Biomedical subjects

Arjen van Ooyen

Publications and source records attributed to Arjen van Ooyen.

8 recordsLinked to original sources

PubMed2006

Mathematical modelling and numerical simulation of the morphological development of neurons.

BACKGROUND: The morphological development of neurons is a very complex process involving both genetic and environmental components. Mathematical modelling and numerical simulation are valuable tools in helping us unravel particular aspects of how individual neurons grow their characteristic morphologies and eventually form appropriate networks with each other. METHODS: A variety of mathematical models that consider (1) neurite initiation (2) neurite elongation (3) axon pathfinding, and (4) neurite branching and dendritic shape formation are reviewed. The different mathematical techniques employed are also described. RESULTS: Some comparison of modelling results with experimental data is made. A critique of different modelling techniques is given, leading to a proposal for a unified modelling environment for models of neuronal development. CONCLUSION: A unified mathematical and numerical simulation framework should lead to an expansion of work on models of neuronal development, as has occurred with compartmental models of neuronal electrical activity.

Animals↗

PubMed2006

A mathematical framework for modeling axon guidance.

In this paper, a simulation tool for modeling axon guidance is presented. A mathematical framework in which a wide range of models can been implemented has been developed together with efficient numerical algorithms. In our framework, models can be defined that consist of concentration fields of guidance molecules in combination with finite-dimensional state vectors. These vectors can characterize migrating growth cones, target neurons that release guidance molecules, or other cells that act as sources of membrane-bound or diffusible guidance molecules. The underlying mathematical framework is presented as well as the numerical methods to solve them. The potential applications of our simulation tool are illustrated with a number of examples, including a model of topographic mapping.

Algorithms↗

PubMed2006

Envisioning the reward.

The primary visual cortex (area V1) is for vision. At least, that is what most researchers believe. However, in a recent issue of Science, Shuler and Bear demonstrate a correlate of reward timing in area V1. This surprising result indicates that brain circuits for reward processing are more extensive than expected and that area V1 has more functionality than previously thought.

Animals↗

PubMed2006

Compartment volume influences microtubule dynamic instability: a model study.

Microtubules (MTs) are cytoskeletal polymers that exhibit dynamic instability, the random alternation between growth and shrinkage. MT dynamic instability plays an essential role in cell development, division, and motility. To investigate dynamic instability, simulation models have been widely used. However, conditions under which the concentration of free tubulin fluctuates as a result of growing or shrinking MTs have not been studied before. Such conditions can arise, for example, in small compartments, such as neuronal growth cones. Here we investigate by means of computational modeling how concentration fluctuations caused by growing and shrinking MTs affect dynamic instability. We show that these fluctuations shorten MT growth and shrinkage times and change their distributions from exponential to non-exponential, gamma-like. Gamma-like distributions of MT growth and shrinkage times, which allow optimal stochastic searching by MTs, have been observed in various cell types and are believed to require structural changes in the MT during growth or shrinkage. Our results, however, show that these distributions can already arise as a result of fluctuations in the concentration of free tubulin due to growing and shrinking MTs. Such fluctuations are possible not only in small compartments but also when tubulin diffusion is slow or when many MTs (de)polymerize synchronously. Volume and all other factors that influence these fluctuations can affect MT dynamic instability and, consequently, the processes that depend on it, such as neuronal growth cone behavior and cell motility in general.

Animals↗

PubMed2005

Role of synaptic inhibition in spatiotemporal patterning of cortical activity.

Developmental upregulation of the GABAA receptor alpha1 subunit causes a faster decay of GABAergic inhibitory postsynaptic currents (IPSCs) in the visual cortex around the time of eye opening. In alpha1 deficient mice, a juvenile type of GABAA receptors is retained during maturation. As a result the decay time of the IPSCs is longer in alpha1-/- mice than in WT mice during the whole life span of the mice. Hence they form a valuable mouse model for studies on cellular aspects of neuronal network functioning. Using voltage sensitive dye imaging methods, we monitored the spatiotemporal excitation patterning in visual cortex slices upon local stimulation of the network. We found that in the alpha1-/- mice, the ability of the network to fire synchronously at gamma-frequencies (20-50 Hz) is diminished. This finding indicates that early onset of GABA synapse maturation is required for the normal neuronal network function in the maturating visual cortex.

Aging↗

PubMed2005

Attention-gated reinforcement learning of internal representations for classification.

Animal learning is associated with changes in the efficacy of connections between neurons. The rules that govern this plasticity can be tested in neural networks. Rules that train neural networks to map stimuli onto outputs are given by supervised learning and reinforcement learning theories. Supervised learning is efficient but biologically implausible. In contrast, reinforcement learning is biologically plausible but comparatively inefficient. It lacks a mechanism that can identify units at early processing levels that play a decisive role in the stimulus-response mapping. Here we show that this so-called credit assignment problem can be solved by a new role for attention in learning. There are two factors in our new learning scheme that determine synaptic plasticity: (1) a reinforcement signal that is homogeneous across the network and depends on the amount of reward obtained after a trial, and (2) an attentional feedback signal from the output layer that limits plasticity to those units at earlier processing levels that are crucial for the stimulus-response mapping. The new scheme is called attention-gated reinforcement learning (AGREL). We show that it is as efficient as supervised learning in classification tasks. AGREL is biologically realistic and integrates the role of feedback connections, attention effects, synaptic plasticity, and reinforcement learning signals into a coherent framework.

Animals↗

PubMed2004

Transport limited effects in a model of dendritic branching.

A variety of stochastic models of dendritic growth in developing neurons have been formulated previously. Such models indicate that the probability of a new branch forming in a growing tree may be modulated by factors such as the number of terminals in the tree and their centrifugal order. However, these models cannot identify any underlying biophysical mechanisms that may cause such dependencies. Here, we explore a new model in which branching depends on the concentration of a branch-determining substance in each terminal segment. The substance is produced in the cell body and is transported by active transport and diffusion to the terminals. The model reveals that transport-limited effects may give rise to the same modulation of branching as indicated by the stochastic models. Different limitations arise if transport is dominated by active transport or by diffusion.

Animals↗

PubMed2002

The effect of dendritic topology on firing patterns in model neurons.

Neuronal firing patterns are influenced by both membrane properties and dendritic morphology. Distinguishing two sources of morphological variability-metrics and topology-we investigate the extent to which model neurons that have the same metrical and membrane properties can still produce different firing patterns as a result of differences in dendritic topology. Within a set of dendritic trees that have the same number of terminal segments and the same total dendritic length, we show that firing frequency strongly correlates with topology as expressed by the mean dendritic path length. The effect of dendritic topology on firing frequency is bigger for trees with equal segment diameters than for trees whose segment diameters obey Rall's 3/2 power law. If active dendritic channels are present, dendritic topology influences not only firing frequency but also type of firing (regular, bursting).

Animals↗