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At least 253 records · Page 14Linked to original sources
PubMed2006

Parallel network simulations with NEURON.

The NEURON simulation environment has been extended to support parallel network simulations. Each processor integrates the equations for its subnet over an interval equal to the minimum (interprocessor) presynaptic spike generation to postsynaptic spike delivery connection delay. The performance of three published network models with very different spike patterns exhibits superlinear speedup on Beowulf clusters and demonstrates that spike communication overhead is often less than the benefit of an increased fraction of the entire problem fitting into high speed cache. On the EPFL IBM Blue Gene, almost linear speedup was obtained up to 100 processors. Increasing one model from 500 to 40,000 realistic cells exhibited almost linear speedup on 2,000 processors, with an integration time of 9.8 seconds and communication time of 1.3 seconds. The potential for speed-ups of several orders of magnitude makes practical the running of large network simulations that could otherwise not be explored.

Action Potentials↗
PubMed2006

The parameters of the stochastic leaky integrate-and-fire neuronal model.

Five parameters of one of the most common neuronal models, the diffusion leaky integrate-and-fire model, also known as the Ornstein-Uhlenbeck neuronal model, were estimated on the basis of intracellular recording. These parameters can be classified into two categories. Three of them (the membrane time constant, the resting potential and the firing threshold) characterize the neuron itself. The remaining two characterize the neuronal input. The intracellular data were collected during spontaneous firing, which in this case is characterized by a Poisson process of interspike intervals. Two methods for the estimation were applied, the regression method and the maximum-likelihood method. Both methods permit to estimate the input parameters and the membrane time constant in a short time window (a single interspike interval). We found that, at least in our example, the regression method gave more consistent results than the maximum-likelihood method. The estimates of the input parameters show the asymptotical normality, which can be further used for statistical testing, under the condition that the data are collected in different experimental situations. The model neuron, as deduced from the determined parameters, works in a subthreshold regimen. This result was confirmed by both applied methods. The subthreshold regimen for this model is characterized by the Poissonian firing. This is in a complete agreement with the observed interspike interval data.

Action Potentials↗
PubMed1996

Neural net identification of thumb movement using spectral characteristics of magnetic cortical rhythms.

Neural nets have shown great promise as tools for reducing and examining multi-dimensional data. When carefully tuned with selected data sets of individual subjects neural nets have indisputable potential in identifying distinct stages of voluntary finger movements. However, robust, automatized data description methods would be needed to eventually extend the use of neural networks into visualization of brain activity during more complex, multimodal tasks where the cortical processes are not equally well understood. We explored the suitability of a self-organizing map (SOM) in the widely studied case of voluntary finger movements (left and right thumb), using as input such spectral characteristics that showed systematic task-dependent changes when averaged over repeated movements. SOMs constructed without individual fine-tuning and with generally chosen training parameters from these spectral features identified correctly 85% of the ongoing movements but, somewhat surprisingly, not the side of thumb movement. Even for this inclusive choice of input, the neural nets were sensitive to transient signals, but focused fine tuning, based on a priori known subgroups in the data, is clearly required for more detailed classification. Thus, a neural net visualization is likely not the most attractive first approach for characterization of cortical processing during complex multimodal tasks.

Brain Mapping↗
PubMed1996

Information theoretic analysis of action potential trains. II. Analysis of correlation among n neurons to deduce connection structure.

We propose a cross-correlational method based on information theory, which produces a network connection structure to account for observed patterns of action potential activity in multi-unit recordings. Firing probabilities and conditional probabilities are estimated from the action potential trains of n neurons. Two-point mutual information (2pMI) and joint conditional mutual information (JCMI) are calculated by using the estimated probabilities, and then the n-point mutual information (npMI) is calculated. A significant peak of npMI indicates that each neuron is connected to all other neurons at specified time differences, either directly or indirectly. To distinguish between direct and indirect connection, the two-point m-joint conditional mutual information (2pJCMI) is calculated over the peak region for each pair of neurons. A minimum effective connection structure among the n neurons can be deduced in this manner. The procedure for deducing the connection structure for three- and n-neuron networks is described. We apply this method to action potential trains produced by simulated neural networks. Some limitations of the method are also discussed.

Action Potentials↗
PubMed1994

Neural control of feeding.

Substantial progress has been made in identifying the possible signals for initiating and terminating the appetitive aspects of feeding behavior in vertebrates. Strong evidence now implicates ATP (or an ATP-like molecule) and a fall in glucose in initiating feeding. In invertebrates, particular progress has been made in defining the nature and mechanisms of action of the neurotransmitters and peptide co-transmitters that regulate the consummatory aspects of feeding, and a number of new research tools for modelling the operation of simple feeding motor program networks have been developed.

Biogenic Amines↗
PubMed2005

The coarse-to-fine hypothesis revisited: evidence from neuro-computational modeling.

The human perceptual system seems to be driven by a coarse-to-fine integration of visual information. Different results have shown a faster integration of low-spatial frequency compared with high-spatial frequency (HSF) information, starting at early retinal processes. The difference in spatial scale decomposition remains throughout the lateral geniculate nucleus (Hubel & Wiesel, 1977) and V1 (Tootell, Silverman, & De Valois, 1981). During the last decade, a debate has emerged concerning the origin of the coarse-to-fine integration. Is it a constant, perceptually driven integration (Parker et al., 1992 and Parker et al., 1996)? Instead, the flexible use hypothesis suggests that different spatial frequency channels could be enhanced depending on the requirement of the task for high-level cognitive processes like categorization (Oliva and Schyns, 1997 and Schyns and Oliva, 1999). In two connectionist simulations, we have shown that global categorization performance could actually be better performed with HSF information when the amount of information is normalized across the different spatial frequency channels. Those results suggest that high-level requirement alone could not explain the coarse-to-fine bias toward LSF information. A hypothesis is proposed concerning the possible implication of the amount of data provided by different spatial frequency channel that might provide the perceptual bias toward LSF information.

Algorithms↗
PubMed2005

Causal models of attention-deficit/hyperactivity disorder: from common simple deficits to multiple developmental pathways.

Until recently, causal models of attention-deficit/hyperactivity disorder (ADHD) have tended to focus on the role of common, simple, core deficits. One such model highlights the role of executive dysfunction due to deficient inhibitory control resulting from disturbances in the frontodorsal striatal circuit and associated mesocortical dopaminergic branches. An alternative model presents ADHD as resulting from impaired signaling of delayed rewards arising from disturbances in motivational processes, involving frontoventral striatal reward circuits and mesolimbic branches terminating in the ventral striatum, particularly the nucleus accumbens. In the present article, these models are elaborated in two ways. First, they are each placed within their developmental context by consideration of the role of person x environment correlation and interaction and individual adaptation to developmental constraint. Second, their relationship to one another is reviewed in the light of recent data suggesting that delay aversion and executive functions might each make distinctive contributions to the development of the disorder. This provides an impetus for theoretical models built around the idea of multiple neurodevelopmental pathways. The possibility of neuropathologic heterogeneity in ADHD is likely to have important implications for the clinical management of the condition, potentially impacting on both diagnostic strategies and treatment options.

Animals↗
PubMed2006

A dynamic model for action understanding and goal-directed imitation.

The understanding of other individuals' actions is a fundamental cognitive skill for all species living in social groups. Recent neurophysiological evidence suggests that an observer may achieve the understanding by mapping visual information onto his own motor repertoire to reproduce the action effect. However, due to differences in embodiment, environmental constraints or motor skills, this mapping very often cannot be direct. In this paper, we present a dynamic network model which represents in its layers the functionality of neurons in different interconnected brain areas known to be involved in action observation/execution tasks. The model aims at substantiating the idea that action understanding is a continuous process which combines sensory evidence, prior task knowledge and a goal-directed matching of action observation and action execution. The model is tested in variations of an imitation task in which an observer with dissimilar embodiment tries to reproduce the perceived or inferred end-state of a grasping-placing sequence. We also propose and test a biologically plausible learning scheme which allows establishing during practice a goal-directed organization of the distributed network. The modeling results are discussed with respect to recent experimental findings in action observation/execution studies.

Action Potentials↗
PubMed2006

Generalized training subset selection for statistical estimation of epicardial activation maps from intravenous catheter measurements.

Catheter-based electrophysiological studies of the epicardium are limited to regions near the coronary vessels or require transthoracic access. We have developed a statistical approach by which to estimate high-resolution maps of epicardial activation from very low-resolution multi-electrode venous catheter measurements. This technique uses a linear estimation model that derives a relationship between venous catheter measurements and unmeasured epicardial sites from a set of previously recorded, high-resolution epicardial activation-time maps used as a training data set based on the spatial covariance of the measurement sites. We performed 14 dog experiments with various interventions to create an epicardial activation-time map database. This database included a total of 592 epicardial activation maps which were recorded using a sock array placed on the ventricles of dog hearts. We present five approaches, which examined sequential addition and removal of maps to select a generalized training set for the estimation technique. The selection consisted of choosing a subset of epicardial ectopic activation-time maps from the database of beats which resulted in estimation accuracy levels better than or at least similar to using all the maps in database. Our aim was to minimize the redundancy in the database and to be able to guide the eventual procedures required to obtain training data from open-chest surgery patients. The results from this study illustrated this redundancy and suggested that by including an optimal subset (around 100 maps) of the full database the estimation technique was able to perform as well as and even in some cases better than including all the maps in the database. The results also suggest that such an approach is feasible for providing accurate reconstruction of complete epicardial activation-time maps in a clinical setting and with fewer maps we can obtain similar reconstruction accuracy levels.

Animals↗
PubMed2005

Genetics of subthalamic nucleus in development and disease.

The subthalamic nucleus (STN) is a crucial node in the basal ganglia. Clinical success in targeting the STN for deep brain stimulation in Parkinson's disease patients has prompted increased interest in understanding STN biology. In this report, we discuss recent evidence for transcription factor mediated regulation of STN development. We also review STN developmental neurobiology and known patterns of gene expression in the developing and mature STN.

Animals↗
PubMed2004

A method for determining neural connectivity and inferring the underlying network dynamics using extracellular spike recordings.

In the present paper we propose a novel method for the identification and modeling of neural networks using extracellular spike recordings. We create a deterministic model of the effective network, whose dynamic behavior fits experimental data. The network obtained by our method includes explicit mathematical models of each of the spiking neurons and a description of the effective connectivity between them. Such a model allows us to study the properties of the neuron ensemble independently from the original data. It also permits to infer properties of the ensemble that cannot be directly obtained from the observed spike trains. The performance of the method is tested with spike trains artificially generated by a number of different neural networks.

Action Potentials↗
PubMed2003

The development of vestibulo-ocular circuitry in the chicken embryo.

This article reviews studies of the organization and development of the vestibulo-ocular reflex arc in the chicken embryo. It summarizes some of the principal features that characterize the development of this circuit, including the gradual clustering of motoneurons in the oculomotor nucleus into functionally identifiable motoneuron pools, the patterning of vestibular projection neurons into coherent clusters with specific axonal trajectories and terminations onto the oculomotor motoneuron pools, the reverse order of synapse formation during development (motoneuron to muscle, then vestibular projection neuron to motoneuron), and the selectivity of initial synaptic termination at both the ultimate and penultimate relays within the reflex arc. Reference to studies in other vertebrate species is made to provide a comparative context, and potential mechanisms are discussed that may contribute to the underlying synaptic specificity in this circuit.

Animals↗
PubMed2004

Pedophilia: neuropsychological evidence encouraging a brain network perspective.

Although the vast majority of current pathogenetic theories support a neurobiological understanding of psychiatric disorders, the brain functional correlates of pedophilia are largely unknown. Based on prior behavior genetics research on human sexual orientation and phenomenology as well as the phenotypical intersection of pedophilia with other psychiatric spectrum disorders, we hypothesize the involvement of striato-thalamo-cortical processing loops in the formation of pedophilic urges and behaviors. Data from a current neuropsychological pilot study in four pedophiles encourage our brain functional perspective. As deduced from the network model, all four patients exhibited pronounced and circumscribed deficits in cognitive domains mediated by striato-thalamically controlled areas of the frontal cortex. All patients were especially impaired in neuropsychological functions associated with the prefrontal and motor processing loops (e.g., response inhibition, working memory and cognitive flexibility), with a performance level located up to five standard deviations below the normative data. Contrary to this, neuropsychological performances in cognitive domains without a comparable high frontal loading were in all participants unobtrusive. In future, studying gene by environment interactions in combination with functional neuroimaging and neuropsychological assessment is promising to elucidate the pathophysiological relationship of psychiatric disorders that are characterized by inadequate urges and poor behavioral inhibition.

Adult↗
PubMed2004

Global exponential stability of cellular neural networks with time-varying coefficients and delays.

In this paper, a class of cellular neural networks with time-varying coefficients and delays is considered. By constructing a suitable Liapunov functional and utilizing the technique of matrix analysis, some new sufficient conditions on the global exponential stability of solutions are obtained. The results obtained in this paper improve and extend some of the previous results.

Neural Networks, Computer↗
PubMed2004

Criteria for exponential [corrected] stability of Cohen-Grossberg neural networks.

In this paper, the Cohen-Grossberg neural network models without and with time delays are considered. By constructing several novel Lyapunov functionals, some sufficient criteria for the existence of a unique equilibrium and global exponential stability of the network are derived. These results are fairly general and can be easily verified. Besides, the approach of the analysis allows one to consider different types of activation functions, including piecewise linear, sigmoids with bounded activations as well as C1-smooth sigmoids. In the meantime, our approach does not require any symmetric assumption of the connection matrix. It is believed that these results are significant and useful for the design and applications of the Cohen-Grossberg model.

Neural Networks, Computer↗
PubMed2005

Global exponential stability of delayed competitive neural networks with different time scales.

A competitive neural network model was recently proposed to describe the dynamics of cortical maps, where there are two types of memories: long-term and short-term memories. Such a network is characterized by a system of differential equations with two types of variables, one models the fast neural activity and the other models the slow modification of synaptic strength. In this paper, we introduce a time delay parameter into the neural network model to characterize the signal transmission delays in real neural systems and the finite switch speed in the circuit implementations of neural networks. Then, we analyze the global exponential stability of the delayed competitive neural networks with different time scales. We allow the model has non-differentiable and unbounded functions, and use the nonsmooth analysis techniques to prove the existence and uniqueness of the equilibrium, and derive a new sufficient condition ensuring global exponential stability of the networks.

Animals↗
PubMed2005

Elman topology with sigma-pi units: an application to the modeling of verbal hallucinations in schizophrenia.

The development of neural network models has greatly enhanced the comprehension of cognitive phenomena. Here, we show that models using multiplicative processing of inputs are both powerful and simple to train and understand. We believe they are valuable tools for cognitive explorations. Our model can be viewed as a subclass of networks built on sigma-pi units and we show how to derive the Kronecker product representation from the classical sigma-pi unit. We also show how the connectivity requirements of the Kronecker product can be relaxed considering statistical arguments. We use the multiplicative network to implement what we call an Elman topology, that is, a simple recurrent network (SRN) that supports aspects of language processing. As an application, we model the appearance of hallucinated voices after network damage, and show that we can reproduce results previously obtained with SRNs concerning the pathology of schizophrenia.

Brain↗
PubMedDate not supplied

FPGA implementation of self organizing map with digital phase locked loops.

The self-organizing map (SOM) has found applicability in a wide range of application areas. Recently new SOM hardware with phase modulated pulse signal and digital phase-locked loops (DPLLs) has been proposed (Hikawa, 2005). The system uses the DPLL as a computing element since the operation of the DPLL is very similar to that of SOM's computation. The system also uses square waveform phase to hold the value of the each input vector element. This paper discuss the hardware implementation of the DPLL SOM architecture. For effective hardware implementation, some components are redesigned to reduce the circuit size. The proposed SOM architecture is described in VHDL and implemented on field programmable gate array (FPGA). Its feasibility is verified by experiments. Results show that the proposed SOM implemented on the FPGA has a good quantization capability, and its circuit size very small.

Algorithms↗