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Neuralink progress update August 28 2020 Some commentary. The good:
The mixed:
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PMID-15928412[0] Naive coadaptive Control May 2005. see notes ____References____ | |||||||||
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PMID-17021028[0] Correlations Between the Same Motor Cortex Cells and Arm Muscles During a Trained Task, Free Behavior, and Natural Sleep in the Macaque Monkey
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PMID-101388[0] Fine control of operantly conditioned firing patterns of cortical neurons.
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A Wireless 32-Channel Implantable Bidirectional Brain Machine Interface
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PMID-21775782[0] Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex (Shenoy)
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ref: -0
tags: Shenoy eye position BMI performance monitoring
date: 01-25-2013 00:41 gmt
revision:1
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PMID-18303802 Cortical neural prosthesis performance improves when eye position is monitored.
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PMID-15858046[0] Redundancy and synergy of neuronal ensembles in motor cortex.
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PMID-21499255[0] Reversible large-scale modification of cortical networks during neuroprosthetic control.
Other notes:
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PMID-16838014[] Neuronal ensemble control of prosthetic devices by a human with tetraplegia
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PMID-11090763[0] EEG-based communication: presence of an error potential.
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IEEE-5332822 (pdf) Neural prosthetic systems: Current problems and future directions
____References____ Chestek, C.A. and Cunningham, J.P. and Gilja, V. and Nuyujukian, P. and Ryu, S.I. and Shenoy, K.V. Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE 3369 -3375 (2009) | |||||||||
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PMID-22157115 Decoding 3D reach and grasp from hybrid signals in motor and premotor cortices: spikes, multiunit activity, and local field potentials.
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PMID-14634657[0]Inference of hand movements from local field potentials in monkey motor cortex
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PMID-22096594[0] Comprehensive analysis of tissue preservation and recording quality from chronic multielectrode implants.
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PMID-10223510 Chronic recording capability of the Utah Intracortical Electrode Array in cat sensory cortex.
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ref: Jarosiewicz-2008.12
tags: Schwartz BMI learning perturbation
date: 03-07-2012 17:11 gmt
revision:2
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PMID-19047633[0] Functional network reorganization during learning in a brain-computer interface paradigm.
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ref: Rouse-2011.06
tags: BMI chronic DBS bidirectional stimulator Washington Medtronic ASIC translational
date: 03-05-2012 23:56 gmt
revision:3
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PMID-21543839[0] A chronic generalized bi-directional brain-machine interface.
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PMID-21867795[0] Deep brain stimulation: BCI at large, where are we going to?
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PMID-20161810[0] Bridging the Divide between Neuroprosthetic Design, Tissue Engineering and Neurobiology
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PMID-19621062 Emergence of a stable cortical map for neuroprosthetic control.
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PMID-19255460[0] Lower layers in the motor cortex are more effective targets for penetrating microelectrodes in cortical prostheses.
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PMID-16838020[0] A high-performance brain-computer interface
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PMID-15247483[0] Cognitive control signals for Neural Prosthetics
PMID-15491902 Cognitive neural prosthetics
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IEEE-1300783 (pdf) Transmission latencies in a telemetry-linked brain-machine interface
____References____ Bossetti, C.A. and Carmena, J.M. and Nicolelis, M.A.L. and Wolf, P.D. Transmission latencies in a telemetry-linked brain-machine interface Biomedical Engineering, IEEE Transactions on 51 6 919 -924 (2004.06) | |||||||||
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PMID-14624244[0] Learning to control a brain-machine interface for reaching and grasping by primates.
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PMID-18923392[0] Direct control of paralysed muscles by cortical neurons.
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PMID-12052948[0] Direct Cortical Control of 3D Neuroprosthetic Devices
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PMID-12647229[0] Robustness of neuroprosthetic decoding algorithms
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PMID-11099043[0] Real-time prediction of hand trajectory by ensembles of cortical neurons in primates
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PMID-9665587[0] Restoration of neural output from a paralyzed patient by a direct brain connection.
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PMID-10896186[] Direct control of a computer from the human central nervous system
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PMID-7409057[0] Operant control of precentral neurons: comparison of fast and slow pyramidal tract neurons.
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PMID-4196269[0] Operantly conditioned patterns on precentral unit activity and correlated responses in adjacent cells and contralateral muscles
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PMID-4974291[0] Operant conditioning of cortical unit activity
PMID-5000088[1] Operant conditioning of specific patterns of neural and muscular activity. In awake monkeys we recorded activity of single "motor" cortex cells, four contralateral arm muscles, and elbow position, while operantly reinforcing several patterns of motor activity. With the monkey's arm held semiprone in a cast hinged at the elbow, we reinforced active elbow movements and tested cell responses to passive elbow movements. With the cast immobilized we reinforced isometric contraction of each of the four muscles in isolation, and bursts of cortical cell activity with and without simultaneous suppression of muscle activity. Correlations between a precentral cell and specific arm muscles consistently appeared under several behavioral conditions, but could be dissociated by reinforcing cell activity and muscle suppression. PMID-4624487[2] Operant conditioning of isolated activity in specific muscles and precentral cells Recorded precentral units in monkeys, trained to contract 4 arm muscles in isolation, under various conditions: passive movements and cutaneous stimulation, active movements and isometric contractions. Some Ss were also reinforced for activity of cortical cells, with no contingency in muscle activity and with simultaneous suppression of all muscular activity. It is concluded that temporal correlations between activity of precentral cells and some other component of the motor response, e.g., muscle activity, force, or position, may depend as strongly on the specific response pattern which is reinforced as on any underlying physiological connection. ____References____ | |||||||||
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PMID-6077726[0] The limbic system and behavioral reinforcement
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with: {277}
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ref: Zacksenhouse-2007.07
tags: Zacksenhouse 2007 Odoherty Nicolelis cortical adaptation BMI
date: 01-06-2012 03:10 gmt
revision:3
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PMID-17637835[0] Cortical modulations increase in early sessions with brain-machine interface.
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Recently we bought a OCZ NIA device for our lab. Having designed similar hardware myself, I simply *had to* take the thing apart to inspect it, as others have done -- see Joe Pit's teardown (with schematic!!). Of course, I graciously let the others try it for a few hours (it doesn't work all that well) before taking the anodized, extruded, surface- ground aluminum case apart. Below is the top side of the 4-layer circuit board inside the case, as well as a key to indicate the function of the labeled devices. (some of the labels are hard to read due to the clutter of the silkscreen on the board; sorry).
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from the book "Neural Prostheses for Restoration of Sensory and Motor Function" edited by John Chapin and Karen Moxon. Phillip Kennedy's one-channel neurotrophic glass electrode BMI (axons apparently grew into the electrode, and he recorded from them) Pat Wolf on neural amplification / telemetry technology battery technology for powering the neural telemetry | |||||||||
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images/425_1.pdf August 2007
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http://www.ibva.com/Gallery/Gallery.htm
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IEEE-5946801 (pdf) A low-power implantable neuroprocessor on nano-FPGA for Brain Machine interface applications
____References____ Fei Zhang and Aghagolzadeh, M. and Oweiss, K. Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on 1593 -1596 (2011) | |||||||||
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IEEE-5910570 (pdf) Spiking neural network decoder for brain-machine interfaces
____References____ Dethier, J. and Gilja, V. and Nuyujukian, P. and Elassaad, S.A. and Shenoy, K.V. and Boahen, K. Neural Engineering (NER), 2011 5th International IEEE/EMBS Conference on 396 -399 (2011) | |||||||||
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IEEE-1634510 (pdf) Continuous shared control for stabilizing reaching and grasping with brain-machine interfaces.
____References____ Kim, H.K. and Biggs, J. and Schloerb, W. and Carmena, M. and Lebedev, M.A. and Nicolelis, M.A.L. and Srinivasan, M.A. Continuous shared control for stabilizing reaching and grasping with brain-machine interfaces Biomedical Engineering, IEEE Transactions on 53 6 1164 -1173 (2006) | |||||||||
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Experiment: you have a key. You want that key to learn to control a BMI, but you do not want the BMI to learn how the key does things, as
Given this, I propose a very simple groupweight: one axis is controlled by the summed action of a certain population of neurons, the other by a second, disjoint, population; a third population serves as control. The task of the key is to figure out what does what: how does the firing of a given unit translate to movement (forward model). Then the task during actual behavior is to invert this: given movement end, what sequence of firings should be generated? I assume, for now, that the brain has inbuilt mechanisms for inverting models (not that it isn't incredibly interesting -- and I'll venture a guess that it's related to replay, perhaps backwards replay of events). This leaves us with the task of inferring the tool-model from behavior, a task that can be done now with our modern (though here-mentioned quite simple) machine learning algorithms. Specifically, it can be done through supervised learning: we know the input (neural firing rates) and the output (cursor motion), and need to learn the transform between them. I can think of many ways of doing this on a computer:
{i need to think more about model-building, model inversion, and songbird learning?} | |||||||||
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PMID-17694874[0] The muscle activation method: an approach to impedance control of brain-machine interfaces through a musculoskeletal model of the arm.
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PMID-18509337[0] Cortical control of a prosthetic arm for self-feeding
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PMID-19464514[0] Three-dimensional, automated, real-time video system for tracking limb motion in brain-machine interface studies.
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PMID-17978021[0] Biomimetic Brain Machine Interfaces for the Control of Movement.
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From Uncertain Spikes to Prosthetic Control a powerpoint presentation w/ good overview of all that the Brown group has done | |||||||||
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PMID-13969854[0] Control and Training of Individual Motor Units
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PMID-4207598[0] Behavioral control of firing patterns of normal and abnormal neurons in chronic epileptic cortex.
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PMID-6794389[0] Single neuron recording from motor cortex as a possible source of signals for control of external devices
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bibtex:Olson-2005 Evidence of a mechanism of neural adaptation in the closed loop control of directions
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ref: Hatsopoulos-2005.01
tags: BMI Hatsopoulos Donoghue cortex
date: 01-03-2012 22:49 gmt
revision:4
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PMID-17282055[0][] Cortically controlled brain-machine interface
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PMID-19750199[0] A brain-machine interface instructed by direct intracortical microstimulation.
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PMID-16859758 Brain-machine interfaces: past, present and future.
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PMID-19668698[0] A low-cost multielectrode system for data acquisition enabling real-time closed-loop processing with rapid recovery from stimulation artifacts
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IEEE-1419566 (pdf) A Portable Wireless DSP System for a Brain Machine Interface
____References____ Darmanjian, S. and Morrison, S. and Dang, B. and Gugel, K. and Principe, J. Neural Engineering, 2005. Conference Proceedings. 2nd International IEEE EMBS Conference on 112 -115 (2005) | |||||||||
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IEEE-1439548 (pdf) Interpreting spatial and temporal neural activity through a recurrent neural network brain-machine interface
____References____ Sanchez, J.C. and Erdogmus, D. and Nicolelis, M.A.L. and Wessberg, J. and Principe, J.C. Interpreting spatial and temporal neural activity through a recurrent neural network brain-machine interface Neural Systems and Rehabilitation Engineering, IEEE Transactions on 13 2 213 -219 (2005) | |||||||||
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PMID-16291944[0] Stable ensemble performance with single-neuron variability during reaching movements in primates.
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PMID-14757341[1] A low power multichannel analog front end for portable neural signal recordings.
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ref: OLDS-1954.12
tags: Olds Milner operant conditioning electrical reinforcement wireheading BMI
date: 12-29-2011 05:09 gmt
revision:5
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PMID-13233369[0] Positive reinforcement produced by electrical stimulation of septal area and other regions of rat brain.
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PMID-20228862[0] Grand Challenges of Brain Computer Interfaces in the Years to Come
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PMID-18429703 Psychophysical evaluation for visual prosthesis.
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PMID-20011034[0] A Wireless Brain-Machine Interface for Real-Time Speech Synthesis
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ref: Kennedy-1992.08
tags: BMI Kennedy cone electrode electrophysiology recording neurotrophic
date: 12-17-2011 01:00 gmt
revision:1
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PMID-1407726[] The cone electrode: ultrastructural studies following long-term recording in rat and monkey cortex
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PMID-17057705 Long-term motor cortex plasticity induced by an electronic neural implant.
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PMID-15214971[0] Ensemble recordings of human subcortical neurons as a source of motor control signals for a brain-machine interface
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PMID-15022843[0] A simulation study of information transmission by multi-unit microelectrode recordings key idea:
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PMID-4041789 Synchrony between cortical neurons during operant conditioning.
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PMID-12929922 Training in cortical control of neuroprosthetic devices improves signal extraction from small neuronal ensembles.
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PMID-10404201 Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex.
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PMID-18667540[0] Learning a novel myoelectric-controlled interface task.
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http://www.cs.colostate.edu/eeg/links.html
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PMID-15858046[] Redundancy and Synergy of Neuronal Ensembles in Motor Cortex
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PMID-11894084[0] Instant neural control of a movement signal.
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PMID-17234689[0] Volitional control of neural activity: implications for brain-computer interfaces (part of a symposium)
humm.. this paper came out a month ago, and despite the fact that he is much older and more experienced than i, we have arrived at the same conclusions by looking at the same set of data/papers. so: that's good, i guess. ____References____ | |||||||||
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PMID-17143147[0] Decoding movement intent from human premotor cortex neurons for neural prosthetic applications
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PMID-4991377[0] Predicting measures of motor performance from multiple cortical spike trains. Recordings have been obtained simultaneously from several, individually selected neurons in the motor cortex of unanesthetized monkey as the animal performed simple arm movements. With the use of comparatively simple quantitative procedures, the activity of small sets of cells was found to be adequate for rather accurate real-time prediction of the time course of various response measurements. In addition, the results suggest that hypotheses concerning the response variables "controlled" by cortical motor systems may well depend upon whether or not the temporal relations between simultaneously active neurons are taken into account. cited in miguel's book, "Methods for Neural ensemble recordings". However, I can't get the text online. ____References____ | |||||||||
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PMID-17234696[0] Brain-computer interfaces: communication and restoration of movement in paralysis
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PMID-17271333[0] Neuron selection and visual training for population vector based cortical control.
PMID-16705272[1] Selection and parameterization of cortical neurons for neuroprosthetic control
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PMID-17271178[0] automatic spike sorting for neural decoding
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PMID-17271543[] http://hardm.ath.cx:88/pdf/sanchez2004.pdf ____References____ | |||||||||
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PMID-15217341[0] Cortical Neuro Prosthetics
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I find this amusing - but appallingly old.
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PMID-15852014[] Decoding the visual and subjective contents of the human brain
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PMID-15010499[0] Recursive Bayesian Decoding of Motor Cortical Signals by Particle Filtering
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PMID-17190032[0] http://hardm.ath.cx:88/pdf/Marzullo2006_CingulateCortexBMI.pdf
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PMID-12899253 Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis
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PMID-12899266 Response error correction-a demonstration of improved human-machine performance using real-time EEG monitoring
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