Simulating electrode arrangements on microelectrode arrays
Tutkimustuotos: Konferenssiesitys, posteri tai abstrakti ›
|DOI - pysyväislinkit|
|Tila||Julkaistu - 18 heinäkuuta 2015|
We simulated the network using the INEX model , which consists of spontaneously active excitatory and inhibitory neurons. 1005 neurons were positioned in a grid inside a circle with a 1mm radius and connected to ~100 nearest neighbors. Different subsets of neurons were chosen for analysis (see Figure 1) modelling various MEA ensembles: every 1-10th neuron (panels A-J), the outer- and inner most neurons (K-L), and different sized grid formations: 3x3=9 electrodes (M-V), 8x8=64 electrodes (W-Y) and 16x16=256 electrodes (Z). Thus panel A represents the entire network. We calculated the spike and burst rates for the selected neurons, and compared these between the different sets of recorded neurons. The bursts were detected using the CMA algorithm .
The spiking and bursting rates of neurons in different arrangements are shown in the Figure 1. In these simulations the neurons on the edges spike and burst less than the neurons in the middle (compare panels K and L), due to different neighborhoods. This resembles biological networks, where parts of the network can be more active than other. Typically, a lower number of recorded neurons results in low variability of spike rates (e.g., panels A-J), which in some cases results in erroneous median values (e.g., panel G) compared to panel A showing the activity of the whole network. Also when the recorded neurons cover the entire area of network, the recorded neurons represent better the behavior of the network, thus even low number of electrodes provide (3x3 grid (M-V)) sufficient results.
This research has been supported by the 3DNeuroN project in the European Union's Seventh Framework Programme, Future and Emerging Technologies, grant agreement n°296590.
1. Lenk K: A simple phenomenological neuronal model with inhibitory and excitatory synapses. In Advances in Nonlinear Speech Processing. 2011:232–238.
2. Kapucu FE, Tanskanen JMA, Mikkonen JE, Ylä-Outinen L, Narkilahti S, Hyttinen JAK: Burst analysis tool for developing neuronal networks exhibiting highly varying action potential dynamics. Front Comput Neurosci 2012, 6.