systems. Applications often imply that DNFs should have only one compact region of firing neurons (activity bubble), whereas the rest of the field should not fire (e.g., if the field represents motor commands). In this article we prove the conditions of activity bubble uniqueness in the case of locally symmetric input bubbles. The qualitative condition on inhomogeneous inputs used in earlier work on DNFs is transfered to a quantitative condition of a balance between the internal dynamics and the input. The mathematical analysis is carried out for the two-dimensional case with methods that can be extended to more than two dimensions. The article concludes with an example of how our theoretical results facilitate the practical use of DNFs.
Action Potentials↗
Learning invariant object recognition in the visual system with continuous transformations.
The cerebral cortex utilizes spatiotemporal continuity in the world to help build invariant representations. In vision, these might be representations of objects. The temporal continuity typical of objects has been used in an associative learning rule with a short-term memory trace to help build invariant object representations. In this paper, we show that spatial continuity can also provide a basis for helping a system to self-organize invariant representations. We introduce a new learning paradigm "continuous transformation learning" which operates by mapping spatially similar input patterns to the same postsynaptic neurons in a competitive learning system. As the inputs move through the space of possible continuous transforms (e.g. translation, rotation, etc.), the active synapses are modified onto the set of postsynaptic neurons. Because other transforms of the same stimulus overlap with previously learned exemplars, a common set of postsynaptic neurons is activated by the new transforms, and learning of the new active inputs onto the same postsynaptic neurons is facilitated. We demonstrate that a hierarchical model of cortical processing in the ventral visual system can be trained with continuous transform learning, and highlight differences in the learning of invariant representations to those achieved by trace learning.
Computer Simulation↗
Negatively correlated firing: the functional meaning of lateral inhibition within cortical columns.
Lateral inhibition is a well documented aspect of neural architecture in the main sensory systems. Existing accounts of lateral inhibition focus on its role in sharpening distinctions between inputs that are closely related. However, these accounts fail to explain the functional role of inhibition in cortical columns, such as those in V1, where neurons have similar response properties. In this paper, we outline a model of position tracking using cortical columns of integrate-and-fire and Hodgkin-Huxley-type neurons which respond optimally to a particular location, to show that negatively correlated firing patterns arise from lateral inhibition in cortical columns and that this provides a clear benefit for population coding in terms of stability, accuracy, estimation time and neural resources.
Action Potentials↗
Fast cortical selection: a principle of neuronal self-organization for perception?
It is commonly accepted that larger visual objects are represented in the cerebral cortex by specific spatial patterns of neuronal activity. Self-organization is a key concept in the different explanations of such neuronal representations. We here propose as a hypothesis that fast cortical selection (FCS) is an intrinsic functional element of cortical self-organization during perception. Selection is a central concept in theoretical biology which has proved its explanatory power in different fields of our natural and cultural world. The central element in the cortical selection process is the pyramidal cell with its two types of excitatory input. In primary cortical areas one of these inputs comes from any of the sensory organs, determining the topological and typological receptive field properties of the cell and also driving it directly. The other type of input connects reciprocally neighbouring pyramidal cells by axon collaterals and only facilitates the driving input. These two functionally different inputs constitute the elementary selection system working by iterative mutual facilitation as a biological algorithm. A short simulation, based entirely on such biological facts, illustrates the dynamic of this selection process: the activity of cells responding better to the external stimulus 'grow and survive' the stimulation, whereas less responsive cells decrease their activity due to competition.
Algorithms↗
Two cortical circuits control propagating waves in visual cortex.
Visual stimuli produce waves of activity that propagate across the visual cortex of fresh water turtles. This study used a large-scale model of the cortex to examine the roles of specific types of cortical neurons in controlling the formation, speed and duration of these waves. The waves were divided into three components: initial depolarizations, primary propagating waves and secondary waves. The maximal conductances of each receptor type postsynaptic to each population of neurons in the model was systematically varied and the speed of primary waves, durations of primary waves and total wave durations were measured. The analyses indicate that wave formation and speed are controlled principally by feedforward excitation and inhibition, while wave duration is controlled principally by recurrent excitation and feedback inhibition.
Animals↗
Rallpacks: a set of benchmarks for neuronal simulators.
The field of computational neurobiology has advanced to the point where there are several general-purpose simulators to choose from. These cater to various niches in the world of realistic neuronal models, which range from the molecular level to descriptions of entire sensory modalities. In addition, there are numerous custom-designed simulations, adaptations of electrical circuit simulators, and other specific implementations of neurobiological models. As a first step towards evaluating this disparate set of simulators and simulations, and towards establishing standards for comparisons of speed and accuracy, we describe a set of benchmarks. These have been given the name 'Rallpacks' in honor of Wilfrid Rall, who pioneered the study of neuronal systems through analytical and numerical techniques.
Algorithms↗
Computer-aided diagnosis via model-based shape analysis: automated classification of wall motion abnormalities in echocardiograms.
RATIONALE AND OBJECTIVE: Shape analysis of endocardial contour sequences from echocardiograms can provide classification of wall motion abnormalities (WMA). MATERIALS AND METHODS: We previously reported on active appearance motion models (AAMM) for automated detection of endocardial contours in sequences of echocardiograms. The shape analysis of AAMM renders eigenvariations of shape/motion, including typical normal and pathologic endocardial contraction patterns. A set of stress echocardiograms (single-beat four-chamber and two-chamber sequences with expert-verified endocardial contours) of 129 infarct patients was split randomly into training (n = 65) and testing (n = 64) sets. AAMMs were generated from the training set and AAMM shape coefficients (ASCs) were extracted for all sequences and statistically related to regional/global visual wall motion scoring (VWMS) and volumetric parameters. RESULTS: Linear regression showed clear correlations between ASCs and VWMS. Discriminant analysis showed good prediction by ASCs of both segmental (74% correctness) and global WMA (90% correctness). Volumetric parameters correlated poorly to regional VWMS. CONCLUSION: 1) ASCs show promising accuracy for automated WMA classification. 2) VWMS and endocardial border motion are closely related; with accurate automated border detection, automated WMA classification should be feasible. 3) ASC shape analysis allows contour set evaluation by direct comparison to clinical parameters.
Cardiac Volume↗
Reducing multiclass cancer classification to binary by output coding and SVM.
Multiclass cancer classification based on microarray data is presented. The binary classifiers used combine support vector machines with a generalized output-coding scheme. Different coding strategies, decoding functions and feature selection methods are incorporated and validated on two cancer datasets: GCM and ALL. Using random coding strategy and recursive feature elimination, the testing accuracy achieved is as high as 83% on GCM data with 14 classes. Comparing with other classification methods, our method is superior in classificatory performance.
Artificial Intelligence↗
Learning receptive fields using predictive feedback.
Previously, it was suggested that feedback connections from higher- to lower-level areas carry predictions of lower-level neural activities, whereas feedforward connections carry the residual error between the predictions and the actual lower-level activities [Rao, R.P.N., Ballard, D.H., 1999. Nature Neuroscience 2, 79-87.]. A computational model implementing the hypothesis learned simple cell receptive fields when exposed to natural images. Here, we use predictive feedback to explain tuning properties in medial superior temporal area (MST). We implement the hypothesis using a new, biologically plausible, algorithm based on matching pursuit, which retains all the features of the previous implementation, including its ability to efficiently encode input. When presented with natural images, the model developed receptive field properties as found in primary visual cortex. In addition, when exposed to visual motion input resulting from movements through space, the model learned receptive field properties resembling those in MST. These results corroborate the idea that predictive feedback is a general principle used by the visual system to efficiently encode natural input.
Algorithms↗
Modeling attention to salient proto-objects.
Selective visual attention is believed to be responsible for serializing visual information for recognizing one object at a time in a complex scene. But how can we attend to objects before they are recognized? In coherence theory of visual cognition, so-called proto-objects form volatile units of visual information that can be accessed by selective attention and subsequently validated as actual objects. We propose a biologically plausible model of forming and attending to proto-objects in natural scenes. We demonstrate that the suggested model can enable a model of object recognition in cortex to expand from recognizing individual objects in isolation to sequentially recognizing all objects in a more complex scene.
Attention↗
Feature-based attention in human visual cortex: simulation of fMRI data.
We use a computational neuroscience approach to study the role of feature-based attention in visual perception. This model is used to numerically simulate a visual attention experiment. The neurodynamical system consists of many interconnected modules that can be related to the dorsal and ventral paths of the visual cortex. The biased competition hypothesis is taken into account within the model. From the experimental point of view, measurements exist, which confirm that feature-based attention influences visual cortical responses to stimuli outside the attended location. These measurements show that attention to a given stimulus attribute (in this case "color red") increases the response of cortical visual areas to a spatially distant, ignored stimulus that shares the same attribute. Our neurodynamical model is used to numerically compute the neural activity of area V4 corresponding to such ignored stimulus, giving a good description of the experimental data.
Attention↗
Synaptic adaptation and odor-background segmentation.
Habituation is a form of non-associative memory that plays an important role in filtering stable or redundant inputs. The present study examines the contribution of habituation and cortical adaptation to odor-background segmentation. Segmentation of target odorants from background odorants is a fundamental computational requirement for the olfactory system. Recent electrophysiological data have shown that odor specific adaptation in piriform cortex neurons, mediated at least partially by synaptic adaptation between the olfactory bulb outputs and piriform cortex pyramidal cells, may provide an ideal mechanism for odor-background segmentation. This rapid synaptic adaptation acts as a filter to enhance cortical responsiveness to changing stimuli, while reducing responsiveness to static, potentially background stimuli. Using previously developed computational models of the olfactory system, we here show how synaptic adaptation at the olfactory bulb input to the piriform cortex, as demonstrated electrophysiologically, creates odor specific adaptation. We show how this known feature of olfactory cortical processing can contribute to adaptation to a background odor and to odor-background segmentation. We then show in a behavioral experiment that the odor-background segmentation is perceptually important and functions at the same time-scale as the synaptic adaptation observed between the olfactory bulb and cortex.
Adaptation, Physiological↗
Evaluation of linear diaphragm-chest expansion models for magnetic resonance imaging motion artifact correction.
The efficacy of Fourier analysis, autoregressive with exogenous input (ARX) and adaptive models to estimate diaphragm position from respiratory belt signal (a measure of chest expansion) was evaluated for the purpose of correcting respiratory motion artifacts in magnetic resonance imaging (MRI). Respiratory belt signal and diaphragm position data were obtained simultaneously during one-dimensional MRI scans with sampling intervals of 100 ms for 128 s (1280 samples). The models were trained using the first 512 data samples for the Fourier method and the first 640 samples for the ARX and adaptive methods. The remaining samples were used as a test set for evaluating the models. Both ARX and adaptive methods produced more accurate results than the Fourier method as reflected by the normalized mean square error (NMSE) and correlation coefficient (R) between the estimated and actual diaphragm position during normal breathing (P < 0.05). However, all three models had difficulty modeling diaphragm positions during breathing plateaus.
Adolescent↗
Sensor fusion by pseudo information measure: a mobile robot application.
In any autonomous mobile robot, one of the most important issues to be designed and implemented is environment perception. In this paper, a new approach is formulated in order to perform sensory data integration for generation of an occupancy grid map of the environment. This method is an extended version of the Bayesian fusion method for independent sources of information. The performance of the proposed method of fusion and its sensitivity are discussed. Map building simulation for a cylindrical robot with eight ultrasonic sensors and mapping implementation for a Khepera robot have been separately tried in simulation and experimental works. A new neural structure is introduced for conversion of proximity data that are given by Khepera IR sensors to occupancy probabilities. Path planning experiments have also been applied to the resulting maps. For each map, two factors are considered and calculated: the fitness and the augmented occupancy of the map with respect to the ideal map. The length and the least distance to obstacles were the other two factors that were calculated for the routes that are resulted by path planning experiments. Experimental and simulation results show that by using the new fusion formulas, more informative maps of the environment are obtained. By these maps more appropriate routes could be achieved. Actually, there is a tradeoff between the length of the resulting routes and their safety and by choosing the proper fusion function, this tradeoff is suitably tuned for different map building applications.
Algorithms↗
Modeling and prediction for discharge lifetime of battery systems using hybrid evolutionary algorithms.
A hybrid evolutionary modeling algorithm (HEMA) is proposed to build the discharge lifetime models with multiple impact factors for battery systems as well as make predictions. The main idea of the HEMA is to embed a genetic algorithm (GA) into genetic programming (GP), where GP is employed to optimize the structure of a model, while a GA is employed to optimize its parameters. The experimental results on lithium-ion batteries show that the HEMA works effectively, automatically and quickly in modeling the discharge lifetime of battery systems. The algorithm has some advantages compared with most existing modeling methods and can be applied widely to solving the automatic modeling problems in many fields.
Algorithms↗
Data differentiation and parameter analysis of a chronic hepatitis B database with an artificial neuromolecular system.
This paper describes the application of a biologically motivated system to the diagnosis of chronic hepatitis B. The system integrates intra- and inter-neuronal information processing so as to capture the biology-like gradual transformability of structure/function relationships. The system was applied to a clinical hepatitis B database, divided into two sets. The first set comprised 676 records, of which one half were chronic hepatitis B patients, and the other half healthy individuals. The second set included 375 records, of which one third were chronic hepatitis B patients; another third were hepatitis B carriers, and the remaining third healthy non-carriers. Each record consisted of ten examination items. Experimental results showed that the system was able to correctly differentiate 99.3 and 91.2% of the records in the first and the second sets, respectively. Differentiation means making a distinction between different categories of data in each set. After substantial learning with the first set, the system was then tested with the second set, and it was able to correctly differentiate 95. 7% of the records, suggesting a high differentiating capability in this system. This system demonstrated an effective self-organizing capability in determining significant and insignificant examination items from patterns of the clinical data. It also showed that some combinations of these items were more effective for determining whether one is infected with chronic hepatitis B than others.
Alanine Transaminase↗
State-related thalamic changes during antipsychotic treatment in schizophrenia: preliminary observations.
Thalamic volumes and psychotic symptoms were assessed during psychotic exacerbation and during antipsychotic drug treatment. Reduction of psychotic symptoms (SAPS) during four weeks of treatment was highly correlated with volumetric expansion as measured by magnetic resonance imaging in both left and right thalamus [r(s)=0.75 and r(s)=0.82, respectively (both P<0.04)].
Adult↗