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PubMed1994

Decoding cortical neuronal signals: network models, information estimation and spatial tuning.

We have studied the encoding of spatial pattern information by complex cells in the primary visual cortex of awake monkeys. Three models for the conditional probabilities of different stimuli, given the neuronal response, were fit and compared using cross-validation. For our data, a feed-forward neural network proved to be the best of these models. The information carried by a cell about a stimulus set can be calculated from the estimated conditional probabilities. We performed a spatial spectroscopy of the encoding, examining how the transmitted information varies with both the average coarseness of the stimulus set and the coarseness differences within it. We find that each neuron encodes information about many features at multiple scales. Our data do not appear to allow a characterization of these variations in terms of the detection of simple single features such as oriented bars.

Animals↗
PubMed1988

A model of autocatalytic replication.

The catalytic effects that existing polymer chains have on the formation of new chains are modeled using ideas from spin glasses and neural networks. Computer simulation shows that isolated groups of chains in this model are capable of accurately replicating a wide variety of complex structures without templating. Replication in the model arises spontaneously and rapidly, leading to an extremely simple realization of a system exhibiting Darwinian evolution.

Animals↗
PubMed1998

Isoleucine uptake in Corynebacterium glutamicum ATCC 13032 is directed by the brnQ gene product.

By complementation analysis of an isoleucine-uptake-deficient Escherichia coli strain, it was shown that a 1.6-kb HindIII-StuI fragment of Corynebacterium glutamicum ATCC 13032, located downstream of the aecD gene, encodes an isoleucine uptake system. Sequence analysis revealed that the complementing fragment carried an open reading frame, termed brnQ, that encodes a protein with sequence similarities to branched-chain amino acid carriers of gram-positive and gram-negative bacteria. The brnQ gene specifies a predominantly hydrophobic protein of 426 amino acid residues with a calculated molecular mass of 44.9 kDa. A topology prediction by neural network computer analysis suggests the existence of 12 hydrophobic segments that most probably form transmembrane alpha-helices. A C. glutamicum mutant strain harboring a defined deletion of brnQ in the chromosome showed a considerably lower isoleucine uptake rate of 0.04 nmol min-1 mg (dry mass)-1 as compared to the wild-type strain rate of 1.2 nmol min-1 mg (dry mass)-1. Overexpression of brnQ by means of a tac promotor resulted in an elevated uptake rate for isoleucine of 11.3 nmol min-1 mg (dry mass)-1. Evidently, the brnQ gene encodes the only transport system in C. glutamicum directing isoleucine uptake.

Amino Acid Sequence↗
PubMed2002

Estimation of premotor synaptic drives to simulated abducens motoneurons for control of eye position.

The firing rate of an abducens motoneuron (AbMN) is linearly related to eye position with slope K, above recruitment threshold theta. Within the AbMN population K increases as theta increases. It is possible that these properties depend on the synaptic drives generated by the major premotor inputs to AbMNs, namely position-vestibular-pause (PVP) cells and eye and head velocity (EHV) cells in the medial vestibular nucleus, and eye-position and burst-position cells in the nucleus prepositus hypoglossi (NPH). Premotor inputs to AbMNs were therefore modelled by a two-layer net, in which the output nodes represented the AbMNs (with fixed intrinsic properties) and the input nodes the three classes of premotor units ( n=20/class). Conjugate eye-position commands were used to generate the firing rates in premotor units found experimentally. The output of the net was compared with observed AbMN firing rates, and the resultant error used to adjust the magnitude and sign of the connection weights between premotor units and AbMNs. To provide additional constraints on permitted weights, the net was also trained under simulated smooth pursuit, cancellation of the vestibular-ocular reflex, and the vestibulo-ocular reflex itself (all at 0.5 Hz). Since the projections of EHV cells have not been clearly characterized, two versions of the model were trained, corresponding to different assumptions about these projections. In both versions, position-related AbMN firing rates were derived mainly from an excitatory drive from PVP cells and an inhibitory drive from NPH cells with the opposite ON direction. Variation in AbMN threshold theta and position sensitivity K depended on the strength of the drive from the NPH: the stronger the drive, the higher both K and theta. This arrangement was observed in six variants of the basic model with different parameter values, and in a simplified form (constant PVP drive and varying NPH drive) was able to generate qualitatively the observed relationship between K and theta even in the absence of input from EHV cells. It appears to be a robust mechanism for producing the experimentally observed variation in position-related firing of AbMNs, even without a contribution from their intrinsic properties, and predicts that local blocking of the inhibitory drive from cells in the NPH should lower both the position threshold and sensitivity of an individual AbMN. The model also indicates that if EHV cells have ipsilateral inhibitory projections, as has been proposed on the basis of their similarity with cells receiving input from the flocculus, then their role in eye-position control would reinforce that of cells in the NPH.

Abducens Nerve↗
PubMed2003

Neuronal network modelling of the effects of anaesthetic agents on somatosensory pathways.

The whole question of consciousness, awareness and depth of anaesthesia is both timely, little understood and deeply challenging. Models of the underlying neural pathway mechanisms/dynamics are necessary for understanding the interactions involved and their structure and function. A neuronal network of the somatosensory pathways is proposed in this paper based on experimental information and physiological investigation into anaesthesia. Existing mathematical neuronal models from the literature have been modified and then employed to describe the dynamics of the proposed pathway network. Effects of anaesthetic agents on the cortex were simulated in the model which describes the evoked cortical responses. By comparison with responses from anaesthetised rats, the model's responses are able to describe the dynamics of typical responses. Thus, the proposed model promises to be valuable for investigating the mechanisms of anaesthesia on the cortex and the effects of brain lesions.

Anesthetics↗
PubMed2003

Roles of dynamic linkage of stable attractors across cortical networks in recalling long-term memory.

We propose a neural network model for a category-association task. By simulating the model, neuronal relevance of cortical interactions to recalling long-term memory was investigated. The model consists of the left and right hemispheres, each of which has IT (inferotemporal cortex) and PC (prefrontal cortex) networks. Information about visual features and their categories were encoded into point attractors of the IT and PC networks, respectively. In the task, the IT network of the right hemisphere was stimulated with a cue feature. After a delay period, the IT network of the left hemisphere was simultaneously stimulated with the choice feature and an irrelevant feature. The cue and choice features belong to the same category, while the irrelevant feature belongs to another category. To complete the task, the IT network must select the point attractor corresponding to the choice feature. We demonstrate that the top-down pathway (PC-to-IT) triggers the retrieval of long-term memory of the choice feature from the IT, and the bottom-up pathway (IT-to-PC) contributes to the maintenance of the retrieved memory during the delay period. The key mechanism for the retrieval and maintenance of that memory is the dynamic linkage of attractors across separate cortical networks. We show that a single hemisphere is sufficient for the memory retrieval, but it is advantageous to use the two hemispheres because the retrieved memory is thereby retained with greater reliability until the brain chooses the choice feature.

Action Potentials↗
PubMed2003

Changing excitation and inhibition in simulated neural networks: effects on induced bursting behavior.

The development of synchronous bursting in neuronal ensembles represents an important change in network behavior. To determine the influences on development of such synchronous bursting behavior we study the dynamics of small networks of sparsely connected excitatory and inhibitory neurons using numerical simulations. The synchronized bursting activities in networks evoked by background spikes are investigated. Specifically, patterns of bursting activity are examined when the balance between excitation and inhibition on neuronal inputs is varied and the fraction of inhibitory neurons in the network is changed. For quantitative comparison of bursting activities in networks, measures of the degree of synchrony are used. We demonstrate how changes in the strength of excitation on inputs of neurons can be compensated by changes in the strength of inhibition without changing the degree of synchrony in the network. The effects of changing several network parameters on the network activity are analyzed and discussed. These changes may underlie the transition of network activity from normal to potentially pathologic (e.g., epileptic) states.

Action Potentials↗
PubMed2004

Frequency-dependent selection of alternative spinal pathways with common periodic sensory input.

Electrical stimulation of the lumbar cord at distinct frequency ranges has been shown to evoke either rhythmical, step-like movements (25-50 Hz) or a sustained extension (5-15 Hz) of the paralysed lower limbs in complete spinal cord injured subjects. Frequency-dependent activation of previously "silent" spinal pathways was suggested to contribute to the differential responsiveness to distinct neuronal "codes" and the modifications in the electromyographic recordings during the actual implementation of the evoked motor tasks. In the present study we examine this suggestion by means of a simplified biology-based neuronal network. Involving two basic mechanisms, temporal summation of synaptic input and presynaptic inhibition, the model exhibits several patterns of mono- and/or oligo-synaptic motor output in response to different interstimulus intervals. It thus reproduces fundamental input-output features of the lumbar cord isolated from the brain. The results confirm frequency-dependent spinal pathway selection as a simple mechanism which enables the cord to respond to distinct neuronal codes with different motor behaviours and to control the actual performance of the latter.

Action Potentials↗
PubMed2006

Neural signatures: multiple coding in spiking-bursting cells.

Recent experiments have revealed the existence of neural signatures in the activity of individual cells of the pyloric central pattern generator (CPG) of crustacean. The neural signatures consist of cell-specific spike timings in the bursting activity of the neurons. The role of these intraburst neural fingerprints is still unclear. It has been reported previously that some muscles can reflect small changes in the spike timings of the neurons that innervate them. However, it is unclear to what extent neural signatures contribute to the command message that the muscles receive from the motoneurons. It is also unknown whether the signatures have any functional meaning for the neurons that belong to the same CPG or to other interconnected CPGs. In this paper, we use realistic neural models to study the ability of single cells and small circuits to recognize individual neural signatures. We show that model cells and circuits can respond distinctly to the incoming neural fingerprints in addition to the properties of the slow depolarizing waves. Our results suggest that neural signatures can be a general mechanism of spiking-bursting cells to implement multicoding.

Action Potentials↗
PubMed2002

Intronic mutations outside of Alu-repeat-rich domains of the LDL receptor gene are a cause of familial hypercholesterolemia.

Familial hypercholesterolemia (FH), a frequent monogenic condition complicated by premature cardiovascular disease, is characterized by high allelic heterogeneity at the low-density lipoprotein receptor ( LDLR) locus. Despite more than a decade of genetic testing, knowledge about intronic disease-causing mutations has remained limited because of lack of available genomic sequences. Based on the finding from bioinformatic analysis that Alu repeats represent 85% of LDLR intronic sequences outside exon-intron junctions, we designed a strategy to improve the exploration of genomic regions in the vicinity of exons in 110 FH subjects from an admixed population. In the first group of 42 patients of negative mutation carriers, as previously established by former screening strategies (denaturing gradient gel electrophoresis, DNA sequencing with former primers overlapping splice-sites, Southern Blotting), about half ( n=22) were found to be carriers of at least one heterozygous mutation. Among a second group of 68 newly recruited patients, 27% of mutation carriers ( n=37) had a splicing regulatory mutation. Overall, out of the 54 mutations identified, 13 were intronic, and 18 were novel, out of which nearly half were intronic. Two novel intronic mutations (IVS8-10G-->A within the polypyrimidine tract and IVS7+10G-->A downstream of donor site) might create potential aberrant splice sites according to neural-network computed estimation, contrary to 31 common single nucleotide variations also identified at exon-intron junctions. This new strategy of detecting the most likely disease-causing LDLR mutations outside of Alu-rich genomic regions reveals that intronic mutations may have a greater impact than previously reported on the molecular basis of FH.

Base Sequence↗
PubMed2006

Dopamine modulation in the basal ganglia locks the gate to working memory.

The prefrontal cortex and basal ganglia are deeply implicated in working memory. Both structures are subject to dopaminergic neuromodulation in a way that exerts a critical influence on the proper operation of working memory. We present a novel network model to elucidate the role of phasic dopamine in the interaction of these two structures in initiating and maintaining mnemonic activity. We argue that neuromodulation plays a critical role in protecting memories against both internal and external sources of noise. Increases in cortical gain engendered by prefrontal dopamine release help make memories robust against external distraction, but do not offer protection against internal noise accompanying recurrent cortical activity. Rather, the output of the basal ganglia provides the gating function of stabilization against noise and distraction by enhancing select memories through targeted disinhibition of cortex. Dopamine in the basal ganglia effectively locks this gate by influencing the stability of up and down states in the striatum. Dopamine's involvement in affective processing endows this gating with specificity to motivational salience. We model a spatial working memory task and show that these combined effects of dopamine lead to superior performance.

Action Potentials↗
PubMedDate not supplied

A continuous attractor network model without recurrent excitation: maintenance and integration in the head direction cell system.

Motivated by experimental observations of the head direction system, we study a three population network model that operates as a continuous attractor network. This network is able to store in a short-term memory an angular variable (the head direction) as a spatial profile of activity across neurons in the absence of selective external inputs, and to accurately update this variable on the basis of angular velocity inputs. The network is composed of one excitatory population and two inhibitory populations, with inter-connections between populations but no connections within the neurons of a same population. In particular, there are no excitatory-to-excitatory connections. Angular velocity signals are represented as inputs in one inhibitory population (clockwise turns) or the other (counterclockwise turns). The system is studied using a combination of analytical and numerical methods. Analysis of a simplified model composed of threshold-linear neurons gives the conditions on the connectivity for (i) the emergence of the spatially selective profile, (ii) reliable integration of angular velocity inputs, and (iii) the range of angular velocities that can be accurately integrated by the model. Numerical simulations allow us to study the proposed scenario in a large network of spiking neurons and compare their dynamics with that of head direction cells recorded in the rat limbic system. In particular, we show that the directional representation encoded by the attractor network can be rapidly updated by external cues, consistent with the very short update latencies observed experimentally by Zugaro et al. (2003) in thalamic head direction cells.

Action Potentials↗
PubMed2007

Local cortical circuit model inferred from power-law distributed neuronal avalanches.

How cortical neurons process information crucially depends on how their local circuits are organized. Spontaneous synchronous neuronal activity propagating through neocortical slices displays highly diverse, yet repeatable, activity patterns called "neuronal avalanches". They obey power-law distributions of the event sizes and lifetimes, presumably reflecting the structure of local circuits developed in slice cultures. However, the explicit network structure underlying the power-law statistics remains unclear. Here, we present a neuronal network model of pyramidal and inhibitory neurons that enables stable propagation of avalanche-like spiking activity. We demonstrate a neuronal wiring rule that governs the formation of mutually overlapping cell assemblies during the development of this network. The resultant network comprises a mixture of feedforward chains and recurrent circuits, in which neuronal avalanches are stable if the former structure is predominant. Interestingly, the recurrent synaptic connections formed by this wiring rule limit the number of cell assemblies embeddable in a neuron pool of given size. We investigate how the resultant power laws depend on the details of the cell-assembly formation as well as on the inhibitory feedback. Our model suggests that local cortical circuits may have a more complex topological design than has previously been thought.

Action Potentials↗
PubMed2006

Neural network model of the primary visual cortex: from functional architecture to lateral connectivity and back.

The role of intrinsic cortical dynamics is a debatable issue. A recent optical imaging study (Kenet et al., 2003) found that activity patterns similar to orientation maps (OMs), emerge in the primary visual cortex (V1) even in the absence of sensory input, suggesting an intrinsic mechanism of OM activation. To better understand these results and shed light on the intrinsic V1 processing, we suggest a neural network model in which OMs are encoded by the intrinsic lateral connections. The proposed connectivity pattern depends on the preferred orientation and, unlike previous models, on the degree of orientation selectivity of the interconnected neurons. We prove that the network has a ring attractor composed of an approximated version of the OMs. Consequently, OMs emerge spontaneously when the network is presented with an unstructured noisy input. Simulations show that the model can be applied to experimental data and generate realistic OMs. We study a variation of the model with spatially restricted connections, and show that it gives rise to states composed of several OMs. We hypothesize that these states can represent local properties of the visual scene.

Action Potentials↗
PubMed2006

The influence of cortical feature maps on the encoding of the orientation of a short line.

The inhomogeneous distribution of the receptive fields of cortical neurons influences the cortical representation of the orientation of short lines seen in visual images. We construct a model of the response of populations of neurons in the human primary visual cortex by combining realistic response properties of individual neurons and cortical maps of orientation and location preferences. The encoding error, which characterizes the difference between the parameters of a visual stimulus and their cortical representation, is calculated using Fisher information as the square root of the variance of a statistically efficient estimator. The error of encoding orientation varies considerably with the location and orientation of the short line stimulus as modulated by the underlying orientation preference map. The average encoding error depends only weakly on the structure of the orientation preference map and is much smaller than the human error of estimating orientation measured psychophysically. From this comparison we conclude that the actual mechanism of orientation perception does not make efficient use of all the information available in the neuronal responses and that it is the decoding of visual information from neuronal responses that limits psychophysical performance.

Action Potentials↗
PubMed2006

On the sensitive dependence on initial conditions of the dynamics of networks of spiking neurons.

We have previously formulated an abstract dynamical system for networks of spiking neurons and derived a formal result that identifies the criterion for its dynamics, without inputs, to be "sensitive to initial conditions". Since formal results are applicable only to the extent to which their assumptions are valid, we begin this article by demonstrating that the assumptions are indeed reasonable for a wide range of networks, particularly those that lack overarching structure. A notable aspect of the criterion is the finding that sensitivity does not necessarily arise from randomness of connectivity or of connection strengths, in networks. The criterion guides us to cases that decouple these aspects: we present two instructive examples of networks, one with random connectivity and connection strengths, yet whose dynamics is insensitive, and another with structured connectivity and connection strengths, yet whose dynamics is sensitive. We then argue based on the criterion and the gross electrophysiology of the cortex that the dynamics of cortical networks ought to be almost surely sensitive under conditions typically found there. We supplement this with two examples of networks modeling cortical columns with widely differing qualitative dynamics, yet with both exhibiting sensitive dependence. Next, we use the criterion to construct a network that undergoes bifurcation from sensitive dynamics to insensitive dynamics when the value of a control parameter is varied. Finally, we extend the formal result to networks driven by stationary input spike trains, deriving a superior criterion than previously reported.

Action Potentials↗
PubMed2006

Fitting experimental data to models that use morphological data from public databases.

Ideally detailed neuron models should make use of morphological and electrophysiological data from the same cell. However, this rarely happens. Typically a modeler will choose a cell morphology from a public database, assign standard values for Ra, Cm, and other parameters and then do the modeling study. The assumption is that the model will produce results representative of what might be obtained experimentally. To test this assumption we developed models of CA1 hippocampal pyramidal neurons using 4 different morphologies obtained from 3 public databases. The multiple run fitter in NEURON was used to fit parameter values in each of the 4 morphological models to match experimental data recorded from 19 CA1 pyramidal cells. Fits with fixed standard parameter values produced results that were generally not representative of our experimental data. However, when parameter values were allowed to vary, excellent fits were obtained in almost all cases, but the fitted parameter values were very different among the 4 reconstructions and did not match standard values. The differences in fitted values can be explained by very different diameters, total lengths, membrane areas and volumes among the reconstructed cells, reflecting either cell heterogeneity or issues with the reconstruction data. The fitted values compensated for these differences to make the database cells and experimental cells more similar electrotonically. We conclude that models using fully reconstructed morphologies need to be calibrated with experimental data (even when morphological and electrophysiological data come from the same cell), model results should be generated with multiple reconstructions, morphological and experimental cells should come from the same strain of animal at the same age, and blind use of standard parameter values in models that use reconstruction data may not produce representative experimental results.

Action Potentials↗
PubMed2006

A multivariate population density model of the dLGN/PGN relay.

Using a population density approach we study the dynamics of two interacting collections of integrate-and-fire-or-burst (IFB) neurons representing thalamocortical (TC) cells from the dorsal lateral geniculate nucleus (dLGN) and thalamic reticular (RE) cells from the perigeniculate nucleus (PGN). Each population of neurons is described by a multivariate probability density function that satisfies a conservation equation with appropriately defined probability fluxes and boundary conditions. The state variables of each neuron are the membrane potential and the inactivation gating variable of the low-threshold Ca2+ current I(T). The synaptic coupling of the populations and external excitatory drive are modeled by instantaneous jumps in the membrane potential of postsynaptic neurons. The population density model is validated by comparing its response to time-varying retinal input to Monte Carlo simulations of the corresponding IFB network composed of 100 to 1,000 cells per population. In the absence of retinal input, the population density model exhibits rhythmic bursting similar to the 7 to 14 Hz oscillations associated with slow wave sleep that require feedback inhibition from RE to TC cells. When the TC and RE cell potassium leakage conductances are adjusted to represent cholingergic neuromodulation and arousal of the network, rhythmic bursting of the probability density model may either persists or be eliminated depending on the number of excitatory (TC to RE) or inhibitory (RE to TC) connections made by each presynaptic cell. When the probability density model is stimulated with constant retinal input (10-100 spikes/sec), a wide range of responses are observed depending on cellular parameters and network connectivity. These include asynchronous burst and tonic spikes, sleep spindle-like rhythmic bursting, and oscillations in population firing rate that are distinguishable from sleep spindles due to their amplitude, frequency, or the presence of tonic spikes. In this context of dLGN/PGN network modeling, we find the population density approach using 2,500 mesh points and resolving membrane voltage to 0.7 mV is over 30 times more efficient than 1,000-cell Monte Carlo simulations.

Action Potentials↗