Adaptive flight control with living neuronal networks on microelectrode arrays

DeMarse, Thomas B.; Dockendorf, Karl

doi:10.1109/ijcnn.2005.1556108

Cited by 38 publications

(22 citation statements)

References 14 publications

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“…Looking to neuroscience for examples of how neural networks are trained can inform new strategies to train non‐neural cellular networks for desired GJC. A number of neuronal plasticity studies grow neurons on microelectrode arrays (MEAs) that can then be used to monitor the patterns of firing neurons and to also deliver electrical impulses that can stimulate the firing of certain neurons in the network (DeMarse and Dockendorf, ; le Feber et al, ; Franke et al, ; Pimashkin et al, ). Cardiomyocytes, pancreatic islet cells, and fibroblasts can all be monitored for electrical activity using MEAs (Parak et al, ; Bornat et al, ; Pfeiffer et al, ; Spira and Hai, ; Schönecker et al, ).…”

Section: Resultsmentioning

confidence: 99%

Gap junctional signaling in pattern regulation: Physiological network connectivity instructs growth and form

Mathews

Levin

2016

Developmental Neurobiology

121

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Gap junctions (GJs) are aqueous channels that allow cells to communicate via physiological signals directly. The role of gap junctional connectivity in determining single-cell functions has long been recognized. However, GJs have another important role: the regulation of large-scale anatomical pattern. GJs are not only versatile computational elements that allow cells to control which small molecule signals they receive and emit, but also establish connectivity patterns within large groups of cells. By dynamically regulating the topology of bioelectric networks in vivo, GJs underlie the ability of many tissues to implement complex morphogenesis. Here, a review of recent data on patterning roles of GJs in growth of the zebrafish fin, the establishment of left-right patterning, the developmental dysregulation known as cancer, and the control of large-scale head-tail polarity, and head shape in planarian regeneration has been reported. A perspective in which GJs are not only molecular features functioning in single cells, but also enable global neural-like dynamics in non-neural somatic tissues has been proposed. This view suggests a rich program of future work which capitalizes on the rapid advances in the biophysics of GJs to exploit GJ-mediated global dynamics for applications in birth defects, regenerative medicine, and morphogenetic bioengineering. © 2016 Wiley Periodicals, Inc. Develop Neurobiol 77: 643-673, 2017.

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Section: Resultsmentioning

confidence: 99%

Gap junctional signaling in pattern regulation: Physiological network connectivity instructs growth and form

Mathews

Levin

2016

Developmental Neurobiology

121

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“…Specifically: (1) the development of improved methods for reading/writing bioelectrical state information into somatic tissues and sculpting non-neural bioelectric circuits (advances in optogenetics beyond excitable cells and in the synthetic biology of gap junction and neurotransmitter signaling) 32d , (2) continued work on cracking the bioelectric code (understanding how bioelectric state information maps onto the topology of various patterning outcomes in tractable model systems such as planaria) 16a , (3) formulation and testing of quantitative, molecular models of LTP, habituation, sensitization, and synaptic plasticity applied to slow bioelectric signaling in non-neural cell groups regulating regenerative growth 96b , (4) use of reagents that impact cognition (hallucinogens, anesthetics 131 , stimulants, nootropics/cognitive enhancers, etc.) in developmental and regenerative patterning assays to probe conservation of pathways between neuroscience and morphogenesis, (5) in silico study and synthetic implementation of biophysics models of circuits which can stably store bioelectric state information as attractor states of ion channel activity in arbitrary cell types 132 , (6) creation of larger-scale computational models of regeneration and functional experiments in morphogenesis based on goal-seeking and error minimization algorithms with molecularly-specified metrics 133 , (7) exploration of molecular models of cognitive concepts (attention, autism spectrum, sleep, visual illusions/hallucinations, addiction) in specific patterning and mispatterning contexts, (8) experimental examination of learning and complex behavior 134 in non-neural in vitro constructs to understand the cognitive powers of non-excitable cell networks 135 , (9) bioengineering platforms that reward and punish in vitro patterning systems for specific changes in growth and morphogenesis (seeking to demonstrate instrumental learning and top-down control of shape in developmental or regenerative contexts), and (10) a mechanistic investigation of the mechanism of persistence of memories through massive brain regeneration, which is likely to reveal the interface between somatic and neural memories 136 .…”

Section: Discussionmentioning

confidence: 99%

Re-membering the body: applications of computational neuroscience to the top-down control of regeneration of limbs and other complex organs

2015

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A major goal of regenerative medicine and bioengineering is the regeneration of complex organs, such as limbs, and the capability to create artificial constructs (so-called biobots) with defined morphologies and robust self-repair capabilities. Developmental biology presents remarkable examples of systems that self-assemble and regenerate complex structures toward their correct shape despite significant perturbations. A fundamental challenge is to translate progress in molecular genetics into control of large-scale organismal anatomy, and the field is still searching for an appropriate theoretical paradigm for facilitating control of pattern homeostasis. However, computational neuroscience provides many examples in which cell networks (brains) store memories of geometrical states and coordinate their activity towards proximal and distant goals. In this Perspective, we propose that programming large-scale morphogenesis requires exploiting the information processing by which cellular structures work toward specific shapes. In non-neural cells, as in the brain, bioelectric signaling implements information processing, decision-making, and memory in regulating pattern and its remodeling. Thus, approaches used in computational neuroscience to understand goal-seeking neural systems offer a toolbox of techniques to model and control regenerative pattern formation. Here, we review recent data on developmental bioelectricity as a regulator of patterning, and propose that target morphology could be encoded within tissues as a kind of memory, using the same molecular mechanisms and algorithms so successfully exploited by the brain. We highlight the next steps of an unconventional research program, which may allow top-down control of growth and form for numerous applications in regenerative medicine and synthetic bioengineering.

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“…showed that elementary pattern recognition and signal processing functions could be impressed on a cultured network [87]. Marom’s group showed differential learning of rare and frequent stimuli [88], which formed the basis for DeMarse’s demonstration of control of a simulated device [89]. Feedback control of network behavior has been demonstrated [90].…”

Section: Coding and Plasticitymentioning

confidence: 99%

Designing Neural Networks in Culture

2010

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Technology has advanced to where it is possible to design and grow—with predefined geometry and surprisingly good fidelity—living networks of neurons in culture dishes. Here we overview the elements of design, emphasizing the lithographic techniques that alter the cell culture surface which in turn influences the attachment and growth of the neural networks. Advanced capability in this area makes it possible to design networks of desired complexity. Other issues addressed include the influence of glial cells and media on activity and the potential for extending the designs into three dimensions. Investigators are advancing the art and science of analyzing and controlling through stimulation the function of the neural networks, including the ability to take advantage of their geometric form in order to influence functional properties.

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Adaptive flight control with living neuronal networks on microelectrode arrays

Cited by 38 publications

References 14 publications

Gap junctional signaling in pattern regulation: Physiological network connectivity instructs growth and form

Gap junctional signaling in pattern regulation: Physiological network connectivity instructs growth and form

Re-membering the body: applications of computational neuroscience to the top-down control of regeneration of limbs and other complex organs

Designing Neural Networks in Culture

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