Skip to main content

Cortical Motor Prosthesis

  • Living reference work entry
  • First Online:
Encyclopedia of Computational Neuroscience

Definition

Neuromotor prostheses or, more commonly referred to as brain-machine interfaces (BMIs) or brain-computer interfaces (BCIs), refer to systems controlling prosthetic devices via an interface with ensembles of the neurons often from the cortex. Electrical potentials emanating from neurons in the vicinity of an electrode interface are decoded to extract useful control signals for external devices, typically an artificial limb or a robot. Nonelectric potentials such as the metabolic signals are also being used in some BMIs.

Introduction

Cortically controlled BMIs utilize voluntary modulations of cortical neurons in controlling an external prosthetic device. The system-level architecture of a BMI setup is shown in Fig. 1. Individual neural signals, i.e., action potential spikes, local field potentials, ECoGs, and EEGs, or a combination of these signals, can be used to control a motor prosthesis. Temporal and spectral modulations of these signals are typically mapped (or decoded)...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Abbreviations

ALS:

Amyotrophic lateral sclerosis

BCI:

Brain-computer interface

BMI:

Brain-machine interface

ECG:

Electrocardiogram

ECoG:

Electrocorticogram

EEG:

Electroencephalogram

EMG:

Electromyogram

EOG:

Electrooculogram

ERP:

Event-related potential

SCI:

Spinal cord injury

References

  • Andersen RA, Musallam S, Pesaran B (2004) Selecting the signals for a brain-machine interface. Curr Opin Neurobiol 14(6):720–726

    Article  CAS  PubMed  Google Scholar 

  • Aniruddha C, Vikram A, Ander R, Soumyadipta A, Nitish T (2007) A brain-computer interface with vibrotactile biofeedback for haptic information. J NeuroEng Rehab 4

    Google Scholar 

  • Ashe J, Georgopoulos AP (1994) Movement parameters and neural activity in motor cortex and area 5. Cereb Cortex 4(6):590–600

    Article  CAS  PubMed  Google Scholar 

  • Badreldin I, Southerland J, Mukta V, Eleryan A, Balasubramanian K, Fagg A, Hatsopoulos N, Oweiss K (2013) Unsupervised decoder initialization for brain-machine interfaces using neural state space dynamics. In: Neural engineering (NER), 2013 international IEEE/EMBS conference on, San Diego, California, USA, pp 997–1000

    Google Scholar 

  • Balasubramanian K, Southerland J, Mukta V, Qian K, Eleryan A, Fagg AH, Sluzky M, Oweiss K, Hatsopoulos N (2013) Operant conditioning of a multiple degree-of-freedom brain-machine interface in a primate model of amputation. In: Engineering in medicine and biology society (EMBC), 2013 annual international conference of the IEEE, Osaka, Japan, pp 303–306

    Google Scholar 

  • Berg J, Dammann J, Tenore F, Tabot G, Boback J, Manfredi L, Peterson M, Katyal K, Johannes M, Makhlin A, Wilcox R, Franklin R, Vogelstein R, Hatsopoulos N, Bensmaia S (2013) Behavioral demonstration of a somatosensory neuroprosthesis. Neural Syst Rehab Eng IEEE Trans 21(3):500–507

    Article  CAS  Google Scholar 

  • Black MJ, Bienenstock E, Donoghue JP, Serruya M, Wu W, Gao Y (2003) Connecting brains with machines: the neural control of 2D cursor movement. In: Neural engineering (NER), 2003 international IEEE/EMBS conference on, Capri Island, Italy, pp 580–583

    Google Scholar 

  • Black MJ, Donoghue JP (2007) Probabilistically modeling and decoding neural population activity in motor cortex. In: Guido Dornhege THDJM, del Millán JR, Müller K-R (eds) Toward Brain-Computer Interfacing, MIT Press, Cambridge, MA, pp. 147–159

    Google Scholar 

  • Brown EN, Nguyen DP, Frank LM, Wilson MA, Solo V (2001) An analysis of neural receptive field plasticity by point process adaptive filtering. Proc Natl Acad Sci 98(21):12261–12266

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Brown EN, Barbieri R, Eden UT, Frank LM (2003) Likelihood methods for neural spike train data analysis. In: Feng J (ed.), Computational neuroscience: A comprehensive approach. Chapman & Hall/CRC London, PP 253–286

    Google Scholar 

  • Brunner P, Ritaccio AL, Lynch TM, Emrich JF, Wilson JA, Williams JC, Aarnoutse EJ, Ramsey NF, Leuthardt EC, Bischof H, Schalk G (2009) A practical procedure for real-time functional mapping of eloquent cortex using electrocorticographic signals in humans. Epilepsy Behav 15(3):278–286

    Article  PubMed Central  PubMed  Google Scholar 

  • Cabel DW, Cisek P, Scott SH (2001) Neural activity in primary motor cortex related to mechanical loads applied to the shoulder and elbow during a postural task. J Neurophysiol 86(4):2102–2108

    CAS  PubMed  Google Scholar 

  • Carmena JM, Lebedev MA, Crist RE, O’Doherty JE, Santucci DM, Dimitrov DF, Patil PG, Henriquez CS, Nicolelis MA (2003) Learning to control a brain–machine interface for reaching and grasping by primates. PLoS Biol 1(2):e42

    Article  PubMed Central  PubMed  Google Scholar 

  • Chebat D-R, Schneider FC, Kupers R, Ptito M (2011) Navigation with a sensory substitution device in congenitally blind individuals. Neuroreport 22(7):342–347

    Article  PubMed  Google Scholar 

  • Chen Z (2003) Bayesian filtering: from kalman filters to particle filters, and beyond. Statistics 182(1):1–69

    Article  Google Scholar 

  • Cheney PD, Fetz EE (1980) Functional classes of primate corticomotoneuronal cells and their relation to active force. J Neurophysiol 44(4):773–791

    CAS  PubMed  Google Scholar 

  • Cincotti F, Kauhanen L, Aloise F, Palomäki T, Caporusso N, Jylänki P, Mattia D, Babiloni F, Vanacker G, Nuttin M, Marciani MG, del Millán JR (2007) Vibrotactile feedback for brain-computer interface operation. Intell Neurosci 12

    Google Scholar 

  • Ciocarlie M, Goldfeder C, Allen P (2007) Dexterous grasping via eigengrasps: a low-dimensional approach to a high-complexity problem. In: Proceedings of the robotics: science and systems 2007 manipulation workshop - sensing and adapting to the real world. Robotics: science and systems conference, Atlanta, Georgia USA

    Google Scholar 

  • Clanton ST, McMorland AJ, Zohny Z, Jeffries SM, Rasmussen RG, Flesher SN, Velliste M (2013) Seven degree of freedom cortical control of a robotic arm. In: Brain-computer interface research. Springer, Berlin, pp 73–81

    Chapter  Google Scholar 

  • Collinger JL, Wodlinger B, Downey JE, Wang W, Tyler-Kabara EC, Weber DJ, McMorland AJ, Velliste M, Boninger ML, Schwartz AB (2013) High-performance neuroprosthetic control by an individual with tetraplegia. The Lancet 381(9866):557–564

    Article  Google Scholar 

  • Corke P (2011) Robotics, vision and control: fundamental algorithms in MATLAB, vol 73. Springer, Berlin

    Book  Google Scholar 

  • Crago PE, Houk JC, Hasan Z (1976) Regulatory actions of human stretch reflex. J Neurophysiol 39(5):925–935

    CAS  PubMed  Google Scholar 

  • Donoghue JP, Sanes JN, Hatsopoulos NG, Gaál G (1998) Neural discharge and local field potential oscillations in primate motor cortex during voluntary movements. J Neurophysiol 79(1):159–173

    CAS  PubMed  Google Scholar 

  • Dushanova J, Donoghue J (2010) Neurons in primary motor cortex engaged during action observation. Eur J Neurosci 31(2):386–398

    Article  PubMed Central  PubMed  Google Scholar 

  • Eide PK, Jørum E, Stenehjem AE (1996) Somatosensory findings in patients with spinal cord injury and central dysaesthesia pain. J Neurol Neurosurg Psychiatry 60(4):411–415

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Fagg A, Ojakangas G, Miller L, Hatsopoulos N (2009) Kinetic trajectory decoding using motor cortical ensembles. Neural Syst Rehab Eng IEEE Trans 17(5):487–496

    Article  Google Scholar 

  • Farwell LA, Donchin E (1988) Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol 70(6):510–523

    Article  CAS  PubMed  Google Scholar 

  • Fetz EE (1969) Operant conditioning of cortical unit activity. Science 163(3870):955–958

    Article  CAS  PubMed  Google Scholar 

  • Fetz EE, Finocchio DV (1971) Operant conditioning of specific patterns of neural and muscular activity. Science 174(4007):431–435

    Article  CAS  PubMed  Google Scholar 

  • Fetz E, Finocchio D (1975) Correlations between activity of motor cortex cells and arm muscles during operantly conditioned response patterns. Exp Brain Res 23(3):217–240

    CAS  PubMed  Google Scholar 

  • Finnerup NB, Gyldensted C, Fuglsang-Frederiksen A, Bach FW, Jensen TS (2004) Sensory perception in complete spinal cord injury. Acta Neurol Scand 109(3):194–199

    Article  CAS  PubMed  Google Scholar 

  • Flash T, Hogan N (1985) The coordination of arm movements: an experimentally confirmed mathematical model. J Neurosci 5(7):1688–1703

    CAS  PubMed  Google Scholar 

  • Flint R, Wright Z, Slutzky M (2012) Control of a biomimetic brain machine interface with local field potentials: Performance and stability of a static decoder over 200 days. In: Engineering in medicine and biology society (EMBC), 2012 annual international conference of the IEEE, San Diego, California, USA, pp 6719–6722

    Google Scholar 

  • Flint RD, Wright ZA, Scheid MR, Slutzky MW (2013) Long term, stable brain machine interface performance using local field potentials and multiunit spikes. J Neural Eng 10(5):056005

    Article  PubMed  Google Scholar 

  • Fu Q, Suarez J, Ebner T (1993) Neuronal specification of direction and distance during reaching movements in the superior precentral premotor area and primary motor cortex of monkeys. J Neurophysiol 70(5):2097–2116

    CAS  PubMed  Google Scholar 

  • Fu Q, Flament D, Coltz J, Ebner T (1995) Temporal encoding of movement kinematics in the discharge of primate primary motor and premotor neurons. J Neurophysiol 73(2):836–854

    CAS  PubMed  Google Scholar 

  • Georgopoulos AP, Schwartz AB, Kettner RE (1986) Neuronal population coding of movement direction. Science 233(4771):1416–1419

    Article  CAS  PubMed  Google Scholar 

  • Gilja V, Nuyujukian P, Chestek CA, Cunningham JP, Byron MY, Fan JM, Church-land MM, Kaufman MT, Kao JC, Ryu SI, Shenoy KV (2012) A high-performance neural prosthesis enabled by control algorithm design. Nature Neurosci 15:1752–1757

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Grill WM, Norman SE, Bellamkonda RV (2009) Implanted neural interfaces: biochallenges and engineered solutions. Annu Rev Biomed Eng 11:1–24

    Article  CAS  PubMed  Google Scholar 

  • Hatsopoulos NG, Donoghue JP (2009) The science of neural interface systems. Ann Rev Neurosci 32:249

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Hatsopoulos N, Joshi J, O’Leary JG (2004) Decoding continuous and discrete motor behaviors using motor and premotor cortical ensembles. J Neurophysiol 92(2):1165–1174

    Article  PubMed  Google Scholar 

  • Hochberg LR, Serruya MD, Friehs GM, Mukand JA, Saleh M, Caplan AH, Branner A, Chen D, Penn RD, Donoghue JP (2006) Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442(7099):164–171

    Article  CAS  PubMed  Google Scholar 

  • Hochberg LR, Bacher D, Jarosiewicz B, Masse NY, Simeral JD, Vogel J, Haddadin S, Liu J, Cash SS, van der Smagt P et al (2012) Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 485(7398):372–375

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Hwang EJ, Andersen RA (2013) The utility of multichannel local field potentials for brain-machine interfaces. J Neural Eng 10(4):046005

    Article  PubMed  Google Scholar 

  • Ingram JN, Körding KP, Howard IS, Wolpert DM (2008) The statistics of natural hand movements. Exp Brain Res 188(2):223–236

    Article  PubMed Central  PubMed  Google Scholar 

  • Kaczmarek K, Webster J, Bach-y Rita P, Tompkins WJ (1991) Electrotactile and vibrotactile displays for sensory substitution systems. Biom Eng IEEE Trans 38(1):1–16

    Article  CAS  Google Scholar 

  • Katzner S, Nauhaus I, Benucci A, Bonin V, Ringach DL, Carandini M (2009) Local origin of field potentials in visual cortex. Neuron 61(1):35–41

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Kennedy PR, Bakay RA (1998) Restoration of neural output from a paralyzed patient by a direct brain connection. Neuroreport 9(8):1707–1711

    Article  CAS  PubMed  Google Scholar 

  • Kennedy PR, Bakay RA, Moore MM, Adams K, Goldwaithe J (2000) Direct control of a computer from the human central nervous system. Rehab Eng IEEE Trans 8(2):198–202

    Article  CAS  Google Scholar 

  • Kennedy P, Andreasen D, Ehirim P, King B, Kirby T, Mao H, Moore M (2004) Using human extra-cortical local field potentials to control a switch. J Neural Eng 1(2):72

    Article  PubMed  Google Scholar 

  • Lawhern V, Wu W, Hatsopoulos N, Paninski L (2010) Population decoding of motor cortical activity using a generalized linear model with hidden states. J Neurosci Methods 189(2):267–280

    Article  PubMed Central  PubMed  Google Scholar 

  • Lebedev MA, Nicolelis MA (2011) Toward a whole-body neuroprosthetic, chapter-3. In: Jens Schouenborg MG, Danielsen N (eds) Brain machine interfaces: implications for science, clinical practice and society, vol 194, Progress in brain research. Elsevier, Amsterdam, pp 47–60

    Chapter  Google Scholar 

  • Lebedev MA, Carmena JM, O’Doherty JE, Zacksenhouse M, Henriquez CS, Principe JC, Nicolelis MA (2005) Cortical ensemble adaptation to represent velocity of an artificial actuator controlled by a brain-machine interface. J Neurosci 25(19):4681–4693

    Article  CAS  PubMed  Google Scholar 

  • Li Y, Long J, Yu T, Yu Z, Wang C, Zhang H, Guan C (2010) An EEG-based BCI system for 2-d cursor control by combining mu/beta rhythm and p300 potential. Biomed Eng IEEE Trans 57(10):2495–2505

    Article  Google Scholar 

  • Mason C, Gomez J, Ebner T (2001) Hand synergies during reach-to-grasp. J Neurophysiol 86(6):2896–2910

    CAS  PubMed  Google Scholar 

  • McFarland DJ, Lefkowicz AT, Wolpaw JR (1997) Design and operation of an EEG-based brain-computer interface with digital signal processing technology. Behav Res Methods Instrum Comput 29(3):337–345

    Article  Google Scholar 

  • McFarland DJ, Sarnacki WA, Wolpaw JR (2010) Electroencephalographic (EEG) control of three-dimensional movement. J Neural Eng 7(3):036007

    Article  PubMed Central  PubMed  Google Scholar 

  • Milekovic T, Fischer J, Pistohl T, Ruescher J, Schulze-Bonhage A, Aertsen A, Rickert J, Ball T, Mehring C (2012) An online brain–machine interface using decoding of movement direction from the human electrocorticogram. J Neural Eng 9(4):046003

    Article  PubMed  Google Scholar 

  • Mountcastle VB, LaMotte RH, Carli G (1972) Detection thresholds for stimuli in humans and monkeys: comparison with threshold events in mechanoreceptive afferent nerve fibers innervating the monkey hand. J Neurophysiol 35:122–136

    CAS  PubMed  Google Scholar 

  • Mountney J, Obeid I, Silage D (2011). Modular particle filtering FPGA hardware architecture for brain machine interfaces. In: Engineering in medicine and biology society, EMBC, 2011 annual international conference of the IEEE, pp 4617–4620

    Google Scholar 

  • Mountney J, Sobel M, Obeid I (2009). Bayesian auxiliary particle filters for estimating neural tuning parameters. In: Engineering in medicine and biology society. EMBC 2009. Annual international conference of the IEEE, Minneapolis, Minnesota, USA, pp 5705–5708

    Google Scholar 

  • Nemec B, Zlajpah L (2000) Null space velocity control with dynamically consistent pseudo-inverse. Robotica 18(5):513–518

    Article  Google Scholar 

  • Nicolelis MA (2003) Brain-machine interfaces to restore motor function and probe neural circuits. Nat Rev Neurosci 4(5):417–422

    Article  CAS  PubMed  Google Scholar 

  • Nijboer F, Furdea A, Gunst I, Mellinger J, McFarland DJ, Birbaumer N, Kübler A (2008a) An auditory brain-computer interface (bci). J Neurosci Methods 167(1):43–50

    Article  PubMed  Google Scholar 

  • Nijboer F, Sellers E, Mellinger J, Jordan M, Matuz T, Furdea A, Halder S, Mochty U, Krusienski D, Vaughan T, Wolpaw J, Birbaumer N, Kübler A (2008b) A p300-based brain-computer interface for people with amyotrophic lateral sclerosis. Clin Neurophysiol 119(8):1909–1916

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • O’Doherty JE, Lebedev M, Hanson TL, Fitzsimmons N, Nicolelis MA (2009) A brain-machine interface instructed by direct intracortical microstimulation. Front Integ Neurosci 3(20)

    Google Scholar 

  • O’Doherty JE, Lebedev MA, Ifft PJ, Zhuang KZ, Shokur S, Bleuler H, Nicolelis MA (2011) Active tactile exploration using a brain-machine-brain interface. Nature 479(7372):228–231

    Article  PubMed Central  PubMed  Google Scholar 

  • Paninski L, Fellows MR, Hatsopoulos NG, Donoghue JP (2004) Spatiotemporal tuning of motor cortical neurons for hand position and velocity. J Neurophysiol 91(1):515–532

    Article  PubMed  Google Scholar 

  • Patterson PE, Katz JA (1992) Design and evaluation of a sensory feedback system that provides grasping pressure in a myoelectric hand. J Rehabil Res Dev 29(1):1–8

    Article  CAS  PubMed  Google Scholar 

  • Santello M (2002) Kinematic synergies for the control of hand shape. Arch Italiennes de Biologie 140(3):221–228

    CAS  Google Scholar 

  • Santhanam G, Ryu SI, Byron MY, Afshar A, Shenoy KV (2006) A high-performance brain–computer interface. Nature 442(7099):195–198

    Article  CAS  PubMed  Google Scholar 

  • Schalk G, Kubánek J, Miller K, Anderson N, Leuthardt E, Ojemann J, Limbrick D, Moran D, Gerhardt L, Wolpaw J (2007) Decoding two-dimensional movement trajectories using electrocorticographic signals in humans. J Neural Eng 4(3):264

    Article  CAS  PubMed  Google Scholar 

  • Schreuder M, Blankertz B, Tangermann M (2010) A new auditory multi-class brain-computer interface paradigm: spatial hearing as an informative cue. PLoS ONE 5(4):e9813

    Article  PubMed Central  PubMed  Google Scholar 

  • Seel N (2012) Operant conditioning. In: Seel N (ed) Encyclopedia of the sciences of learning. Springer, New York, p 2526

    Chapter  Google Scholar 

  • Sergio LE, Hamel-Pâquet C, Kalaska JF (2005) Motor cortex neural correlates of output kinematics and kinetics during isometric-force and arm-reaching tasks. J Neurophysiol 94(4):2353–2378

    Article  PubMed  Google Scholar 

  • Serruya MD, Hatsopoulos NG, Paninski L, Fellows MR, Donoghue JP (2002) Brain-machine interface: instant neural control of a movement signal. Nature 416(6877):141–142

    Article  CAS  PubMed  Google Scholar 

  • Shadmehr R (2005) The computational neurobiology of reaching and pointing: a foundation for motor learning. MIT Press, Cambridge, MA

    Google Scholar 

  • Shpigelman L, Lalazar H, Vaadia E (2008) Kernel-arma for hand tracking and brainmachine interfacing during 3d motor control. In: Koller D, Schuurmans D, Bengio Y, Bottou L (eds) Advances in neural information processing systems. Curran Associates, Inc. Red Hook, NY, 21:1489–1496

    Google Scholar 

  • Sitaram R, Caria A, Birbaumer N (2009) Hemodynamic brain-computer interfaces for communication and rehabilitation. Neural Netw 22(9):1320–1328

    Article  PubMed  Google Scholar 

  • Slutzky MW, Jordan LR, Krieg T, Chen M, Mogul DJ, Miller LE (2010) Optimal spacing of surface electrode arrays for brain–machine interface applications. J Neural Eng 7(2):026004

    Article  Google Scholar 

  • Smith A, Hepp-Reymond MC, Wyss U (1975) Relation of activity in precentral cortical neurons to force and rate of force change during isometric contractions of finger muscles. Exp Brain Res 23(3):315–332

    CAS  PubMed  Google Scholar 

  • Suminski AJ, Tkach DC, Hatsopoulos NG (2009) Exploiting multiple sensory modalities in brain-machine interfaces. Neural Netw 22(9):1224–1234

    Article  PubMed Central  PubMed  Google Scholar 

  • Suminski AJ, Tkach DC, Fagg AH, Hatsopoulos NG (2010) Incorporating feedback from multiple sensory modalities enhances brain-machine interface control. J Neurosci 30(50):16777–16787

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Taira M, Boline J, Smyrnis N, Georgopoulos AP, Ashe J (1996) On the relations between single cell activity in the motor cortex and the direction and magnitude of three-dimensional static isometric force. Exp Brain Res 109(3):367–376

    CAS  PubMed  Google Scholar 

  • Taylor DM, Tillery SIH, Schwartz AB (2002) Direct cortical control of 3d neuro-prosthetic devices. Science 296(5574):1829–1832

    Article  CAS  PubMed  Google Scholar 

  • Tkach D, Reimer J, Hatsopoulos NG (2008) Observation-based learning for brain–machine interfaces. Curr Opin Neurobiol 18(6):589–594

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Wander JD, Blakely T, Miller KJ, Weaver KE, Johnson LA, Olson JD, Fetz EE, Rao RPN, Ojemann JG (2013) Distributed cortical adaptation during learning of a brain-computer interface task. Proc Natl Acad Sci 110(26):10818–10823

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Wang W, Chan SS, Heldman DA, Moran DW (2007) Motor cortical representation of position and velocity during reaching. J Neurophysiol 97(6):4258–4270

    Article  PubMed  Google Scholar 

  • Wang W, Collinger JL, Degenhart AD, Tyler-Kabara EC, Schwartz AB, Moran DW, Weber DJ, Wodlinger B, Vinjamuri RK, Ashmore RC et al (2013) An electrocorticographic brain interface in an individual with tetraplegia. PLoS One 8(2):e55344

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Wolpaw J, Wolpaw EW (2012) Brain-computer interfaces: principles and practice. Oxford University Press, New York

    Book  Google Scholar 

  • Wu W, Gao Y, Bienenstock E, Donoghue JP, Black MJ (2006) Bayesian population decoding of motor cortical activity using a kalman filter. Neural Comput 18(1):80–118

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Karthikeyan Balasubramanian .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this entry

Cite this entry

Balasubramanian, K., Hatsopoulos, N.G. (2014). Cortical Motor Prosthesis. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7320-6_705-1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4614-7320-6_705-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, New York, NY

  • Online ISBN: 978-1-4614-7320-6

  • eBook Packages: Springer Reference Biomedicine and Life SciencesReference Module Biomedical and Life Sciences

Publish with us

Policies and ethics