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Computational neuroscience

photo Arjen van Ooyen (Adriaan van Ooijen)

Arjen van Ooyen
(Adriaan van Ooijen)

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Computational neuroscience

The interdisciplinary field of computational neuroscience combines neuroscience with disciplines such as computer science, mathematics, and physics to advance and deepen our understanding of the nervous system. In particular, computational neuroscience is concerned with modeling neural systems and analyzing complex experimental data. We operate in this field along the following research lines: (1) neurite outgrowth and formation of synaptic connectivity and (2) impact of neuronal morphology and synaptic connectivity on activity dynamics.

Neurite outgrowth and formation of synaptic connectivity

Cognitive function strongly depends on the organization of synaptic connectivity in cortical networks. Synaptic connectivity determines how information is transmitted and what spatiotemporal patterns of electrical activity can arise.

During development, neurons form synapses when their growing axonal and dendritic arbors come into close proximity of each other. Strikingly, neurons retain their capacity for growth and synapse formation (structural plasticity) in the adult brain. Dendritic spines and axonal boutons, the pre- and postsynaptic parts of synapses, appear and disappear frequently, accompanied by synapse formation and elimination. Lasting alterations in afferent activity to the cortex, such as those caused by retinal lesions, trigger extensive spine dynamics, leading to massive structural adaptations in cortical synaptic connectivity.

The principles governing structural plasticity are, however, poorly understood. Our central hypothesis is that the attempt of neurons to maintain their average electrical activity at a particular level (homeostatic regulation) is a main organizing principle of neuronal networks. We conjecture that homeostatic regulation not only guides the formation of networks but also drives the compensatory structural changes following loss of input caused by lesions, stroke, and neurodegeneration.

To explore the potential implications of homeostatic structural plasticity for the development and reorganization of neuronal networks, we use network models in which each neuron creates new spines and boutons when its level of electrical activity is below a homeostatic set-point and deletes spines and boutons when its activity is above the set-point. Synapses are formed by merging spines and boutons.

Understanding the principles of structural plasticity may inspire novel treatments for stimulating functional repair after brain damage and neurodegeneration.

To read more, see neurite outgrowth and network formation.

Impact of neuronal morphology and synaptic connectivity on activity dynamics

Electrical activity modulates the development of neuronal morphology and synaptic connectivity, but morphology and connectivity in turn also influence neuronal activity and network dynamics.

Using detailed neuron models, we investigate how the morphology of dendritic trees influences neuronal firing and synapse strengths.

Using network models, we examine how the pattern of synaptic connections within and between networks affects learning and the generation of neuronal oscillations. Oscillations arise from reciprocal interactions between excitatory and inhibitory neurons and play a critical role in cognitive functions such as attention, memory, and learning.

To read more, see firing patterns, synapse strengths, neuronal oscillations, and learning and memory.


Publications are listed under the various research topics as well as in my publications page. The full text of most papers is available in PDF format (please read copyright notice). Publications can also be downloaded from my profiles at ResearchGate and For citations to my work, see my Google Scholar profile.


A new book chapter (Van Ooyen and Butz-Ostendorf, 2019) reviews model studies that show that activity-dependent neurite outgrowth, a form of homeostatic structural plasticity, can build critical networks.

Our model of homeostatic structural plasticity (Butz and Van Ooyen, 2013; Butz et al., 2014) has been implemented in NEST (see also Diaz-Pier et al., 2016).

Listed below are three key publications on modeling neural development and structural plasticity:

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Last modified: 15 August 2019