Arjen van Ooyen

Activity-dependent structural plasticity

Plasticity in the brain reaches far beyond a mere changing of synaptic strengths. Recent time-lapse imaging in the living brain reveals ongoing structural plasticity by forming or breaking of synapses, motile spines, and re-routing of axonal branches in the developing and adult brain. Some forms of structural plasticity do not follow Hebbian- or anti-Hebbian paradigms of plasticity but rather appear to contribute to the homeostasis of network activity. Four decades of lesion studies have brought up a wealth of data on the mutual interdependence of neuronal activity, neurotransmitter release and neuronal morphogenesis and network formation. Here, we review these former studies on structural plasticity in the context of recent experimental studies. We compare spontaneous and experience-dependent structural plasticity with lesion-induced (reactive) structural plasticity that occurs during development and in the adult brain. Understanding the principles of neural network reorganization on a structural level is relevant for a deeper understanding of long-term memory formation as well as for the treatment of neurological diseases such as stroke.

Perceptual learning rules based on reinforcers and attention.

How does the brain learn those visual features that are relevant for behavior? In this article, we focus on two factors that guide plasticity of visual representations. First, reinforcers cause the global release of diffusive neuromodulatory signals that gate plasticity. Second, attentional feedback signals highlight the chain of neurons between sensory and motor cortex responsible for the selected action. We here propose that the attentional feedback signals guide learning by suppressing plasticity of irrelevant features while permitting the learning of relevant ones. By hypothesizing that sensory signals that are too weak to be perceived can escape from this inhibitory feedback, we bring attentional learning theories and theories that emphasized the importance of neuromodulatory signals into a single, unified framework.

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.

Does Morphology Influence Temporal Plasticity

Applying bounded weight-independent temporal plasticity rule to synapses from independent Poisson firing presynaptic neurons onto a conductance-based integrate-and-fire neuron leads to a bimodal distribution of synaptic strength (Song et al., 2000). We extend this model to investigate the effects of spreading the synapses over the dendritic tree. The results suggest that distal synapses tend to lose out to proximal ones in the competition for synaptic strength. Against expectations, versions of the plasticity rule with a smoother transition between potentiation and depression make little difference to the distribution or lead to all synapses losing.

NETMORPH: A Framework for the Stochastic Generation of Large Scale Neuronal Networks With Realistic Neuron Morphologies

We present a simulation framework, called NETMORPH, for the developmental generation of 3D large-scale neuronal networks with realistic neuron morphologies. In NETMORPH, neuronal morphogenesis is simulated from the perspective of the individual growth cone. For each growth cone in a growing axonal or dendritic tree, its actions of elongation, branching and turning are described in a stochastic, phenomenological manner. In this way, neurons with realistic axonal and dendritic morphologies, including neurite curvature, can be generated. Synapses are formed as neurons grow out and axonal and dendritic branches come in close proximity of each other. NETMORPH is a flexible tool that can be applied to a wide variety of research questions regarding morphology and connectivity. Research applications include studying the complex relationship between neuronal morphology and global patterns of synaptic connectivity. Possible future developments of NETMORPH are discussed.

Automated analysis of neuronal morphology, synapse number and synaptic recruitment

The shape, structure and connectivity of nerve cells are important aspects of neuronal function. Genetic and epigenetic factors that alter neuronal morphology or synaptic localization of pre- and post-synaptic proteins contribute significantly to neuronal output and may underlie clinical states. To assess the impact of individual genes and disease-causing mutations on neuronal morphology, reliable methods are needed. Unfortunately, manual analysis of immuno-fluorescence images of neurons to quantify neuronal shape and synapse number, size and distribution is labor-intensive, time-consuming and subject to human bias and error. We have developed an automated image analysis routine using steerable filters and deconvolutions to automatically analyze dendrite and synapse characteristics in immuno-fluorescence images. Our approach reports dendrite morphology, synapse size and number but also synaptic vesicle density and synaptic accumulation of proteins as a function of distance from the soma as consistent as expert observers while reducing analysis time considerably. In addition, the routine can be used to detect and quantify a wide range of neuronal organelles and is capable of batch analysis of a large number of images enabling high-throughput analysis.

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 Ralls 3/2 power law. If active dendritic channels are present, dendritic topology influences not only firing frequency but also type of firing (regular, bursting).

Competition in the development of nerve connections: a review of models

The establishment and refinement of neural circuits involve both the formation of new connections and the elimination of already existing connections. Elimination of connections occurs, for example, in the development of mononeural innervation of muscle fibres and in the formation of ocular dominance columns in the visual cortex. The process that leads to the elimination of connections is often referred to as axonal or synaptic competition. Although the notion of competition is commonly used, the process is not well understood - with respect to, for example, the type of competition, what axons and synapses are competing for, and the role of electrical activity. This article reviews the types of competition that have been distinguished and the models of competition that have been proposed. Models of both the neuromuscular system and the visual system are described. For each of these models, the assumptions on which it is based, its mathematical structure, and the extent to which it is supported by the experimental data are evaluated. Special attention is given to the different modelling approaches and the role of electrical activity in competition.

Using theoretical models to analyse neural development

The development of the nervous system is an extremely complex and dynamic process. Through the continuous interplay of genetic information and changing intra- and extracellular environments, the nervous system constructs itself from precursor cells that divide and form neurons, which migrate, differentiate and establish synaptic connections. Our understanding of neural development can be greatly assisted by mathematical and computational modelling, because it allows us to bridge the gap between system-level dynamics and the lower level cellular and molecular processes. This Review shows the potential of theoretical models to examine many aspects of neural development.The development of the nervous system is an extremely complex and dynamic process. Through the continuous interplay of genetic information and changing intra- and extracellular environments, the nervous system constructs itself from precursor cells that divide and form neurons, which migrate, differentiate and establish synaptic connections. Our understanding of neural development can be greatly assisted by mathematical and computational modelling, because it allows us to bridge the gap between system-level dynamics and the lower level cellular and molecular processes. This Review shows the potential of theoretical models to examine many aspects of neural development.

Impact of Dendritic Size and Dendritic Topology on Burst Firing in Pyramidal Cells

Neurons display a wide range of intrinsic firing patterns. A particularly relevant pattern for neuronal signaling and synaptic plasticity is burst firing, the generation of clusters of action potentials with short interspike intervals. Besides ion-channel composition, dendritic morphology appears to be an important factor modulating firing pattern. However, the underlying mechanisms are poorly understood, and the impact of morphology on burst firing remains insufficiently known. Dendritic morphology is not fixed but can undergo significant changes in many pathological conditions. Using computational models of neocortical pyramidal cells, we here show that not only the total length of the apical dendrite but also the topological structure of its branching pattern markedly influences inter- and intraburst spike intervals and even determines whether or not a cell exhibits burst firing. We found that there is only a range of dendritic sizes that supports burst firing, and that this range is modulated by dendritic topology. Either reducing or enlarging the dendritic tree, or merely modifying its topological structure without changing total dendritic length, can transform a cells firing pattern from bursting to tonic firing. Interestingly, the results are largely independent of whether the cells are stimulated by current injection at the soma or by synapses distributed over the dendritic tree. By means of a novel measure called mean electrotonic path length, we show that the influence of dendritic morphology on burst firing is attributable to the effect both dendritic size and dendritic topology have, not on somatic input conductance, but on the average spatial extent of the dendritic tree and the spatiotemporal dynamics of the dendritic membrane potential. Our results suggest that alterations in size or topology of pyramidal cell morphology, such as observed in Alzheimers disease, mental retardation, epilepsy, and chronic stress, could change neuronal burst firing and thus ultimately affect information processing and cognition.