Biology subjects

van Albada, S. J.

Publications and source records attributed to van Albada, S. J..

9 recordsLinked to original sources

bioRxiv2025

Spontaneous spiking statistics form unique area-specific fingerprints and reflect the hierarchy of cerebral cortex

The cerebral cortex is hierarchically organised from sensory to higher cognitive areas1-4. Several dynamical5-8 and anatomical1-4,8 measures, such as timescales and neurotransmitter receptor expression, have independently been linked to the cortical hierarchy. However, a systematic and quantitative characterisation of the relationship between spontaneous spiking activity and the cortical hierarchy remains elusive. Here, we test the hypothesis that single-neuron spontaneous spiking statistics uniquely characterise each cortical area, and that they quantitatively correlate with the cortical hierarchy. We study the spontaneous activity of neurons in seven macaque cortical areas (V1, V4, DP, 7A, M1, PMd, PFC)9-12 in the eyes-open and eyes-closed conditions recorded in a dim-lit room. First, we uncover that the firing rate, inter-spike interval variation, and cross-correlation form a unique fingerprint of the cortical areas, but only when considering them in combination. Second, we show that the differences between the spiking statistics correlate with multiple anatomical markers1,2,4,13-17 of the cortical hierarchy. This effect is much stronger in the eyes-closed condition, suggesting that visual input or the expectation thereof modulates the hierarchical organisation of spontaneous activity. We also observe an increase in timescales up the hierarchy, in agreement with previous findings5,18,19. In conclusion, we demonstrate that spontaneous single-neuron spiking activity reflects the hierarchical organisation of the cerebral cortex: distinct spiking statistics for hierarchically distant areas; similar statistics for nearby areas. Our results thus add a new dynamical dimension to the concept of the cortical hierarchy.

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bioRxiv2024

Modeling neuron-astrocyte interactions in neural networks using distributed simulation

Astrocytes engage in local interactions with neurons, synapses, other glial cell types, and the vasculature through intricate cellular and molecular processes, playing an important role in brain information processing, plasticity, cognition, and behavior. This study advances understanding of local interactions and self-organization of neuron-astrocyte networks and contributes to the broader investigation of their potential relationship with global activity regimes and overall brain function. We present six new contributions: (1) the development of a new model-building framework for neuron-astrocyte networks, (2) the introduction of connectivity concepts for tripartite neuron-astrocyte interactions in biological neural networks, (3) the design of a scalable architecture capable of simulating networks with up to a million cells, (4) a formalized description of neuron-astrocyte modeling that facilitates reproducibility, (5) the integration of experimental data to a greater extent than existing studies, and (6) simulation results demonstrating how neuron-astrocyte interactions drive the emergence of synchronization in local neuronal groups. Specifically, we develop a new technology for representing astrocytes and their interactions with neurons in distributed simulation code for large-scale spiking neuronal networks. This includes an astrocyte model with calcium dynamics, an extended neuron model receiving calcium-dependent signals from astrocytes, and a parallelized connectivity generation scheme for tripartite interactions between pre- and postsynaptic neurons and astrocytes. We verify the efficiency of our reference implementation through benchmarks varying in computing resources and network sizes. Our in silico experiments reproduce experimental data on astrocytic effects on neuronal synchronization, demonstrating that astrocytes consistently induce local synchronization in groups of neurons across various connectivity schemes and global activity regimes. By adjusting the strength of neuron-astrocyte interactions, we can switch the global activity regime from asynchronous to network-wide synchronization. This work represents an advancement in neuron-astrocyte modeling, introducing a novel framework that enables large-scale simulations of astrocytic influence on neuronal networks. Author summaryAstrocytes play an important role in regulating synapses, neuronal networks, and cognitive functions. However, models that include both neurons and astrocytes are underutilized compared to models with only neurons in theoretical and computational studies. We address this issue by developing theoretical concepts for representing astrocytic connectivity and interactions and provide a reference implementation supporting distributed parallel computing in the spiking neural network simulator NEST. Using these capabilities, we show how astrocytes help to synchronize neural networks under various connection patterns and activity levels. The new technology makes it easier to include astrocytes in simulations of neural systems, promoting the construction of more realistic, relevant, and reproducible models.

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bioRxiv2024

Cell-type specific projection patterns promote balanced activity in cortical microcircuits

The structure of neural networks provides the stage on which their activity unfolds. Models of cerebral cortex linking connectivity to dynamics have primarily relied on probabilistic estimates of connectivity derived from paired electrophysiological recordings or single-neuron morphologies obtained by light microscopy (LM) studies. Only recently have electron microscopy (EM) data sets been processed and made available for volumes of cortex on the cubic millimeter scale, exposing the actual connectivity of neurons. Here, we construct a population-based, layer-resolved connectivity map from EM data, taking into account the spatial scale of local cortical connectivity. We compare the obtained connectivity with a map based on an established LM data set. Simulating spiking neural networks constrained by the derived microcircuit architectures shows that both models allow for biologically plausible ongoing activity when synaptic currents caused by neurons outside the network model are specifically adjusted for every population. However, differentially varying the external current onto excitatory and inhibitory populations reveals that only the EM-based model robustly exhibits biologically plausible dynamics. Our work confirms the long-standing hypothesis that a preference of excitatory neurons for inhibitory targets, not present in the LM-based model, promotes balanced activity in cortical microcircuits.

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bioRxiv2024

Cortical multi-area model with joint excitatory-inhibitory clusters accounts for spiking statistics, inter-area propagation, and variability dynamics

The primate brain uses billions of interacting neurons to produce macroscopic dynamics and behavior, but current methods only allow neuroscientists to investigate a subset of the neural activity. Computational modeling offers an alternative testbed for scientific hypotheses, by allowing full control of the system. Here, we test the hypothesis that local cortical circuits are organized into joint clusters of excitatory and inhibitory neurons by investigating the influence of this organizational principle on cortical resting-state spiking activity, inter-area propagation, and variability dynamics. The model represents all vision-related areas in one hemisphere of the macaque cortex with biologically realistic neuron densities and connectivities, expanding on a previous unclustered model of this system. Each area is represented by a square millimeter microcircuit including the full density of neurons and synapses, avoiding downscaling artifacts and testing cortical dynamics at the natural scale. We find that joint excitatory-inhibitory clustering normalizes spiking activity statistics in terms of firing rate distributions and inter-spike interval variability. A comparison with data from cortical areas V1, V4, FEF, 7a, and DP shows that the clustering enables the resting-state activity of especially higher cortical areas to be better captured. In addition, we find that the clustering supports signal propagation across all areas in both feedforward and feedback directions with reasonable latencies. Finally, we also show that localized stimulation of the clustered model quenches the variability of neural activity, in agreement with experimental observations. We conclude that joint clustering of excitatory and inhibitory neurons is a likely organizational principle of local cortical circuits, supporting resting-state spiking activity statistics, inter-area propagation, and variability dynamics.

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bioRxiv2023

A Layered Microcircuit Model of Somatosensory Cortex with Three Interneuron Types and Cell-Type-Specific Short-Term Plasticity

Three major types of GABAergic interneurons, parvalbumin- (PV), somatostatin- (SOM) and vasoactive intestinal peptide-expressing (VIP) cells, play critical but distinct roles in the cortical microcircuitry. Their specific electrophysiology and connectivity shape their inhibitory functions. To study the network dynamics and signal processing specific to these cell types in the cerebral cortex, we developed a multi-layer model incorporating biologically realistic interneuron parameters from rodent somatosensory cortex. The model is fitted to in vivo data on cell-type-specific population firing rates. With a protocol of cell-type-specific stimulation, network responses when activating different neuron types are examined. The model reproduces the experimentally observed inhibitory effects of PV and SOM cells and disinhibitory effect of VIP cells on excitatory cells. We further create a version of the model incorporating cell-type-specific short-term synaptic plasticity (STP). While the ongoing activity with and without STP is similar, STP modulates the responses of Exc, SOM, and VIP cells to cell-type-specific stimulation, presumably by changing the dominant inhibitory pathways. With slight adjustments, the model also reproduces sensory responses of specific interneuron types recorded in vivo. Our model provides predictions on network dynamics involving cell-type-specific short-term plasticity and can serve to explore the computational roles of inhibitory interneurons in sensory functions.

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bioRxiv2023

Neural manifolds in V1 change with top-down signals from V4 targeting the foveal region

High-dimensional brain activity is often organized into lower-dimensional neural manifolds. However, the neural manifolds of the visual cortex remain understudied. Here, we study large-scale multielectrode electrophysiological recordings of macaque (Macaca mulatta) areas V1, V4 and DP with a high spatio-temporal resolution. We find, for the first time, that the population activity of V1 contains two separate neural manifolds, which correlate strongly with eye closure (eyes open/closed) and have distinct dimensionalities. Moreover, we find strong top-down signals from V4 to V1, particularly to the foveal region of V1, which are significantly stronger during the eyes-open periods, a previously unknown effect. Finally, in silico simulations of a balanced spiking neuron network qualitatively reproduce the experimental findings. Taken together, our analyses and simulations suggest that top-down signals modulate the population activity of V1, causing two distinct neural manifolds. We postulate that the top-down modulation during the eyes-open periods prepares V1 for fast and efficient visual responses, resulting in a type of visual stand-by state.

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bioRxiv2022

Ubiquitous lognormal distribution of neuron densities across mammalian cerebral cortex

Numbers of neurons and their spatial variation are fundamental organizational features of the brain. Despite the large corpus of cytoarchitectonic data available in the literature, the statistical distributions of neuron densities within and across brain areas remain largely uncharacterized. Here, we show that neuron densities are compatible with a lognormal distribution across cortical areas in several mammalian species, and find that this also holds true within cortical areas. A minimal model of noisy cell division, in combination with distributed proliferation times, can account for the coexistence of lognormal distributions within and across cortical areas. Our findings uncover a new organizational principle of cortical cytoarchitecture: the ubiquitous lognormal distribution of neuron densities, which adds to a long list of lognormal variables in the brain.

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bioRxiv2020

Spiking neural network model of motor cortex with joint excitatory and inhibitory clusters reflects task uncertainty, reaction times, and variability dynamics

When preparing a movement, we often rely on partial or incomplete information, which can decrement task performance. In behaving monkeys we show that the degree of cued target information is reflected in both, neural variability in motor cortex and behavioral reaction times. We study the underlying mechanisms in a spiking motor-cortical attractor model. By introducing a novel and biologically realistic network topology where excitatory neuron clusters are locally balanced with inhibitory neuron clusters we robustly achieve metastable network activity across a wide range of network parameters. In application to the monkey task, the model performs target-specific action selection and accurately reproduces the task-epoch dependent reduction of trial-to-trial variability in vivo where the degree of reduction directly reflects the amount of processed target information, while spiking irregularity remained constant throughout the task. In the context of incomplete cue information, the increased target selection time of the model can explain increased behavioral reaction times . We conclude that context-dependent neural and behavioral variability is a signum of attractor computation in the motor cortex.

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bioRxiv2018

Open Source Brain: a collaborative resource for visualizing, analyzing, simulating and developing standardized models of neurons and circuits

Computational models are powerful tools for investigating brain function in health and disease. However, biologically detailed neuronal and circuit models are complex and implemented in a range of specialized languages, making them inaccessible and opaque to many neuroscientists. This has limited critical evaluation of models by the scientific community and impeded their refinement and widespread adoption. To address this, we have combined advances in standardizing models, open source software development and web technologies to develop Open Source Brain, a platform for visualizing, simulating, disseminating and collaboratively developing standardized models of neurons and circuits from a range of brain regions. Model structure and parameters can be visualized and their dynamical properties explored through browser-controlled simulations, without writing code. Open Source Brain makes neural models transparent and accessible and facilitates testing, critical evaluation and refinement, thereby helping to improve the accuracy and reproducibility of models, and their dissemination to the wider community.

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