9:16 AM - *SM03.01.07
CMOS Microelectronic Systems to Characterize Neurons and Networks at Subcellular Resolution
Andreas Hierlemann1
ETH Zürich1
Show Abstract
Microelectrode arrays (MEAs) have been widely used in recent years for in-vitro investigation of neuronal cells and neural networks, as they enable long-term bi-directional interfacing with networks of living cells [1, 2]. Modern CMOS-based active MEA devices, especially high-density MEAs, provide the capability to simultaneously perform electrophysiological recordings from thousands of electrodes at cellular/subcellular spatial resolution [3-6]. The high spatio-temporal resolution enables better separation and assignment of neural activities for closely spaced neurons as well as localized and specific stimulation of single individual neurons [7].
An exemplary CMOS high-density microelectrode system includes a large sensing area of 4.48 × 2.43 mm2 comprising 59’760 (332 × 180) electrodes of 3 × 7.5 µm2 size at a pitch of 13.5 µm [6]. The system incorporates several types of sensing units with the aim of extracting a wide spectrum of information from the same neuronal culture or brain slice: 16 dual-mode stimulation buffers, 2048 action-potential recording channels, 32 local-field-potential recording channels, 32 current recording units, 32 impedance measurement units and 28 neurotransmitter detection units [6].
By using such a system it was possible to record subcellular-resolution data in various preparations, ranging from organotypic and acute slices to cultures of dissociated neurons and stem-cell derived neurons. It was also possible to detect low-amplitude signals of action potentials traveling along thin axons (~100 nm diameter). Moreover, the stimulation features of CMOS microtransducer arrays and integrated microsystems offer the capability to bi-directionally interact, also in closed loop and real time, with potentially every single neuron in a given neuronal network. Applications include research in neural diseases and pharmacology.
Acknowledgements
The work was supported by the European Community through the ERC Advanced Grant “neuroXscales” under contract number AdG 694829.
References
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