Modular microstructure design to build neuronal networks of defined functional connectivity

Forró, Csaba; Thompson-Steckel, Greta; Weaver, Sean; Weydert, Serge; Ihle, Stephan J.; Dermutz, Harald; Aebersold, Mathias J.; Pilz, Raphael; Demkó, László; Vörös, János

doi:10.1016/j.bios.2018.08.075

Cited by 76 publications

(134 citation statements)

References 93 publications

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“…During the last decade, new methods of neuroengineering have been developed to control the position of cells and direction of axon and dendrite growth (le Feber et al, 2015;Na et al, 2016;Renault et al, 2016). Recently, it has been shown that the main feature of functional network topology as unidirectional synaptic connectivity between cell clusters can also be engineered using microfluidic technology (Gladkov et al, 2017;Poli et al, 2017;Forró et al, 2018). Being implanted in the damaged brain, such tools of network structure manipulation allow one to mimic brain areas, which are involved in reflex activity, pattern retrieval in multilayered unidirectional network (Brewer et al, 2013;Poli et al, 2017) for neural tissue recovery from brain injury (Shimba et al, 2019).…”

Section: Living Neural Network: Biological Side Of Neural Integrationmentioning

confidence: 99%

Neurohybrid Memristive CMOS-Integrated Systems for Biosensors and Neuroprosthetics

et al. 2020

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Here we provide a perspective concept of neurohybrid memristive chip based on the combination of living neural networks cultivated in microfluidic/microelectrode system, metal-oxide memristive devices or arrays integrated with mixed-signal CMOS layer to control the analog memristive circuits, process the decoded information, and arrange a feedback stimulation of biological culture as parts of a bidirectional neurointerface. Our main focus is on the state-of-the-art approaches for cultivation and spatial ordering of the network of dissociated hippocampal neuron cells, fabrication of a large-scale crossbar array of memristive devices tailored using device engineering, resistive state programming, or non-linear dynamics, as well as hardware implementation of spiking neural networks (SNNs) based on the arrays of memristive devices and integrated CMOS electronics. The concept represents an example of a brain-on-chip system belonging to a more general class of memristive neurohybrid systems for a newgeneration robotics, artificial intelligence, and personalized medicine, discussed in the framework of the proposed roadmap for the next decade period.

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Section: Living Neural Network: Biological Side Of Neural Integrationmentioning

confidence: 99%

Neurohybrid Memristive CMOS-Integrated Systems for Biosensors and Neuroprosthetics

et al. 2020

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“…Microfluidic devices have been used for drug screening applications and in-depth study of cell behaviour in varying disease or injured states with high throughput capability [74]. Meanwhile embedded MEAs can facilitate the study of neural network dynamics, signalling behaviour and fluid perfusion [73,75,76,77,78]. A few examples are mentioned here, and Osaki et al (2018) provides a detailed discussion of microfluidic disease modelling of the CNS [79].…”

Section: In Vitro Neural Modelsmentioning

confidence: 99%

Layer-By-Layer: The Case for 3D Bioprinting Neurons to Create Patient-Specific Epilepsy Models

2019

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The ability to create three-dimensional (3D) models of brain tissue from patient-derived cells, would open new possibilities in studying the neuropathology of disorders such as epilepsy and schizophrenia. While organoid culture has provided impressive examples of patient-specific models, the generation of organised 3D structures remains a challenge. 3D bioprinting is a rapidly developing technology where living cells, encapsulated in suitable bioink matrices, are printed to form 3D structures. 3D bioprinting may provide the capability to organise neuronal populations in 3D, through layer-by-layer deposition, and thereby recapitulate the complexity of neural tissue. However, printing neuron cells raises particular challenges since the biomaterial environment must be of appropriate softness to allow for the neurite extension, properties which are anathema to building self-supporting 3D structures. Here, we review the topic of 3D bioprinting of neurons, including critical discussions of hardware and bio-ink formulation requirements.

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“…However, it is difficult to detect and characterize AP propagation (e.g., direction and velocity) in neuronal cultures using conventional MEAs: it is virtually impossible to ensure that electrodes are well placed for AP detection, source–target information is inaccessible, and the amplitudes of the APs recorded from axons are typically very low and impossible to discriminate. To improve the signal-to-noise ratio (SNR) of electrophysiological recordings and the localization of neuronal processes on recording electrodes, devices combining microElectrodes and microFluidics (µEF devices) have been developed for neuroscience applications 3–6 . A µEF device is composed of a microfluidic device mounted on a MEA to form an enclosed culture chamber composed of two (or more) isolated compartments connected by microchannels.…”

Section: Introductionmentioning

confidence: 99%

“…A number of studies have been conducted using µEF devices to assess (i) the directionality of communication and the origin of bursting behavior in networks of distinct populations of neurons 3,6,8–12 ; (ii) changes to the propagation velocity with culture age 13,14 ; and (iii) the effects of pharmacological, biochemical, or electrical stimulation 13,15,16 .…”

Section: Introductionmentioning

confidence: 99%

µSpikeHunter: An advanced computational tool for the analysis of neuronal communication and action potential propagation in microfluidic platforms

Heiney

Mateus

Lopes

et al. 2019

Sci Rep

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Understanding neuronal communication is fundamental in neuroscience, but there are few methodologies offering detailed analysis for well-controlled conditions. By interfacing microElectrode arrays with microFluidics (μEF devices), it is possible to compartmentalize neuronal cultures with a specified alignment of axons and microelectrodes. This setup allows the extracellular recording of spike propagation with a high signal-to-noise ratio over the course of several weeks. Addressing these μEF devices, we developed an advanced yet easy-to-use publically available computational tool, μSpikeHunter, which provides a detailed quantification of several communication-related properties such as propagation velocity, conduction failure, spike timings, and coding mechanisms. The combination of μEF devices and μSpikeHunter can be used in the context of standard neuronal cultures or with co-culture configurations where, for example, communication between sensory neurons and other cell types is monitored and assessed. The ability to analyze axonal signals (in a user-friendly, time-efficient, high-throughput manner) opens the door to new approaches in studies of peripheral innervation, neural coding, and neuroregeneration, among many others. We demonstrate the use of μSpikeHunter in dorsal root ganglion neurons where we analyze the presence of both anterograde and retrograde signals in μEF devices. A fully functional version of µSpikeHunter is publically available for download from https://github.com/uSpikeHunter .

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Modular microstructure design to build neuronal networks of defined functional connectivity

Cited by 76 publications

References 93 publications

Neurohybrid Memristive CMOS-Integrated Systems for Biosensors and Neuroprosthetics

Neurohybrid Memristive CMOS-Integrated Systems for Biosensors and Neuroprosthetics

Layer-By-Layer: The Case for 3D Bioprinting Neurons to Create Patient-Specific Epilepsy Models

µSpikeHunter: An advanced computational tool for the analysis of neuronal communication and action potential propagation in microfluidic platforms

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