arXiv subjects

Sacha Jennifer van Albada

Publications and source records attributed to Sacha Jennifer van Albada.

8 recordsLinked to original sources

arXiv2020

Usage and Scaling of an Open-Source Spiking Multi-Area Model of Monkey Cortex

We are entering an age of `big' computational neuroscience, in which neural network models are increasing in size and in numbers of underlying data sets. Consolidating the zoo of models into large-scale models simultaneously consistent with a wide range of data is only possible through the effort of large teams, which can be spread across multiple research institutions. To ensure that computational neuroscientists can build on each other's work, it is important to make models publicly available as well-documented code. This chapter describes such an open-source model, which relates the connectivity structure of all vision-related cortical areas of the macaque monkey with their resting-state dynamics. We give a brief overview of how to use the executable model specification, which employs NEST as simulation engine, and show its runtime scaling. The solutions found serve as an example for organizing the workflow of future models from the raw experimental data to the visualization of the results, expose the challenges, and give guidance for the construction of ICT infrastructure for neuroscience.

q-bio.NC↗

arXiv2020

Bringing Anatomical Information into Neuronal Network Models

For constructing neuronal network models computational neuroscientists have access to wide-ranging anatomical data that nevertheless tend to cover only a fraction of the parameters to be determined. Finding and interpreting the most relevant data, estimating missing values, and combining the data and estimates from various sources into a coherent whole is a daunting task. With this chapter we aim to provide guidance to modelers by describing the main types of anatomical data that may be useful for informing neuronal network models. We further discuss aspects of the underlying experimental techniques relevant to the interpretation of the data, list particularly comprehensive data sets, and describe methods for filling in the gaps in the experimental data. Such methods of `predictive connectomics' estimate connectivity where the data are lacking based on statistical relationships with known quantities. It is instructive, and in certain cases necessary, to use organizational principles that link the plethora of data within a unifying framework where regularities of brain structure can be exploited to inform computational models. In addition, we touch upon the most prominent features of brain organization that are likely to influence predicted neuronal network dynamics, with a focus on the mammalian cerebral cortex. Given the still existing need for modelers to navigate a complex data landscape full of holes and stumbling blocks, it is vital that the field of neuroanatomy is moving toward increasingly systematic data collection, representation, and publication.

q-bio.NC↗

arXiv2018

Transformation of arbitrary distributions to the normal distribution with application to EEG test-retest reliability

Many variables in the social, physical, and biosciences, including neuroscience, are non-normally distributed. To improve the statistical properties of such data, or to allow parametric testing, logarithmic or logit transformations are often used. Box-Cox transformations or ad hoc methods are sometimes used for parameters for which no transformation is known to approximate normality. However, these methods do not always give good agreement with the Gaussian. A transformation is discussed that maps probability distributions as closely as possible to the normal distribution, with exact agreement for continuous distributions. To illustrate, the transformation is applied to a theoretical distribution, and to quantitative electroencephalographic (qEEG) measures from repeat recordings of 32 subjects which are highly non-normal. Agreement with the Gaussian was better than using logarithmic, logit, or Box-Cox transformations. Since normal data have previously been shown to have better test-retest reliability than non-normal data under fairly general circumstances, the implications of our transformation for the test-retest reliability of parameters were investigated. Reliability was shown to improve with the transformation, where the improvement was comparable to that using Box-Cox. An advantage of the general transformation is that it does not require laborious optimization over a range of parameters or a case-specific choice of form.

stat.ME↗

arXiv2018

Mean-field modeling of the basal ganglia-thalamocortical system. II. Dynamics of parkinsonian oscillations

Neuronal correlates of Parkinson's disease (PD) include a slowing of the electroencephalogram (EEG) and enhanced synchrony at 3-7 and 7-30 Hz in the basal ganglia, thalamus, and cortex. This study describes the dynamics of a physiologically based mean-field model of the basal ganglia-thalamocortical system, and shows how it accounts for key electrophysiological correlates of PD. Its connectivity comprises partially segregated direct and indirect pathways through the striatum, a hyperdirect pathway involving a corticosubthalamic projection, thalamostriatal feedback, and local inhibition in striatum and external pallidum (GPe). In a companion paper, realistic steady-state firing rates were obtained for the healthy state, and after dopamine loss modeled by weaker direct and stronger indirect pathways, reduced intrapallidal inhibition, lower firing thresholds of the GPe and subthalamic nucleus (STN), a stronger striato-GPe projection, and weaker cortical interactions. Here we show that oscillations around 5 and 20 Hz can arise with a strong indirect pathway, which also increases synchrony throughout the basal ganglia. Further, increased theta power with nigrostriatal degeneration correlates with reduced alpha power and peak frequency, matching experiments. Unlike the hyperdirect pathway, the indirect pathway sustains oscillations with realistic phase relationships. Changes in basal ganglia responses to transient stimuli accord with experimental data. Reduced cortical gains due to both nigrostriatal and mesocortical dopamine loss lead to slower cortical activity changes and may be related to bradykinesia. Finally, increased EEG power found in some studies may be partly explained by a lower effective GPe firing threshold, reduced GPe-GPe inhibition, and/or weaker intracortical connections in PD. Strict separation of the direct and indirect pathways is not necessary for these results.

q-bio.NC↗

arXiv2018

Mean-field modeling of the basal ganglia-thalamocortical system. I. Firing rates in healthy and parkinsonian states

Parkinsonism leads to various electrophysiological changes in the basal ganglia-thalamocortical system (BGTCS), often including elevated discharge rates of the subthalamic nucleus (STN) and the output nuclei, and reduced activity of the globus pallidus external segment (GPe). These rate changes have been explained qualitatively in terms of the direct/indirect pathway model, involving projections of distinct striatal populations to the output nuclei and GPe. Although these populations partly overlap, evidence suggests dopamine depletion differentially affects cortico-striato-pallidal connection strengths to the two pallidal segments. Dopamine loss may also decrease the striatal signal-to-noise ratio, reducing both corticostriatal coupling and striatal firing thresholds. Here we present a mean-field model of the BGTCS with structure and parameter estimates closely based on physiology and anatomy. Changes in firing rates due to modeled dopamine loss are compared with experiment. Our results suggest that a stronger indirect pathway, possibly combined with a weakened direct pathway, is compatible with empirical evidence. However, altered corticostriatal connection strengths are probably not solely responsible for substantially increased STN activity often found. A lower STN firing threshold, weaker intracortical inhibition, and stronger striato-GPe inhibition help explain the relatively large increase in STN rate. Changes in cortex, GPe, and STN help normalize the cortical rate, also in accord with experiments. The model integrates the basal ganglia into a unified framework along with an existing thalamocortical model that accounts for a wide range of electrophysiological phenomena. A companion paper discusses the dynamics and oscillations of this combined system.

q-bio.NC↗

arXiv2018

Variability of model-free and model-based quantitative measures of EEG

The extent of intra-individual and inter-individual variability is an important factor in determining the statistical, and hence possibly clinical, significance of observed differences in the EEG. This study investigates the changes in classical quantitative EEG (qEEG) measures, as well as of parameters obtained by fitting frequency spectra to a continuum model of brain electrical activity. These parameters may have extra variability due to model selection and fitting. Besides estimating levels of intra-individual and inter-individual variability, we determine approximate time scales for change in qEEG measures and model parameters. This provides an estimate of the recording length needed to capture a given percentage of the total intra-individual variability. Also, if more precise time scales can be obtained in future, these may aid the characterization of physiological processes underlying various EEG measures. Heterogeneity of the subject group was constrained by testing only healthy males in a narrow age range (mean = 22.3 years, sd = 2.7). Resting eyes-closed EEGs of 32 subjects were recorded at weekly intervals over an approximately six-week period. Of these, 13 subjects had follow-up recordings spanning up to a year. QEEG measures, computed from Cz spectra, were powers in five frequency bands, alpha peak frequency, and spectral entropy. Of these, theta, alpha, and beta band powers were most reproducible. Of nine model parameters obtained by fitting model predictions to experiment, the most reproducible ones quantified the total power and the time delay between cortex and thalamus. About 95% of the maximum change in spectral parameters was reached within minutes of recording time, implying that repeat recordings are not necessary to capture the bulk of the variability in EEG spectra likely to occur in the resting eyes-closed state on the scale of a year.

q-bio.NC↗

arXiv2016

Boundary Dynamics of Three-Dimensional Asymptotically Anti-de Sitter Space-Times

This thesis is organized as follows. In Chapter 2, some preliminaries are given on isometries and conformal symmetries, and we become familiar with the Virasoro algebra. Two examples of classical central charges are discussed. Chapter 3 contains an introduction to the Hamiltonian formulation of gauge theories in the context of Maxwell theory. The knowledge gained in Chapter 3 is applied to general relativity in Chapter 4. The Hamiltonian is shown to acquire a surface term due to the presence of the boundary. The geometrical properties of anti-de Sitter space and the BTZ black hole are the subject of Chapter 5. The main part of the discussion follows in Chapter 6, which contains the calculation of the central charge in the asymptotic symmetry algebra of asymptotically AdS3 space-times. After some preliminaries on the AdS/CFT correspondence and Chern-Simons theory, the derivation of the boundary conformal field theory is summarized in Chapter 7. Finally, Strominger's entropy calculation is presented in Chapter 8, along with some comments on this approach.

hep-th↗

arXiv2015

Scalability of asynchronous networks is limited by one-to-one mapping between effective connectivity and correlations

Network models are routinely downscaled compared to nature in terms of numbers of nodes or edges because of a lack of computational resources, often without explicit mention of the limitations this entails. While reliable methods have long existed to adjust parameters such that the first-order statistics of network dynamics are conserved, here we show that limitations already arise if also second-order statistics are to be maintained. The temporal structure of pairwise averaged correlations in the activity of recurrent networks is determined by the effective population-level connectivity. We first show that in general the converse is also true and explicitly mention degenerate cases when this one-to-one relationship does not hold. The one-to-one correspondence between effective connectivity and the temporal structure of pairwise averaged correlations implies that network scalings should preserve the effective connectivity if pairwise averaged correlations are to be held constant. Changes in effective connectivity can even push a network from a linearly stable to an unstable, oscillatory regime and vice versa. On this basis, we derive conditions for the preservation of both mean population-averaged activities and pairwise averaged correlations under a change in numbers of neurons or synapses in the asynchronous regime typical of cortical networks. We find that mean activities and correlation structure can be maintained by an appropriate scaling of the synaptic weights, but only over a range of numbers of synapses that is limited by the variance of external inputs to the network. Our results therefore show that the reducibility of asynchronous networks is fundamentally limited.

q-bio.NC↗