The reconstitution of visual cortical feature selectivity &lt;i&gt;in vitro&lt;/i&gt;

Schottdorf, Manuel

doi:10.53846/goediss-6721

Cited by 4 publications

(4 citation statements)

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“…In addition, the training and experimental data created will require efficient frameworks for storage and analyses. This could include a combination of in vivo proto-neural networks developed in brain organoids and in silico analog/digital hybrid, and/or neuromorphic computing (41,90,141,142). Transmedia progressive learning will therefore exhibit the advantages of both biological and machine computing and learning, while mitigating the limitations of each.…”

Section: Big-data Infrastructuresmentioning

confidence: 99%

Organoid intelligence (OI): the new frontier in biocomputing and intelligence-in-a-dish

et al. 2023

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Recent advances in human stem cell-derived brain organoids promise to replicate critical molecular and cellular aspects of learning and memory and possibly aspects of cognition in vitro. Coining the term “organoid intelligence” (OI) to encompass these developments, we present a collaborative program to implement the vision of a multidisciplinary field of OI. This aims to establish OI as a form of genuine biological computing that harnesses brain organoids using scientific and bioengineering advances in an ethically responsible manner. Standardized, 3D, myelinated brain organoids can now be produced with high cell density and enriched levels of glial cells and gene expression critical for learning. Integrated microfluidic perfusion systems can support scalable and durable culturing, and spatiotemporal chemical signaling. Novel 3D microelectrode arrays permit high-resolution spatiotemporal electrophysiological signaling and recording to explore the capacity of brain organoids to recapitulate the molecular mechanisms of learning and memory formation and, ultimately, their computational potential. Technologies that could enable novel biocomputing models via stimulus-response training and organoid-computer interfaces are in development. We envisage complex, networked interfaces whereby brain organoids are connected with real-world sensors and output devices, and ultimately with each other and with sensory organ organoids (e.g. retinal organoids), and are trained using biofeedback, big-data warehousing, and machine learning methods. In parallel, we emphasize an embedded ethics approach to analyze the ethical aspects raised by OI research in an iterative, collaborative manner involving all relevant stakeholders. The many possible applications of this research urge the strategic development of OI as a scientific discipline. We anticipate OI-based biocomputing systems to allow faster decision-making, continuous learning during tasks, and greater energy and data efficiency. Furthermore, the development of “intelligence-in-a-dish” could help elucidate the pathophysiology of devastating developmental and degenerative diseases (such as dementia), potentially aiding the identification of novel therapeutic approaches to address major global unmet needs.

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Section: Big-data Infrastructuresmentioning

confidence: 99%

Organoid intelligence (OI): the new frontier in biocomputing and intelligence-in-a-dish

et al. 2023

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“…One discrepancy was already pointed out and concerns the power-spectral density width that is much thinner for stable model solutions than in experimentally observed orientation domains [409]. Another perhaps related discrepancy is the invariance of the average pinwheel density to phase shuffling of closed-form model solutions in contrast to an increased pinwheel density in experimentally observed orientation domains [22,411]. The dependence of the pinwheel density on phase shuffling is a clear deviation to orientation domains that are modeled by Gaussian random fields.…”

Section: Finite Power-spectral Density Widthmentioning

confidence: 97%

Genetic determination and layout rules of visual cortical architecture

Liedtke¹

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“…According to this perspective, when (super-) critical systems such as developing cultures are exposed to the right input, they should deviate from their characteristic bursting behavior. The idea to stimulate cultured neurons to control their emergent dynamics is not new [168][169][170][171]. However, in Chapter 4 we provided the input through precise optogenetic stimulation, which enabled us to asynchronously target individual neurons with relative ease compared to electrical methods [172].…”

Section: A Unified Picture Of Neuronal Avalanchesmentioning

confidence: 99%

“…This idea invites careful speculation about tentative long-term stimulation patterns: For example, when assuming a Hebbian learning rule, an asynchronous pattern would foster long-range projections, whereas a stimulation with a wave-like pattern (mimicking network bursts or retinal waves) would foster local connections. Clearly, the required experimental setup needs to permit long-term (optogenetic) stimulation with simultaneous activity recordings, which poses tremendous engineering challenges [171].…”

Section: Next Stepsmentioning

confidence: 99%

Spreading Processes in Complex Networks of Cultured Neurons and Society

Spitzner

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As I am getting closer to the finishing line of the Ph.D., I find myself sampling emotion-space with a bigger step size than before. On the one hand, I feel sentimental to see this chapter coming to an end, and about having to part ways with many people who I have grown very fond of. On the other hand, I am excited about the things to come, and I am happy about the subtle reminder to stop every now and then, catch a breath, and appreciate all the good things that came my way. Looking back, they were more than anyone could ask for. And almost always they came with kind people attached to them: people who gave me a chance, who believed in me, who supported me -all without asking for anything in return. The next few paragraphs, and the implied hug (imagine me with a wide smile), are for all those people. Thank you! I mentioned chances. One incredible and crucial chance was given to me by Viola.Without her, and without that chance some five years ago, I would not be here. Thank you Viola, for inviting me to become part of your group, for providing patient guidance, for indulging my often naive point of view, and for teaching me to focus on the things that matter. It has been my honest pleasure to work with you.Viola has a knack for gathering amazing people around her -which becomes clear instantaneously when you meet her group. I fondly remember my first day, when I met a student who proficiently explained the project I would work on. I judged him to be a senior Ph.D.; Jonas was indeed close to graduating, and handed in his Master's thesis soon after.Over the years to come, we would share a multitude of projects, and dinners, every one of which was a treat. Thank you, Jonas, for always being around, for being my trusted coauthor on several papers, and for helping out every time I got stuck. Then there is of course João, who I want to thank for discussions on a nerd-level I can have with no one else, and in particular, for standing with me through the longest review process of all. Thanks to Jens, who taught me that all (well most) scientific problems are solvable, and to Annika of the timescales team, for spotting oh-so-many bugs in my code and providing the kindest of feedback. I also want to thank Lucas and Fabian, my next-door theoreticians, for countless discussions, frequent advice, and the occasional rant, all of which I enjoyed deeply. Thanks to Sebastian who joined me on various coding sessions that well exceeded standard working hours, and to Seba, with his overwhelmingly positive attitude. I know no other person who radiates good vibes like you do, and you have been a great inspiration for me.From all my colleagues, I want to especially thank Johannes. I owe you a huge chunk of gratitude, for help in more ways than I can count. Over the past decade (!) you have been a mentor, counselor, and inspiration, and you have become one of my dearest friends. This thesis would not have been possible without your relentless support. Even if I turn my back on academia in the end, I hope for any excuse to work (or to pr...

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The reconstitution of visual cortical feature selectivity <i>in vitro</i>

Cited by 4 publications

References 424 publications

Organoid intelligence (OI): the new frontier in biocomputing and intelligence-in-a-dish

Organoid intelligence (OI): the new frontier in biocomputing and intelligence-in-a-dish

Genetic determination and layout rules of visual cortical architecture

Spreading Processes in Complex Networks of Cultured Neurons and Society

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