A Biological Imitation Game

Koch, Christof; Buice, Michael A.

doi:10.1016/j.cell.2015.09.045

Cited by 9 publications

(9 citation statements)

References 20 publications

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“…The model by Potjans and Diesmann ( 2014 ) with 80,000 point neurons and 0.3 billion synapses integrated a large body of cell type and connectivity data and reproduced many dynamical properties of cortical microcircuits. More recently, cellular and synaptic organization principles derived from experimental data were used to build what has been labeled as “the most complete simulation of a piece of excitable brain matter to date” (Koch and Buice, 2015 ; Markram et al, 2015 ). Cell models were classified into 207 types with distinct electro-physiological and full 3D morphological reconstructions, derived from recording and labeling over 14,000 neurons.…”

Section: Discussionmentioning

confidence: 99%

Restoring Behavior via Inverse Neurocontroller in a Lesioned Cortical Spiking Model Driving a Virtual Arm

Durá-Bernal

Neymotin

et al. 2016

Front. Neurosci.

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Neural stimulation can be used as a tool to elicit natural sensations or behaviors by modulating neural activity. This can be potentially used to mitigate the damage of brain lesions or neural disorders. However, in order to obtain the optimal stimulation sequences, it is necessary to develop neural control methods, for example by constructing an inverse model of the target system. For real brains, this can be very challenging, and often unfeasible, as it requires repeatedly stimulating the neural system to obtain enough probing data, and depends on an unwarranted assumption of stationarity. By contrast, detailed brain simulations may provide an alternative testbed for understanding the interactions between ongoing neural activity and external stimulation. Unlike real brains, the artificial system can be probed extensively and precisely, and detailed output information is readily available. Here we employed a spiking network model of sensorimotor cortex trained to drive a realistic virtual musculoskeletal arm to reach a target. The network was then perturbed, in order to simulate a lesion, by either silencing neurons or removing synaptic connections. All lesions led to significant behvaioral impairments during the reaching task. The remaining cells were then systematically probed with a set of single and multiple-cell stimulations, and results were used to build an inverse model of the neural system. The inverse model was constructed using a kernel adaptive filtering method, and was used to predict the neural stimulation pattern required to recover the pre-lesion neural activity. Applying the derived neurostimulation to the lesioned network improved the reaching behavior performance. This work proposes a novel neurocontrol method, and provides theoretical groundwork on the use biomimetic brain models to develop and evaluate neurocontrollers that restore the function of damaged brain regions and the corresponding motor behaviors.

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Section: Discussionmentioning

confidence: 99%

Restoring Behavior via Inverse Neurocontroller in a Lesioned Cortical Spiking Model Driving a Virtual Arm

Durá-Bernal

Neymotin

et al. 2016

Front. Neurosci.

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“…The complexity of the project is astonishing, and it opens amazing perspectives for insight in the complexities of the brain. The paper also raises questions of more philosophical nature brilliantly examined in the accompanying perspective by Koch and Buice (2015). …”

Section: Introductionmentioning

confidence: 96%

An Evaluation of the Accuracy of Classical Models for Computing the Membrane Potential and Extracellular Potential for Neurons

Tveito

Jæger

Lines

et al. 2017

Front. Comput. Neurosci.

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Two mathematical models are part of the foundation of Computational neurophysiology; (a) the Cable equation is used to compute the membrane potential of neurons, and, (b) volume-conductor theory describes the extracellular potential around neurons. In the standard procedure for computing extracellular potentials, the transmembrane currents are computed by means of (a) and the extracellular potentials are computed using an explicit sum over analytical point-current source solutions as prescribed by volume conductor theory. Both models are extremely useful as they allow huge simplifications of the computational efforts involved in computing extracellular potentials. However, there are more accurate, though computationally very expensive, models available where the potentials inside and outside the neurons are computed simultaneously in a self-consistent scheme. In the present work we explore the accuracy of the classical models (a) and (b) by comparing them to these more accurate schemes. The main assumption of (a) is that the ephaptic current can be ignored in the derivation of the Cable equation. We find, however, for our examples with stylized neurons, that the ephaptic current is comparable in magnitude to other currents involved in the computations, suggesting that it may be significant—at least in parts of the simulation. The magnitude of the error introduced in the membrane potential is several millivolts, and this error also translates into errors in the predicted extracellular potentials. While the error becomes negligible if we assume the extracellular conductivity to be very large, this assumption is, unfortunately, not easy to justify a priori for all situations of interest.

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“…Today's most impressive multiscale simulations are arguably the weather simulations that provide, with increased accuracy year by year, our weather forecasts (Bauer et al, 2015). These physics-and chemistry-based simulations bridge scales from tens of meters to tens of thousands of kilometers, the size of our planet, and are in computational complexity comparable to whole-brain simulations (Koch and Buice, 2015).…”

Section: Brain Simulationsmentioning

confidence: 99%

The Scientific Case for Brain Simulations

Einevoll

Destexhe

Diesmann

et al. 2019

Neuron

148

129

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In BriefA key element of several large-scale brain research projects such as the EU Human Brain Project is simulation of large networks of neurons. Here it is argued why such simulations are indispensable for bridging the neuron and system levels in the brain. AbstractA key element of the European Union's Human Brain Project (HBP) and other large-scale brain research projects is simulation of largescale model networks of neurons. Here we argue why such simulations will likely be indispensable for bridging the scales between the neuron and system levels in the brain, and a set of brain simulators based on neuron models at different levels of biological detail should thus be developed. To allow for systematic refinement of candidate network models by comparison with experiments, the simulations should be multimodal in the sense that they should not only predict action potentials, but also electric, magnetic, and optical signals measured at the population and system levels.

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A Biological Imitation Game

Cited by 9 publications

References 20 publications

Restoring Behavior via Inverse Neurocontroller in a Lesioned Cortical Spiking Model Driving a Virtual Arm

Restoring Behavior via Inverse Neurocontroller in a Lesioned Cortical Spiking Model Driving a Virtual Arm

An Evaluation of the Accuracy of Classical Models for Computing the Membrane Potential and Extracellular Potential for Neurons

The Scientific Case for Brain Simulations

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