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PubMed2006

On the analysis and interpretation of inhomogeneous quadratic forms as receptive fields.

In this letter, we introduce some mathematical and numerical tools to analyze and interpret inhomogeneous quadratic forms. The resulting characterization is in some aspects similar to that given by experimental studies of cortical cells, making it particularly suitable for application to second-order approximations and theoretical models of physiological receptive fields. We first discuss two ways of analyzing a quadratic form by visualizing the coefficients of its quadratic and linear term directly and by considering the eigenvectors of its quadratic term. We then present an algorithm to compute the optimal excitatory and inhibitory stimuli--those that maximize and minimize the considered quadratic form, respectively, given a fixed energy constraint. The analysis of the optimal stimuli is completed by considering their invariances, which are the transformations to which the quadratic form is most insensitive, and by introducing a test to determine which of these are statistically significant. Next we propose a way to measure the relative contribution of the quadratic and linear term to the total output of the quadratic form. Furthermore, we derive simpler versions of the above techniques in the special case of a quadratic form without linear term. In the final part of the letter, we show that for each quadratic form, it is possible to build an equivalent two-layer neural network, which is compatible with (but more general than) related networks used in some recent articles and with the energy model of complex cells. We show that the neural network is unique only up to an arbitrary orthogonal transformation of the excitatory and inhibitory subunits in the first layer.

Action Potentials↗
PubMed2005

Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control.

A broad range of neural and behavioral data suggests that the brain contains multiple systems for behavioral choice, including one associated with prefrontal cortex and another with dorsolateral striatum. However, such a surfeit of control raises an additional choice problem: how to arbitrate between the systems when they disagree. Here, we consider dual-action choice systems from a normative perspective, using the computational theory of reinforcement learning. We identify a key trade-off pitting computational simplicity against the flexible and statistically efficient use of experience. The trade-off is realized in a competition between the dorsolateral striatal and prefrontal systems. We suggest a Bayesian principle of arbitration between them according to uncertainty, so each controller is deployed when it should be most accurate. This provides a unifying account of a wealth of experimental evidence about the factors favoring dominance by either system.

Animals↗
PubMedDate not supplied

Development of QSAR models to predict and interpret the biological activity of artemisinin analogues.

This work presents the development of Quantitative Structure-Activity Relationship (QSAR) models to predict the biological activity of 179 artemisinin analogues. The structures of the molecules are represented by chemical descriptors that encode topological, geometric, and electronic structure features. Both linear (multiple linear regression) and nonlinear (computational neural network) models are developed to link the structures to their reported biological activity. The best linear model was subjected to a PLS analysis to provide model interpretability. While the best linear model does not perform as well as the nonlinear model in terms of predictive ability, the application of PLS analysis allows for a sound physical interpretation of the structure-activity trend captured by the model. On the other hand, the best nonlinear model is superior in terms of pure predictive ability, having a training error of 0.47 log RA units (R2 = 0.96) and a prediction error of 0.76 log RA units (R2 = 0.88).

Antimalarials↗
PubMed2004

Temporal coding in a silicon network of integrate-and-fire neurons.

Spatio-temporal processing of spike trains by neuronal networks depends on a variety of mechanisms distributed across synapses, dendrites, and somata. In natural systems, the spike trains and the processing mechanisms cohere though their common physical instantiation. This coherence is lost when the natural system is encoded for simulation on a general purpose computer. By contrast, analog VLSI circuits are, like neurons, inherently related by their real-time physics, and so, could provide a useful substrate for exploring neuronlike event-based processing. Here, we describe a hybrid analog-digital VLSI chip comprising a set of integrate-and-fire neurons and short-term dynamical synapses that can be configured into simple network architectures with some properties of neocortical neuronal circuits. We show that, despite considerable fabrication variance in the properties of individual neurons, the chip offers a viable substrate for exploring real-time spike-based processing in networks of neurons.

Action Potentials↗
PubMed1996

Distributed processing on the basis of parallel and antagonistic pathways simulation of the femur-tibia control system in the stick insect.

In inactive stick insects, sensory information from the femoral chordotonal organ (fCO) about position and movement of the femur-tibia joint is transferred via local nonspiking interneurons onto extensor and flexor tibiae motoneurons. Information is processed by the interaction of antagonistic parallel pathways at two levels: (1) at the input side of the nonspiking interneurons and (2) at the input side of the motoneurons. We tested by a combination of physiological experiments and computer simulation whether the known network topology and the properties of its elements are sufficient to explain the generation of the motor output in response to passive joint movements, that is resistance reflexes. In reinvestigating the quantitative characteristics of interneuronal pathways we identified 10 distinct types of nonspiking interneurons. Synaptic inputs from fCO afferents onto these interneurons are direct excitatory and indirect inhibitory. These connections were investigated with respect to position and velocity signals from the fCO. The results were introduced in the network simulation. The motor output of the simulation has the same characteristics as the real system, even when particular types of interneurons were removed in the simulation and the real system.

Animals↗
PubMed2006

Pharmacological and computational analysis of alpha-subunit preferential GABA(A) positive allosteric modulators on the rat septo-hippocampal activity.

Clinically most active anxiolytic drugs are positive allosteric modulators (PAMs) of GABA(A) receptors, represented by benzodiazepine compounds. Due to their non-selective profile, however, they potently modulate several sup-type specific GABA(A) receptors, contributing to their broad-range side effects. Based on observations in genetically altered mice, however, it has been proposed that anxiolytic action of benzodiazepines is predominantly mediated by GABA(A) alpha2/3 subunit-containing receptors. In the present study we analyzed the actions of the preferential GABA(A) alpha1 and alpha2/3 PAMs, zolpidem and L-838417, respectively on hippocampal EEG and medial septum neuronal activity in anesthetized rats. In parallel, a computational model was constructed to model pharmacological actions of these compounds on the septo-hippocampal circuitry. The present results demonstrated that zolpidem inhibited theta oscillation both in the hippocampus and septum, and profoundly inhibited firing activity of septal neurons. L-838417 also inhibited hippocampal and septal theta oscillation, however, it did not significantly alter firing rate activity of septal neurons. Our computational model showed that cessation of periodic firing of hippocampo-septal neurons, representing absence of hippocampal theta activity, disrupted oscillation of septal units, without altering their overall firing activity, similar to changes observed in our in vivo experiments following administration of L-838417. Understanding the correlation between changes in septo-hippocampal activity and actions of selective modulators of GABA(A) subtype receptor modulators would further advance design of anxiolytic drugs.

Action Potentials↗
PubMed2002

Population computation of vectorial transformations.

Many neurons of the central nervous system are broadly tuned to some sensory or motor variables. This property allows one to assign to each neuron a preferred attribute (PA). The width of tuning curves and the distribution of PAs in a population of neurons tuned to a given variable define the collective behavior of the population. In this article, we study the relationship of the nature of the tuning curves, the distribution of PAs, and computational properties of linear neuronal populations. We show that noise-resistant distributed linear algebraic processing and learning can be implemented by a population of cosine tuned neurons assuming a nonuniform but regular distribution of PAs. We extend these results analytically to the noncosine tuning and uniform distribution case and show with a numerical simulation that the results remain valid for a nonuniform regular distribution of PAs for broad noncosine tuning curves. These observations provide a theoretical basis for modeling general nonlinear sensorimotor transformations as sets of local linearized representations.

Algorithms↗
PubMed2004

Diversity beyond variance: modulation of firing rates and network coherence by GABAergic subpopulations.

Computational modelling studies have revealed that heterogeneity in interneuronal networks can powerfully modulate firing rates, responses to excitatory inputs, theta-gamma oscillations and network synchrony. In these previous studies, heterogeneity was represented by the degree of variance in various interneuronal synaptic and cellular parameters. However, a major characteristic of gamma-amino-butyric-acid-ergic (GABAergic) synaptic and interneuronal populations is the presence of distinct subgroups, which variance-based approaches cannot fully address. Here we apply an information theory-based measure of diversity, the Shannon-Wiener diversity index (equivalent to entropy), which takes into account both the number and relative abundance of categories within a population. Computational modelling data and experimental dynamic clamp results show that increasing the diversity index in the somatically injected inhibitory post synaptic currents (IPSC) peak conductances modulates the firing rates of CA1 pyramidal cells in a predictable manner that depends on both the mean and variance of the IPSC conductance. Furthermore, increases in the diversity index in interneuronal populations strongly decreased network coherence, even when population variance, the previously applied measure of heterogeneity, remained unchanged. These modelling and experimental results reveal a new approach to the study of interneuronal heterogeneity, and demonstrate the modulation of principal cell firing rates and interneuronal network coherence by parameter clustering at both the synaptic and cellular levels.

Action Potentials↗
PubMed2006

Adjustable primitive pattern generator: a novel cerebellar model for reaching movements.

Cerebellum has been assumed as an array of adjustable pattern generators (APGs). In recent years, electrophysiological researches have suggested the existence of modular structures in spinal cord called motor primitives. In our proposed model, each "adjustable primitive pattern generator" (APPG) module in the cerebellum is consisted of a large number of parallel APGs, the output of each module being the weighted sum of the outputs of these APGs. Each spinal field is tuned by a coefficient, representing a descending supraspinal command, which is modulated by ith APPG correspondingly. According to this model, motor control can be interpreted in terms of the modification of these coefficients. Vector summation of force fields implies that the complex nonlinearities in neuronal behavior are eliminated, causing our model to be simple and linear. The force field vectors, derived from motor primitives, depend on the state of movement and its derivative and the time that causes different repertoire of movement. This is physiologically plausible. Our model agrees with virtual trajectory hypothesis, stating that dynamics are not computed explicitly in central nervous system, but the desired trajectory, is fed into the spinal cord. We think that the dysmetria and the ataxia seen in some cerebellar diseases may be the result of local disruption of some APPGs. Accordingly, determining the exact location of related motor primitives in human spinal cord and stimulating them by functional neurostimulation may provide a good management for these clinical signs. Surely, experimental researches and clinical trials are needed to validate our hypothesis.

Animals↗
PubMed2005

Extraclassical receptive field phenomena and short-range connectivity in V1.

Extraclassical receptive field phenomena in V1 are commonly attributed to long-range lateral connections and/or extrastriate feedback. We address 2 such phenomena: surround suppression and receptive field expansion at low contrast. We present rigorous computational support for the hypothesis that the phenomena largely result from local short-range (< 0.5 mm) cortical connections and lateral geniculate nucleus input. The neural mechanisms of surround suppression in our simulations operate via (A) enhancement of inhibition, (B) reduction of excitation, or (C) action of both simultaneously. Mechanisms (B) and (C) are substantially more prevalent than (A). We observe, on average, a growth in the spatial summation extent of excitatory and inhibitory synaptic inputs for low-contrast stimuli. However, we find this is neither sufficient nor necessary to explain receptive field expansion at low contrast, which usually involves additional changes in the relative gain of these inputs.

Algorithms↗
PubMedDate not supplied

Reentry and the problem of integrating multiple cortical areas: simulation of dynamic integration in the visual system.

Studies of the cerebral cortex, particularly those of the visual system, demonstrate the existence of multiple, anatomically segregated and functionally specialized cortical areas. There is no evidence that these areas, which are linked by a network of reciprocal connections, are coordinated by a higher-order center. The visual image that we perceive, however, seems to be unified and coherent. In this article, we address the problem of integration posed by these observations. In an extension of our previous work, we develop a dynamic model of reentry. Reentry is a process of parallel and recursive signaling along ordered anatomical connections that achieves integration by giving rise to constructive and correlative properties within and among maps. We present and test a computer model simulating nine functionally segregated visual areas organized into three streams for form, color, and motion. The model receives visual input consisting of camera images of objects of different shapes and colors. We show the specialized response properties of the areas in the three streams. A computational strategy involving a phase variable is introduced to represent explicitly the dynamics of short-term temporal correlations among thousands of units distributed across different areas. We then illustrate constructive and correlative consequences of reentry within a system of reciprocal intra- and interareal connections by two examples taken from psychophysics: generation of form from motion and motion capture. The model solves the so-called "binding problem" through short-term correlations, which serve to link similar object features within a simulated cortical area and to bind multiple attributes of one or more objects across several areas, including a nontopographic one. Integration emerges from cooperative effects within and among the specialized areas. These effects lead to a simple output, a simulated foveation response, that is used as a basis for conditioning. Reward is mediated by the activation of a saliency system that is modeled on diffuse projection systems in the brain. As a result, the visual cortical model carries out foveation responses to input stimuli that require the dynamic conjunction and discrimination of form, color, and location for successful performance.

Animals↗
PubMed2006

Phase resetting and coupling of noisy neural oscillators.

A number of experimental groups have recently computed Phase Response Curves (PRCs) for neurons. There is a great deal of noise in the data. We apply methods from stochastic nonlinear dynamics to coupled noisy phase-resetting maps and obtain the invariant density of phase distributions. By exploiting the special structure of PRCs, we obtain some approximations for the invariant distributions. Comparisons to Monte-Carlo simulations are made. We show how phase-dependence of the noise can move the peak of the invariant density away from the peak expected from the analysis of the deterministic system and thus lead to noise-induced bifurcations.

Action Potentials↗
PubMedDate not supplied

A laterally interconnected neural architecture in MST accounts for psychophysical discrimination of complex motion patterns.

The complex patterns of visual motion formed across the retina during self-motion, often referred to as optic flow, provide a rich source of information describing our dynamic relationship within the environment. Psychophysical studies indicate the existence of specialized detectors for component motion patterns (radial, circular, planar) that are consistent with the visual motion properties of cells in the medial superior temporal area (MST) of nonhuman primates. Here we use computational modeling and psychophysics to investigate the structural and functional role of these specialized detectors in performing a graded motion pattern (GMP) discrimination task. In the psychophysical task perceptual discrimination varied significantly with the type of motion pattern presented, suggesting perceptual correlates to the preferred motion bias reported in MST. Simulated perceptual discrimination in a population of independent MST-like neural responses showed inconsistent psychophysical performance that varied as a function of the visual motion properties within the population code. Robust psychophysical performance was achieved by fully interconnecting neural populations such that they inhibited nonpreferred units. Taken together, these results suggest that robust processing of the complex motion patterns associated with self-motion and optic flow may be mediated by an inhibitory structure of neural interactions in MST.

Animals↗
PubMed2006

A computational model of how an interaction between the thalamocortical and thalamic reticular neurons transforms the low-frequency oscillations of the globus pallidus.

In Parkinson's disease, neurons of the internal segment of the globus pallidus (GPi) display the low-frequency tremor-related oscillations. These oscillatory activities are transmitted to the thalamic relay nuclei. Computer models of the interacting thalamocortical (TC) and thalamic reticular (RE) neurons were used to explore how the TC-RE network processes the low-frequency oscillations of the GPi neurons. The simulation results show that, by an interaction between the TC and RE neurons, the TC-RE network transforms a low-frequency oscillatory activity of the GPi neurons to a higher frequency of oscillatory activity of the TC neurons (the superharmonic frequency transformation). In addition to the interaction between the TC and RE neurons, the low-threshold calcium current in the RE and TC neurons and the hyperpolarization-activated cation current (I (h)) in the TC neurons have significant roles in the superharmonic frequency transformation property of the TC-RE network. The external globus pallidus (GPe) oscillatory activity, which is directly transmitted to the RE nucleus also displays a significant modulatory effect on the superharmonic frequency transformation property of the TC-RE network.

Action Potentials↗
PubMed1992

Application of artificial neural network to computer-aided diagnosis of coronary artery disease in myocardial SPECT bull's-eye images.

We have developed a computerized system that can aid in the radiologist's diagnosis in the detection and classification of coronary artery diseases. The technique employs a neural network to analyze 201Tl myocardial SPECT bull's-eye images. This multi-layer feed-forward neural network with a backpropagation algorithm has 256 input units (pattern: compressed 16 x 16-matrix images), 5-140 units in a single hidden layer, and eight output units (diagnosis: one normal and seven different types of abnormalities). The neural network was taught using pairs of training (learning) input data (bull's-eye "EXTENT" image) and desired output data ("correct" diagnosis). The effects of the numbers of hidden units and learning iterations in the network on the recognition performance were examined. In our initial stage, the results show that the recognition performance of the neural network is better than that of the radiology resident but worse than that of the experienced radiologist. Our study also demonstrates that the result produced in the neural network depends on the variety of the training examples used. The preliminary study suggests that the neural network approach is useful for the computer-aided diagnosis of coronary artery diseases in myocardial SPECT bull's-eye images.

Coronary Disease↗
PubMed2006

Feedforward inhibition controls the spread of granule cell-induced Purkinje cell activity in the cerebellar cortex.

Synapses associated with the parallel fiber (pf) axons of cerebellar granule cells constitute the largest excitatory input onto Purkinje cells (PCs). Although most theories of cerebellar function assume these synapses produce an excitatory sequential "beamlike" activation of PCs, numerous physiological studies have failed to find such beams. Using a computer model of the cerebellar cortex we predicted that the lack of PCs beams is explained by the concomitant pf activation of feedforward molecular layer inhibition. This prediction was tested, in vivo, by recording PCs sharing a common set of pfs before and after pharmacologically blocking inhibitory inputs. As predicted by the model, pf-induced beams of excitatory PC responses were seen only when inhibition was blocked. Blocking inhibition did not have a significant effect in the excitability of the cerebellar cortex. We conclude that pfs work in concert with feedforward cortical inhibition to regulate the excitability of the PC dendrite without directly influencing PC spiking output. This conclusion requires a significant reassessment of classical interpretations of the functional organization of the cerebellar cortex.

Action Potentials↗
PubMed1994

Context, conditioning, and hippocampal rerepresentation in animal learning.

The researchers argue that a previous computational account of hippocampal region function in associative learning (M. Gluck & C. Myers, 1993) has emergent implications that accurately describe the role of the hippocampal region in contextual processing. This article unifies 2 seemingly conflicting views of contextual processing: It accords contextual cues no special representational status (e.g., R. Rescorla & A. Wagner, 1972), yet it also allows context to stand in a superordinate relationship to the cues it contains (e.g., L. Nadel & J. Willner, 1980). As a result, the account correctly expects that context can develop occasion-setting properties and that context shifts can weaken learned responses or attenuate latent inhibition. The article also explains data suggesting that hippocampal lesions reduce contextual sensitivity. It may help unify several previous theoretical accounts of the hippocampal region's role in contextual processing.

Animals↗
PubMed2005

What is the function of hippocampal theta rhythm?--Linking behavioral data to phasic properties of field potential and unit recording data.

The extensive physiological data on hippocampal theta rhythm provide an opportunity to evaluate hypotheses about the role of theta rhythm for hippocampal network function. Computational models based on these hypotheses help to link behavioral data with physiological measurements of different variables during theta rhythm. This paper reviews work on network models in which theta rhythm contributes to the following functions: (1) separating the dynamics of encoding and retrieval, (2) enhancing the context-dependent retrieval of sequences, (3) buffering of novel information in entorhinal cortex (EC) for episodic encoding, and (4) timing interactions between prefrontal cortex and hippocampus for memory-guided action selection. Modeling shows how these functional mechanisms are related to physiological data from the hippocampal formation, including (1) the phase relationships of synaptic currents during theta rhythm measured by current source density analysis of electroencephalographic data from region CA1 and dentate gyrus, (2) the timing of action potentials, including the theta phase precession of single place cells during running on a linear track, the context-dependent changes in theta phase precession across trials on each day, and the context-dependent firing properties of hippocampal neurons in spatial alternation (e.g., "splitter cells"), (3) the cholinergic regulation of sustained activity in entorhinal cortical neurons, and (4) the phasic timing of prefrontal cortical neurons relative to hippocampal theta rhythm.

Action Potentials↗