B1: Activity dynamics in defined neuronal networks on micro-patterned substrates

Ulrich Egert^P, Ad Aertsen^A and Jürgen Rühe^F

^A = Neurobiology and Biophysics;
^F = Inst. Microsystems Technology, Chemistry & Physics of Interfaces
^P = Biomicrotechnology

Scientific background

Computational neuronal network models (CNN), in which spike activity dynamics is studied in large-scale CNNs with up to 105 neurons in our lab, reduce the complexity of the system studied, identifying and omitting presumably irrelevant detail. Given the complexity of neuronal tissue, it is currently impossible to verify this assumption of irrelevance, provoking questions on the accuracy of these models.
Spike activity in cultures of cortical cells (BNN) has been monitored with substrate-integrated microelectrode arrays (MEAs) for weeks or months. The connectivity between small groups of neurons in these cultures will be determined by multiple simultaneous intracellular recordings. Patterns of adhesion and growth promoting molecules will be used to guide neurite outgrowth, and the networks cellular composition and spatial structure will be characterized immunohistochemically.

Objectives

Our central goal is to understand how the composition, connectivity statistics and plasticity in a neural network determine the dynamics of electrical activity and the computational properties associated with it. To identify network and cellular properties determining the activity dynamics of neuronal circuits we will compare the activity of matched BNNs and CNNs. We will test predictions derived from CNNs on the effect of changes to the connectivity statistics in BNNs.