scholarly journals Engineered biological neural networks on high density CMOS micro electrode arrays

2021 ◽  
Author(s):  
Jens Duru ◽  
Joel Kuechler ◽  
Stephan Johannes Ihle ◽  
Csaba Forro ◽  
Aeneas Bernardi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Network Topology ◽  
Complementary Metal Oxide Semiconductor ◽  
High Density ◽  
Oxide Semiconductor ◽  
Electrode Arrays ◽  
Spatiotemporal Resolution ◽  
Biological Neural Networks ◽  
Micro Electrode Arrays

In bottom-up neuroscience, questions on neural information processing are addressed by engineering small but reproducible biological neural networks of defined network topology \textit{in vitro}. The network topology can be controlled by culturing neurons within polydimethylsiloxane (PDMS) microstructures that are combined with microelectrode arrays (MEAs) for electric access to the network. However, currently used glass MEAs are limited to 256 electrodes and pose a limitation to the spatial resolution as well as the design of more complex microstructures. The use of high density complementary metal-oxide-semiconductor (CMOS) MEAs greatly increases the spatiotemporal resolution, enabling sub-cellular readout and stimulation of neurons in defined neural networks. Unfortunately, the non-planar surface of CMOS MEAs complicates the attachment of PDMS microstructures. To overcome the problem of axons escaping the microstructures through the ridges of the CMOS MEA, we stamp-transferred a thin film of hexane-diluted PDMS onto the array such that the PDMS filled the ridges at the contact surface of the microstructures without clogging the axon guidance channels. Moreover, we provide an impedance-based method to visualize the exact location of the microstructures on the MEA and show that our method can confine axonal growth within the PDMS microstructures. Finally, the high spatiotemporal resolution of the CMOS MEA enabled us to show that we can guide action potentials using the unidirectional topology of our circular multi-node microstructure.

scholarly journals Applicability of independent component analysis on high-density microelectrode array recordings

2012 ◽  
Vol 108 (1) ◽  
pp. 334-348 ◽  
Author(s):  
David Jäckel ◽  
Urs Frey ◽  
Michele Fiscella ◽  
Felix Franke ◽  
Andreas Hierlemann
Keyword(s):  
Microelectrode Array ◽  
Simulated Data ◽  
Complementary Metal Oxide Semiconductor ◽  
Simultaneous Recording ◽  
High Density ◽  
Independent Component ◽  
Oxide Semiconductor ◽  
Spike Sorting ◽  
Spatiotemporal Resolution ◽  
Source Signals

Emerging complementary metal oxide semiconductor (CMOS)-based, high-density microelectrode array (HD-MEA) devices provide high spatial resolution at subcellular level and a large number of readout channels. These devices allow for simultaneous recording of extracellular activity of a large number of neurons with every neuron being detected by multiple electrodes. To analyze the recorded signals, spiking events have to be assigned to individual neurons, a process referred to as “spike sorting.” For a set of observed signals, which constitute a linear mixture of a set of source signals, independent component (IC) analysis (ICA) can be used to demix blindly the data and extract the individual source signals. This technique offers great potential to alleviate the problem of spike sorting in HD-MEA recordings, as it represents an unsupervised method to separate the neuronal sources. The separated sources or ICs then constitute estimates of single-neuron signals, and threshold detection on the ICs yields the sorted spike times. However, it is unknown to what extent extracellular neuronal recordings meet the requirements of ICA. In this paper, we evaluate the applicability of ICA to spike sorting of HD-MEA recordings. The analysis of extracellular neuronal signals, recorded at high spatiotemporal resolution, reveals that the recorded data cannot be modeled as a purely linear mixture. As a consequence, ICA fails to separate completely the neuronal signals and cannot be used as a stand-alone method for spike sorting in HD-MEA recordings. We assessed the demixing performance of ICA using simulated data sets and found that the performance strongly depends on neuronal density and spike amplitude. Furthermore, we show how postprocessing techniques can be used to overcome the most severe limitations of ICA. In combination with these postprocessing techniques, ICA represents a viable method to facilitate rapid spike sorting of multidimensional neuronal recordings.

Multiscale functional connectivity estimation on low-density neuronal cultures recorded by high-density CMOS Micro Electrode Arrays

2012 ◽  
Vol 207 (2) ◽  
pp. 161-171 ◽  
Author(s):  
Alessandro Maccione ◽  
Matteo Garofalo ◽  
Thierry Nieus ◽  
Mariateresa Tedesco ◽  
Luca Berdondini ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
High Density ◽  
Neuronal Cultures ◽  
Low Density ◽  
Electrode Arrays ◽  
Micro Electrode Arrays

scholarly journals Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations

Materials ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 2745 ◽  
Author(s):  
Luis Camuñas-Mesa ◽  
Bernabé Linares-Barranco ◽  
Teresa Serrano-Gotarredona
Keyword(s):  
Neural Networks ◽  
Learning Strategies ◽  
Large Scale ◽  
Sensory Information ◽  
Complementary Metal Oxide Semiconductor ◽  
Cmos Technology ◽  
Oxide Semiconductor ◽  
Hardware Implementations ◽  
Learning Capabilities ◽  
Promising Solution

Inspired by biology, neuromorphic systems have been trying to emulate the human brain for decades, taking advantage of its massive parallelism and sparse information coding. Recently, several large-scale hardware projects have demonstrated the outstanding capabilities of this paradigm for applications related to sensory information processing. These systems allow for the implementation of massive neural networks with millions of neurons and billions of synapses. However, the realization of learning strategies in these systems consumes an important proportion of resources in terms of area and power. The recent development of nanoscale memristors that can be integrated with Complementary Metal–Oxide–Semiconductor (CMOS) technology opens a very promising solution to emulate the behavior of biological synapses. Therefore, hybrid memristor-CMOS approaches have been proposed to implement large-scale neural networks with learning capabilities, offering a scalable and lower-cost alternative to existing CMOS systems.

scholarly journals A modular high-density 294-channel μECoG system on macaque vlPFC for auditory cognitive decoding

2019 ◽  
Author(s):  
Chia-Han Chiang ◽  
Jaejin Lee ◽  
Charles Wang ◽  
Ashley J. Williams ◽  
Timothy H. Lucas ◽  
...  
Keyword(s):  
High Resolution ◽  
Auditory Perception ◽  
Large Scale ◽  
Electrode Array ◽  
Auditory Pathway ◽  
Sound Level ◽  
High Density ◽  
Electrode Arrays ◽  
Spatiotemporal Resolution

AbstractOBJECTIVEA fundamental goal of the auditory system is to parse the auditory environment into distinct perceptual representations. Auditory perception is mediated by the ventral auditory pathway, which includes the ventrolateral prefrontal cortex (vlPFC) late. Because large-scale recordings of auditory signals are quite rare, the spatiotemporal resolution of the neuronal code that underlies vlPFC’s contribution to auditory perception has not been fully elucidated. Therefore, we developed a modular, chronic, high-resolution, multi-electrode array system with long-term viability.APPROACHWe molded three separate μECoG arrays into one and implanted this system in a non-human primate. A custom 3D-printed titanium chamber was mounted on left hemisphere. The molded 294-contact μECoG array was implanted subdurally over vlPFC. μECoG activity was recorded while the monkey participated in a “hearing-in-noise” task in which they reported hearing a “target” vocalization from a background “chorus” of vocalizations. We titrated task difficulty by varying the sound level of the target vocalization, relative to the chorus (target-to-chorus ratio, TCr).MAIN RESULTSWe decoded the TCr and the monkey’s behavioral choices from the μECoG signal. We analyzed decoding capacity as a function of neuronal frequency band, spatial resolution, and time from implantation. Over a one-year period, we were successfully able to record μECoG signals. Although we found significant decoding with as few as two electrodes, we found near-perfect decoding with ∼16 electrodes. Decoding further improved when we included more electrodes. Finally, because the decoding capacity of individual electrodes varied on a day-by-day basis, high-density electrode arrays ensure robust decoding in the long term.SIGNIFICANCEOur results demonstrate the utility and robustness of high-resolution chronic µECoG recording. We developed a new high-resolution surface electrode array that can be scaled to cover larger cortical areas without increasing the chamber footprint.

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