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At least 127 records · Page 7Linked to original sources
PubMed2000

Simulation of cognitive disturbances by a dynamic threshold semantic neural network.

A neural network model with dynamic thresholds, asymmetric connections, and clustered memories simulates spread activation that is hypothesized for semantic networks in the brain. By altering the parameters of the dynamic threshold a large range of disturbances can be generated in the model. These disturbances show metaphorical resemblance to certain general clinical descriptions of mental disturbances found in psychiatric patients engaged in various cognitive tasks. Even though the model is highly theoretical and metaphoric, it may help to gain certain insights into the relation between alterations of certain neural parameters, for example, thresholds and connectivity, and clinical symptoms in patients.

Algorithms↗
PubMed2002

Predicting and evaluating separation quality of micellar electrokinetic capillary chromatography by artificial neural networks.

Computer-aided optimization of micellar electrokinetic capillary chromatography (MEKC) separations was demonstrated by artificial neural networks (ANNs) using a Levenberg-Marquardt algorithm and an orthogonal experimental design. A novel criterion, named Q, for evaluating the separation quality of MEKC was firstly presented, which considered both separation selectivity and analysis time. MEKC separation conditions of seven plant hormones were then simulated and optimized using ANNs based on this novel criterion. The result was further compared to that obtained using ANNs based on a traditionally used criterion of overall normalization resolution (named r). Finally, the separation under optimum conditions predicted by ANNs using the criterion Q was compared to, and proved to be better than that obtained by empirical step-by-step optimization procedures. This method may also be adapted to other separation methods due to its generality.

Algorithms↗
PubMed2001

A hybrid learning network for shift-invariant recognition.

A neural network and the associated learning algorithm are presented as a generic approach for invariant recognition of visual patterns independent of their geometric attributes, such as spatial location, orientation and scale. The network is a multi-layer hierarchy with each layer composed of a set of groups of nodes. The groups of the input layer represent local areas spatially arranged in the visual field according to the geometric variations. Each node in the subsequent higher layers receives input laterally from other groups of the same layer as well as vertically from the layer below. The learning that takes place in the vertical feed forward paths between layers is based on an unsupervised hybrid algorithm combining both competitive learning and Hebbian learning. As the result of the architecture and the hybrid learning, the desired invariant recognition emerges at the output layer of the network. The network can serve as a simple and biologically plausible computational model to account for the invariant object recognition in the biological visual system. Also, as the algorithm is generic and robust, it can be applied to solve various practical recognition problems.

Algorithms↗
PubMed1976

Instability in a hippocampal neural network.

Four computer models, HM1-HM4, of a particular hippocampal neural network have been developed. The models represent two cell populations, the pyramidal cells and the basket cells; the populations are coupled so that pyramidal cells are inhibited by activity they excite in the basket cell population. In models HM2-HM4, this recurrent inhibitory pathway contains a temporal dispersion element. Models HM3-HM4 place the pyramidal cells in an additional recurrent excitatory feedback loop. HM4 represents a pair of interacting hippocampal networks. Simulated network responses to single-shock stimulation are presented for various parameter values of the four models. Calculations are extended more than one second following stimulation. Particular attention is given to simulation of network instabilities. Simulated neural activity is discussed in view of experimental work on normal (nonepileptogenic) and epileptogenic hippocampal cortex.

Computers↗
PubMed1999

Linear circuits for neural networks and affective computing.

Biological phenomena are often modeled with software on digital computers, even though the events may be analog in nature. The author describes the use of linear circuitry in two areas of biological simulation: artificial neural networks and affective computing. The operational amplifier, with the assistance of some new analog chips and simple digital microcontrollers, is featured prominently in these linear designs.

Computer Simulation↗
PubMed2003

Neural networks for computer-aided diagnosis: detection of lung nodules in chest radiograms.

The paper describes a neural-network-based system for the computer aided detection of lung nodules in chest radiograms. Our approach is based on multiscale processing and artificial neural networks (ANNs). The problem of nodule detection is faced by using a two-stage architecture including: 1) an attention focusing subsystem that processes whole radiographs to locate possible nodular regions ensuring high sensitivity; 2) a validation subsystem that processes regions of interest to evaluate the likelihood of the presence of a nodule, so as to reduce false alarms and increase detection specificity. Biologically inspired filters (both LoG and Gabor kernels) are used to enhance salient image features. ANNs of the feedforward type are employed, which allow an efficient use of a priori knowledge about the shape of nodules, and the background structure. The images from the public JSRT database, including 247 radiograms, were used to build and test the system. We performed a further test by using a second private database with 65 radiograms collected and annotated at the Radiology Department of the University of Florence. Both data sets include nodule and nonnodule radiographs. The use of a public data set along with independent testing with a different image set makes the comparison with other systems easier and allows a deeper understanding of system behavior. Experimental results are described by ROC/FROC analysis. For the JSRT database, we observed that by varying sensitivity from 60 to 75% the number of false alarms per image lies in the range 4-10, while accuracy is in the range 95.7-98.0%. When the second data set was used comparable results were obtained. The observed system performances support the undertaking of system validation in clinical settings.

Algorithms↗
PubMed2005

FROST: a distributed neurocomputational model of working memory maintenance.

Many studies suggest that the sustained activation underlying working memory (WM) maintenance is mediated by a distributed network that includes the prefrontal cortex and other structures (e.g., posterior parietal cortex, thalamus, globus pallidus, and the caudate nucleus). A computational model of WM, called FROST (short for FROntal-Striatal-Thalamic), is proposed in which the representation of items and spatial positions is encoded in the lateral prefrontal cortex. During delay intervals, activation in these prefrontal cells is sustained via parallel, prefrontal cortical-thalamic loops. Activation reverberates in these loops because prefrontal cortical excitation of the head of the caudate nucleus leads to disinhibition of the thalamus (via the globus pallidus). FROST successfully accounts for a wide variety of WM data, including single-cell recording data and human behavioral data.

Action Potentials↗
PubMed2006

Evaluation of the GTRACT diffusion tensor tractography algorithm: a validation and reliability study.

Fiber tracking, based on diffusion tensor imaging (DTI), is the only approach available to non-invasively study the three-dimensional structure of white matter tracts. Two major obstacles to this technique are partial volume artifacts and tracking errors caused by image noise. In this paper, a novel fiber tracking algorithm called Guided Tensor Restore Anatomical Connectivity Tractography (GTRACT) is presented. This algorithm utilizes a multi-pass approach to fiber tracking. In the first pass, a 3D graph search algorithm is utilized. The second pass incorporates anatomical connectivity information generated in the first pass to guide the tracking in this stage. This approach improves the ability to reconstruct complex fiber paths as well as the tracking accuracy. Validation and reliability studies using this algorithm were performed on both synthetic phantom data and clinical human brain data. A method is also proposed for the evaluating reliability of fiber tract generation based both on the position of the fiber tracts, as well the anisotropy values along the path. The results demonstrate that the GTRACT algorithm is less sensitive to image noise and more capable of handling areas of complex fiber crossing, compared to conventional streamline methods.

Algorithms↗
PubMed2004

Stimulus-specific oscillations in a retinal model.

High-frequency oscillatory potentials (HFOPs) in the vertebrate retina are stimulus specific. The phases of HFOPs recorded at any given retinal location drift randomly over time, but regions activated by the same stimulus tend to remain phase locked with approximately zero lag, whereas regions activated by spatially separate stimuli are typically uncorrelated. Based on retinal anatomy, we previously postulated that HFOPs are mediated by feedback from a class of axon-bearing amacrine cells that receive excitation from neighboring ganglion cells-via gap junctions-and make inhibitory synapses back onto the surrounding ganglion cells. Using a computer model, we show here that such circuitry can account for the stimulus specificity of HFOPs in response to both high- and low-contrast features. Phase locking between pairs of model ganglion cells did not depend critically on their separation distance, but on whether the applied stimulus created a continuous path between them. The degree of phase locking between spatially separate stimuli was reduced by lateral inhibition, which created a buffer zone around strongly activated regions. Stimulating the inhibited region between spatially separate stimuli increased their degree of phase locking proportionately. Our results suggest several experimental strategies for testing the hypothesis that stimulus-specific HFOPs arise from axon-mediated feedback in the inner retina.

Action Potentials↗
PubMed2004

Derivation and analysis of basic computational operations of thalamocortical circuits.

Shared anatomical and physiological features of primary, secondary, tertiary, polysensory, and associational neocortical areas are used to formulate a novel extended hypothesis of thalamocortical circuit operation. A simplified anatomically based model of topographically and nontopographically projecting ("core" and "matrix") thalamic nuclei, and their differential connections with superficial, middle, and deep neocortical laminae, is described. Synapses in the model are activated and potentiated according to physiologically based rules. Features incorporated into the models include differential time courses of excitatory versus inhibitory postsynaptic potentials, differential axonal arborization of pyramidal cells versus interneurons, and different laminar afferent and projection patterns. Observation of the model's responses to static and time-varying inputs indicates that topographic "core" circuits operate to organize stored memories into natural similarity-based hierarchies, whereas diffuse "matrix" circuits give rise to efficient storage of time-varying input into retrievable sequence chains. Examination of these operations shows their relationships with well-studied algorithms for related functions, including categorization via hierarchical clustering, and sequential storage via hash- or scatter-storage. Analysis demonstrates that the derived thalamocortical algorithms exhibit desirable efficiency, scaling, and space and time cost characteristics. Implications of the hypotheses for central issues of perceptual reaction times and memory capacity are discussed. It is conjectured that the derived functions are fundamental building blocks recurrent throughout the neocortex, which, through combination, gives rise to powerful perceptual, motor, and cognitive mechanisms.

Algorithms↗
PubMed2004

Including long-range dependence in integrate-and-fire models of the high interspike-interval variability of cortical neurons.

Many different types of integrate-and-fire models have been designed in order to explain how it is possible for a cortical neuron to integrate over many independent inputs while still producing highly variable spike trains. Within this context, the variability of spike trains has been almost exclusively measured using the coefficient of variation of interspike intervals. However, another important statistical property that has been found in cortical spike trains and is closely associated with their high firing variability is long-range dependence. We investigate the conditions, if any, under which such models produce output spike trains with both interspike-interval variability and long-range dependence similar to those that have previously been measured from actual cortical neurons. We first show analytically that a large class of high-variability integrate-and-fire models is incapable of producing such outputs based on the fact that their output spike trains are always mathematically equivalent to renewal processes. This class of models subsumes a majority of previously published models, including those that use excitation-inhibition balance, correlated inputs, partial reset, or nonlinear leakage to produce outputs with high variability. Next, we study integrate-and-fire models that have (nonPoissonian) renewal point process inputs instead of the Poisson point process inputs used in the preceding class of models. The confluence of our analytical and simulation results implies that the renewal-input model is capable of producing high variability and long-range dependence comparable to that seen in spike trains recorded from cortical neurons, but only if the interspike intervals of the inputs have infinite variance, a physiologically unrealistic condition. Finally, we suggest a new integrate-and-fire model that does not suffer any of the previously mentioned shortcomings. By analyzing simulation results for this model, we show that it is capable of producing output spike trains with interspike-interval variability and long-range dependence that match empirical data from cortical spike trains. This model is similar to the other models in this study, except that its inputs are fractional-gaussian-noise-driven Poisson processes rather than renewal point processes. In addition to this model's success in producing realistic output spike trains, its inputs have long-range dependence similar to that found in most subcortical neurons in sensory pathways, including the inputs to cortex. Analysis of output spike trains from simulations of this model also shows that a tight balance between the amounts of excitation and inhibition at the inputs to cortical neurons is not necessary for high interspike-interval variability at their outputs. Furthermore, in our analysis of this model, we show that the superposition of many fractional-gaussian-noise-driven Poisson processes does not approximate a Poisson process, which challenges the common assumption that the total effect of a large number of inputs on a neuron is well represented by a Poisson process.

Action Potentials↗
PubMed2006

On the computational power of threshold circuits with sparse activity.

Circuits composed of threshold gates (McCulloch-Pitts neurons, or perceptrons) are simplified models of neural circuits with the advantage that they are theoretically more tractable than their biological counterparts. However, when such threshold circuits are designed to perform a specific computational task, they usually differ in one important respect from computations in the brain: they require very high activity. On average every second threshold gate fires (sets a 1 as output) during a computation. By contrast, the activity of neurons in the brain is much sparser, with only about 1% of neurons firing. This mismatch between threshold and neuronal circuits is due to the particular complexity measures (circuit size and circuit depth) that have been minimized in previous threshold circuit constructions. In this letter, we investigate a new complexity measure for threshold circuits, energy complexity, whose minimization yields computations with sparse activity. We prove that all computations by threshold circuits of polynomial size with entropy O(log n) can be restructured so that their energy complexity is reduced to a level near the entropy of circuit states. This entropy of circuit states is a novel circuit complexity measure, which is of interest not only in the context of threshold circuits but for circuit complexity in general. As an example of how this measure can be applied, we show that any polynomial size threshold circuit with entropy O(log n) can be simulated by a polynomial size threshold circuit of depth 3. Our results demonstrate that the structure of circuits that result from a minimization of their energy complexity is quite different from the structure that results from a minimization of previously considered complexity measures, and potentially closer to the structure of neural circuits in the nervous system. In particular, different pathways are activated in these circuits for different classes of inputs. This letter shows that such circuits with sparse activity have a surprisingly large computational power.

Brain↗
PubMed1991

Possible dual effect of synapses that are putatively purely excitatory or purely inhibitory: bases in stability theory and implications for neural network behavior.

Depolarization of an excitable membrane has a dual effect; excitatory in that it causes rapid opening of calcium and/or sodium channels but inhibitory in that it also causes those channels to inactivate. We considered whether apparently paradoxical or dual behavior might be exhibited by excitatory and inhibitory synaptic inputs. We used the classic Hodgkin-Huxley model for voltage-gated channels plus leakage channels of appropriate selectivity for ligand-gated postsynaptic channels. We summarize a model cell's behavior by calculating elicited firing frequency as a function of reversal potential and conductance of summed synaptic inputs, using stability theory and direct simulations. Dual behavior is elicited in the model with reasonable densities of ligand-gated channels. Thus a particular synaptic input to a neuron may be either excitatory or inhibitory depending on simultaneous activity of other synaptic inputs to the cell. This input-output map may give rise to biologically realistic and rich behaviors as an element of computed neural networks, and still be computationally tractable.

Animals↗
PubMed2002

Rearrangement of receptive field topography after intracortical and peripheral stimulation: the role of plasticity in inhibitory pathways.

Intracortical microstimulation (ICMS) of a single site in the somatosensory cortex of rats and monkeys for 2-6 h increases the number of neurons responsive to the skin region corresponding to the ICMS-site receptive field (RF), with very little effect on the position and size of the ICMS-site RF, and the response evoked at the ICMS site by tactile stimulation. Large changes in RF topography are also observed following several weeks of repetitive stimulation of a restricted skin region during tactile frequency discrimination training in monkeys. It has been suggested that these changes in RF topography are caused by competitive learning in excitatory pathways. This paper analyses the possible role of lateral inhibitory synaptic plasticity in producing cortical plasticity after ICMS and peripheral conditioning in adult animals. The 'EXIN' (afferent excitatory and lateral inhibitory) synaptic plasticity rules are used to model RF changes after ICMS and peripheral stimulation. The EXIN model produces RF topographical changes similar to those observed experimentally. It is shown that lateral inhibitory pathway plasticity is sufficient to model RF changes and increase in position discrimination after peripheral stimulation. Several novel and testable predictions are made based on the EXIN model.

Algorithms↗
PubMed2004

Fair attribution of functional contribution in artificial and biological networks.

This letter presents the multi-perturbation Shapley value analysis (MSA), an axiomatic, scalable, and rigorous method for deducing causal function localization from multiple perturbations data. The MSA, based on fundamental concepts from game theory, accurately quantifies the contributions of network elements and their interactions, overcoming several shortcomings of previous function localization approaches. Its successful operation is demonstrated in both the analysis of a neurophysiological model and of reversible deactivation data. The MSA has a wide range of potential applications, including the analysis of reversible deactivation experiments, neuronal laser ablations, and transcranial magnetic stimulation "virtual lesions," as well as in providing insight into the inner workings of computational models of neurophysiological systems.

Algorithms↗