Conceptual and technical advances define a key moment for theoretical neuroscience

Churchland, Anne K.; Abbott, L. F.

doi:10.1038/nn.4255

Cited by 36 publications

(29 citation statements)

References 18 publications

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“…Unfortunately our understanding of how brains work is currently very limited, although the prospect of eventual theoretical advances is promising (Churchland and Abbot, 2016). None-the-less there are a number of generalizations that can be made that have implications for the study of human mental abilities.…”

Section: Discussionmentioning

confidence: 99%

How neuroscience can inform the study of individual differences in cognitive abilities

McFarland

2017

Reviews in the Neurosciences

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AbstractTheories of human mental abilities should be consistent with what is known in neuroscience. Currently, tests of human mental abilities are modeled by cognitive constructs such as attention, working memory, and speed of information processing. These constructs are in turn related to a single general ability. However, brains are very complex systems and whether most of the variability between the operations of different brains can be ascribed to a single factor is questionable. Research in neuroscience suggests that psychological processes such as perception, attention, decision, and executive control are emergent properties of interacting distributed networks. The modules that make up these networks use similar computational processes that involve multiple forms of neural plasticity, each having different time constants. Accordingly, these networks might best be characterized in terms of the information they process rather than in terms of abstract psychological processes such as working memory and executive control.

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Section: Discussionmentioning

confidence: 99%

How neuroscience can inform the study of individual differences in cognitive abilities

McFarland

2017

Reviews in the Neurosciences

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“…In addition to various experimental approaches, computational modeling is becoming an increasingly important technique because it facilitates the validation of hypotheses and theories regarding neural circuit operation through the integration of existing observations into computer models (Churchland and Abbott 2016;Chaudhuri and Fiete 2016;Denève and Machens 2016;Sporns 2013). Indeed, extensive studies on neural network models covering Caenorhabditis elegans (Szigeti et al 2014;Izquierdo and Beer 2016;Palyanov et al 2011), insects (Wessnitzer and Webb 2006), rodents, and primates (Markram 2006;Izhikevich and Edelman 2008;Eliasmith et al 2012) have greatly contributed to our understanding of neural circuit functions at the systems level.…”

Section: Introductionmentioning

confidence: 99%

A single-cell level and connectome-derived computational model of the Drosophila brain

Huang

Wang

et al. 2018

Preprint

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Computer simulations play an important role in testing hypotheses, integrating knowledge, and providing predictions of neural circuit functions. While considerable effort has been dedicated into simulating primate or rodent brains, the fruit fly (Drosophila melanogaster) is becoming a promising model animal in computational neuroscience for its small brain size, complex cognitive behavior, and abundancy of data available from genes to circuits.Moreover, several Drosophila connectome projects have generated a large number of neuronal images that account for a significant portion of the brain, making a systematic investigation of the whole brain circuit possible. Supported by FlyCircuit (http://www.flycircuit.tw), one of the largest Drosophila neuron image databases, we began a long-term project with the goal to construct a whole-brain spiking network model of the Drosophila brain. In this paper, we report the outcome of the first phase of the project. We developed the Flysim platform, which 1) identifies the polarity of each neuron arbor, 2) predicts connections between neurons, 3) translates morphology data from the database into physiology parameters for computational modeling, 4) reconstructs a brain-wide network model, which consists of 20,089 neurons and 1,044,020 synapses, and 5) performs computer simulations of the resting state. We compared the reconstructed brain network with a randomized brain network by shuffling the connections of each neuron. We found that the reconstructed brain can be easily stabilized by implementing synaptic short-term depression, while the randomized one exhibited seizure-like firing activity under the same treatment.Furthermore, the reconstructed Drosophila brain was structurally and dynamically more diverse than the randomized one and exhibited both Poisson-like and patterned firing activities. Despite being at its early stage of development, this single-cell level brain model allows us to study some of the fundamental properties of neural networks including network balance, critical behavior, long-term stability, and plasticity.All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

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“…Countless methods for measuring and manipulating neurons are now available, outputting extremely large data sets that require increasingly complex analysis techniques. 1 Signs of progress can be seen not only in experimental data analyses but also in theoretical approaches to understand and solve long-standing puzzles. It is true that the development and consequent experimentation of theoretical models have been in course of several years, but never before has the need for theoretical approaches been so high nor the prospects for advancement so great.…”

Section: Boldmentioning

confidence: 99%

“…It is true that the development and consequent experimentation of theoretical models have been in course of several years, but never before has the need for theoretical approaches been so high nor the prospects for advancement so great. 1 Theoretical and computational neuroscience has been especially used for studies of the hippocampus, a structure that appears to be crucial to the formation of long-term memories in our brain. 2 More robust experiments are being used in search of more comprehensive data both in vitro and in vivo, 3 whilst models and simulations are slowly becoming able to reproduce important phenomena of this structure, such as the sharp-wave ripple (SPW-R) events.…”

Section: Boldmentioning

confidence: 99%

“…Throughout this text, the understanding of many basic concepts of neuroscience will be necessary. Table (1) and figure (1) describe the terms and concepts most used in this text at a superficial level to the unfamiliarized reader. Though this level of description should be sufficient to understand this text, we highly suggest the following references for more in-depth descriptions: KANDEL et al 8 for a biological approach and DAYAN and ABBOTT 9 for a more theoretical approach.…”

Section: Neuroscience: Basic Concepts and Terminologymentioning

confidence: 99%

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Structural, functional and dynamical properties of a lognormal network of bursting neurons

Carvalho¹

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First and foremost, I'd like to thank Dr. Kenji Mizuseki, Dr. Kamran Diba and Dr. György Buzsáki for personally providing additional recordings of the hc3 database and allowing me to use their data to pursue the avalanche studies presented in this dissertation. Especially, I'd like to thank Dr. Kenji Mizuseki for taking his time to help me before and after I received the data hard drive he so benevolently sent me through mail. An entire chapter of this dissertation wouldn't be without their help. Still in this context, I shouldn't forget to thank Dr. Tomoki Fukai and Yoshiyuki Omura for allowing me to continue their studies of the MAT neuron network. I arrived at RIKEN in 2015 just as a 3-month trainee, but their trust in my work led me to where I am right now, and to the completition of this dissertation. It wouldn't be an overstate to say I am only writing these acknowledgements right now because of them, and I'm immensely thankful for that. Acknowledgements wouldn't be acknowledgements if I didn't thank my supervisor, my mother, close friends and not-so-close ones for their help throughout the last two (six? twenty four?) years. I have so many people to thank I am even afraid of writing names here, so I won't. Thank you all for everything. I'll just add a special thank you to my cats for being the cutest things in the world, because they are. At last, goodbye, Dinah, goodbye. Thanks for all the fish. "Though I am often in the depths of misery, there is still calmness, pure harmony and music inside me." Vicent van Gogh ABSTRACT CARVALHO, M. M. Structural, functional and dynamical properties of a lognormal network of bursting neurons. 2017. 115p. Dissertation (Master in Science)

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Conceptual and technical advances define a key moment for theoretical neuroscience

Cited by 36 publications

References 18 publications

How neuroscience can inform the study of individual differences in cognitive abilities

How neuroscience can inform the study of individual differences in cognitive abilities

A single-cell level and connectome-derived computational model of the Drosophila brain

Structural, functional and dynamical properties of a lognormal network of bursting neurons

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