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Journal Papers

 

Journal Papers

Comissioned for the Simons Foundation's Simons Collaboration on the Global Brain (SCGB), in association with our Computation Through Dynamics (CTD) research using our  Dynamical Systems Framework (DSF). Illustration credit: Islenia Mil.


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Preprints

143. Paulk AC, Kfir Y, Khanna A, Mustroph M, Trautmann EM, Soper DJ, Stavisky SD, Welkenhuysen M, Dutta B, Shenoy KV, Hochberg LR, Richardson M, Williams ZM, Cash SS. (2021) Large-scale neural recordings with single-cell resolution in human cortex using high-density Neuropixels probes. bioRxiv. url

  • Paulk AC, et al. (2021) Large-scale neural recordings with single-cell resolution in human cortex using high-density Neuropixels probes -- Cortical neuropixel processing pipeline. Code on Github. url

142. Chandrasekaran C, Soldado-Magraner J, Peixoto D, Newsome WT, Shenoy KV, Sahani M (2019) Brittleness in model selection analysis of single neuron firing rates. bioRxivurl

141. Peixoto D, Kiani R, Chandrasekaran C, Ryu SI, Shenoy KV, Newsome WT (2018) Population dynamics of choice representation in dorsal premotor and primary motor cortex. BioRxivurl

140. Gao P, Trautmann E, Yu BM, Santhanam G, Ryu SI, Shenoy KV, Ganguli S (2017) A theory of multineuronal dimensionality, dynamics and measurement. bioRxivurl


2021

139. Sun X*, O'Shea DJ*, Golub MD, Trautmann EM, Vyas S, Ryu SI, Shenoy KV (2021) Cortical preparatory activity indexes learned motor memories. Nature. In press. bioRxiv. url

138. Lee EK, Balasubramanian H, Tsolias A, Anakwe S, Medalla M, Shenoy KV, Chandrasekaran C (2021) Non-linear dimensionality reduction on extracellular waveforms reveals physiological, functional, and laminar diversity in premotor cortex. eLife. 10:e67490. doi.org/10.7554/eLife.67490. pdf url

137. Deo DR, Rezaii PG, Hochberg LR,  Okamura AM, Shenoy KV*,  Henderson JM* (2021) Effects of peripheral haptic feedback on Intracortical brain-computer interface control and associated sensory responses in motor cortex. IEEE Transactions on Haptics. doi.org/10.1109/TOH.2021.3072615. pdf url

136. Simeral JD, Hosman T, Saab J, Flesher SN, Vilela M, Franco B, Kelemen J, Brandman DM, Ciancibello JG, Rezaii PG, Rosler DM, Shenoy KV**, Henderson JM**, Nurmikko AV, Hochberg LR (2021) Home use of a wireless intracortical brain-computer interface by individuals with tetraplegia. IEEE Transactions in Biomedical Engineering. doi.org/10.1109/TBME.2021.3069119. pdf url

135. Trautmann EM*, O'Shea DJ*, Sun X*, Marshel JH, Crow A, Hsueh B, Vesuna S, Cofer L, Bohner G, Allen W, Kauvar I, Quirin S, MacDougall M, Chen Y, Whitmire M, Ramakrishnan C, Sahani M, Seidemann E, Ryu SI, Deisseroth K**, Shenoy KV** (2021) Dendritic calcium signals in rhesus macaque motor cortex drive an optical brain-computer interface. Nature Communications. 12:1-20.

  • Editor's Highlight page
    • ​Nature Communications Editor's Highlights webpage. url
  • Publication Materials
    • ​A "single pdf file with everything below".
      • Full size (50 MB).  pdf
      • Reduced size (13 MB). pdf
    • Main paper. pdf url
    • Supplementary information. pdf url
    • Description of additional supplementary files. pdf url
    • Peer review file. pdf url
    • Supplementary Movies
      • Supplementary Movie 1 - Example dendrite imaging. mp4 url
      • Supplementary Movie 2 - real-time decode of reach behavior using 2P imaging. mp4 url
      • Supplementary Movie 3 - CLARITY imaging of tissue volume. mp4 url
    • Supplementary data 1. xlxs url
    • Reporting summary. pdf url
  • Preview & Comment
    • Canfield RA, Orsborn AL, Horwitz GD (2021) Windows and periscopes into primate behavior. Cell Reports. 36:1-2. pdf url
    • Matsuzaki M, Ebina T (2021) Optical deep-cortex exploration in behaving rhesus macaques. Nature Communications. 12:4656. pdf url
  • Blog Post
    • Nassi* JJ, Trautmann* EM (2021) A deeper dive with photons. Technical Report #02, Version 1.1. Stanford Digital Repository (SDR), Stanford University. doi url
      • Recent breakthroughs in optical-based imaging in nonhuman primates promise to fundamentally advance our understanding of brain function and accelerate the development of next-generation brain-computer interfaces.
      • Two new studies demonstrate complementary approaches for imaging the activity patterns of large populations of neurons in nonhuman primates (NHPs).
      • Bollimunta*, Santacruz* & colleagues used a head-mounted one-photon miniature microscope to image the activity of neurons deep in the motor cortex of a behaving macaque, and demonstrated the ability to perform offline decoding of the animal’s motor behavior using the same neurons tracked over weeks.
      • Trautmann*, O’Shea*, Sun* & colleagues used two-photon microscopy to image the activity of neurons, also in the motor cortex, and show that it is possible to access deep cortical neurons by imaging their apical dendrites near the surface, allowing them to perform online, closed-loop decoding of the behaving macaque’s motor behavior sufficient to drive an optical brain-computer interface (oBCI).
      • Together, these studies pave the way for new experiments to understand how populations of neurons underlie behavior, and how new BCI technologies can treat neurological injury and disease.
    • Related, independent paper published the same week.
      • Bollimunta* A, Santacruz* SR, Eaton RW, Xu PS, Morrison JH, Moxon KA, Carmena** JM, Nassi** JJ (2021) Head-mounted microendoscopic calcium imaging in dorsal premotor cortex of behaving rhesus macaque. Cell Reports. 35(11). pdf supp_mats url
  • Shared resources
    • O'Shea DJ*, Trautmann EM*, Sun X*, Marshel JH, Crow A, Hsueh B, Vesuna S, Cofer L, Bohner G, Allen W, Kauvar I, Quirin S, MacDougall M, Chen Y, Whitmire M, Ramakrishnan C, Sahani M, Seidemann E, Ryu SI, Deisseroth K**, Shenoy KV**(2021) Dendritic calcium signals in rhesus macaque motor cortex drive an optical brain-computer interface. Datasets on Dryadurl 
    • O'Shea* DJ, Trautmann* EM, Sun X*, Deisseroth** K, Shenoy KV** (2021) Dendritic calcium signals in rhesus macaque motor cortex drive an optical brain-computer interface. Code on Zenodourl. Code on Githuburl

 

Character Building. Brain–computer interfaces (BCIs) have the potential to restore communication to people who have lost the ability to move or speak. To date, the focus has largely been on motor skills such as reaching or grasping. In this week’s issue, Francis Willett and his colleagues present the results from an intracortical BCI that decodes attempted handwriting movements from neural activity in the motor cortex and translates it to text in real time. The researchers worked with a man who is paralysed from the neck down, asking him to try to write by imagining he was holding a pen on a piece of paper. The BCI used a neural network to translate the neural signals into letters, allowing the man to reach a writing speed of 90 characters per minute with an accuracy of 94.1%. The cover features aggregated images of the alphabet derived from the study participant’s neural activity as he thought about writing. Cover image: K. Krause / Nature adapted from F. R. Willett et al. Nature 593, 249–254 (2021). High-resolution version:  pdf.

 

134. Willett FR, Avansino DT, Hochberg LR, Henderson JM*, Shenoy KV* (2021) High-performance brain-to-text communication via imagined handwriting. Nature. 593:249-254.

  • Publication materials
    • A single pdf
      • A “single pdf file with everything below, except only a few selected news pieces".
        • Full size (24 MB) pdf
        • Reduced size (8 MB) pdf
    • Main paper. pdf url
    • Supplementary material. pdf url
    • Peer review file. pdf url
    • Videos
      • Video 1: Copying sentences in real-time with the handwriting brain-computer interface. In this video, participant T5 copies sentences displayed on a computer monitor with the handwriting-brain computer interface. When the red square on the monitor turns green, this cues T5 to begin copying the sentence. url
      • Video 2: Hand micromotion while using the handwriting brain-computer interface. Participant T5 is paralyzed from the neck down (C4 ASIA C spinal cord injury) and only generates small micromotions of the hand when attempting to handwrite. T5 retains no useful hand function. url
      • Video 3: Freely answering questions in real-time with the handwriting brain-computer interface. In this video, participant T5 answers questions that appear on a computer monitor using the handwriting brain-computer interface. T5 was instructed to take as much time as he wanted to formulate an answer, and then to write it as quickly as possible. url
      • Video 4: Side-by-side comparison between the handwriting brain-computer interface and the prior state of the art for intracortical brain-computer interfaces. In a prior study (Pandarinath et al., 2017) participant T5 achieved the highest typing speed ever reported with an intracortical brain-computer interface (39 correct characters per minute using a point-and-click typing system). Here, we show an example sentence typed by T5 using the point-and-click system (shown on the bottom) and the new handwriting brain-computer interface (shown on the top), which is more than twice as fast. url
  • News & Views
    • Rajeswaran P, Orsborn AL (2021) Neural interface translates thoughts into type. News & Views. Nature. 593:197-198. pdf url
  • Shared resources
    • Shenoy KV, Willett FR, Nuyujukian P, Henderson JM (2021) Performance considerations for general-purpose typing BCIs, including the handwriting BCI. Technical Report #01, Version 2.7. Stanford Digital Repository (SDR), Stanford University. doi url
    • Willett FR, Avansino DT, Hochberg LR, Henderson* JM, Shenoy* KV (2021) All electrophysiology data reported in Willett et al. (2021) Nature. Datasets on DataDryadurl
    • Willett FR, Avansino DT, Hochberg LR, Henderson* JM, Shenoy* KV (2021) All code written and used in Willett et al. (2021) Nature. Code on GitHuburl
  • News coverage (selected)
    • Altmetric score (> 4,000) url
    • Bundell S (14 May 2021) Nature Podcast. transcript mp3
    • Altman R (18 May 2021) Krishna Shenoy: How brain-computer connections could end paralysis.  A podcast and SiriusXM satellite radio series brought to you by Stanford School of Engineering. The Future of Everything. YouTube url
    • Servick K (13 May 2021) Paralyzed person types at record speed -- by imagining handwriting. Science. url
      • K Servick (23 Oct 2019) AI allows paralyzed person to 'handwrite' with his mind. Science. pdf url
    • News (Stanford & HHMI)
      • Rosen M (12 May 2021) Brain computer interface turns mental handwriting into text on screen. Howard Hughes Medical Institute (HHMI). News article. pdf url
        • Overview video (1:40 minutes). url
      • Goldman B (12 May 2021) Software turns 'mental handwriting' into on-screen words, sentences. Stanford University. News article. pdf url
      • Weiler N, Toth A (12 My 2021) Eavesdropping on brain activity turns imagined handwriting to text. Wu Tsai Neurosciences Institute, Stanford University. url
        • Overview video 1 (2:40 minutes). url
      • Weiler N, Toth A (12 May 2021) Science in Brief: Decoding Text from Brain Activity via Imagined Handwriting. Wu Tsai Neurosciences Institute, Stanford Universityurl
        • Overview video 2 (3:19 minutes). url
    • News (selected)
      • Hamilton J (12 May 2021) Man who is paralyzed communicates by imagining handwriting. All Things Considered. National Public Radio (NPR)pdf url
        • Audio (3:25 minutes). mp3
        • Transcript. pdf
      • BBC Radio (27 May 2021)
        • Relevant 7:00 minute segment mp3
        • Relevant 7:00 minute segment runs from 0:22:40 min to 0:29:41 min in the original PodCast. url
      • Tomorrow Transformed, CNN International (2021) mp4
      • Stetka B (12 May 2021) New brain implant turns visualized letters into text. Scientific American. url
      • Timmer J (12 May 2021) Neural implant lets paralyzed person type by imagining writing. arsTECHNICA. url
      • Nick's Notes, with Nick Desai: A Conversation with Dr. Krishna Shenoy, PhD. url
      • Rodriguez A (14 May 2021) After researchers implanted microchips into his brain, a paralyzed man was able to write with his mind. USA Today. pdf url
      • Computer deciphers brain signals of imagined writing. ABCurl
      • Paralyzed man uses his mind to form real-time sentences. CNNurl
      • Brain-computer interface allows paralysed man to write again. BBC Science Focus / PAurl
      • Paralysed man uses 'mindwriting' brain computer to compose sentences. The Guardianurl
      • The Times view on a breakthrough for paralysis: Positive thinking. The Timesurl
      • Brain device translates thoughts directly onto a computer. The Independenturl
      • How to type by just thinking: Microchip inserted in the brain turns thoughts into text. MailOnlineurl
      • Mind over matter: brain chip allows paralysed man to write. AFPurl
      • Dos chips en el cerebro permiten escribir con la mente. El Paisurl
      • Mindwriting, il software che trasforma i pensieri in parole. La Repubblicaurl
      • Mind over matter: brain chip allows paralysed man to write. The Hinduurl
      • Research filter: Mindwriting and what do our ancient poos reveal about our gut bacteria? ABC Ausurl
      • Neurotechnologie Maschinen können jetzt gedanken lesen. Spiegelurl
      • Handschriftlich kommunizieren mit gedankenkraft. Spektrumurl
      • Un paralítico envía mensajes a un récord de 16 palabras por minuto. Gizmodo. url
      • Mental handwriting produces brain activity turned Into text. Neuroscience Newsurl
      • AI lets man with paralysis type by just thinking about handwriting. New Scientisturl
      • New brain-computer interface translates handwritten thoughts into text for paralysis patients. IFLSurl
      • Brain implants turn imagined handwriting into text on a screen. Science Newsurl
      • New device allows man with paralysis to type by imagining handwriting. Smithsonianurl
      • Brain-computer interface user types 90 characters per minute with mind. The Scientisturl
      • Paralysed man ‘handwrites’ with brain chip. Cosmosurl
      • “Mindwriting” technique helps paralyzed patient use brain activity to write. Technology Networksurl
      • Computer deciphers the brain signals of imagined writing. Inside Scienceurl
      • Implanted sensor translates brain signals Into text. Medpage Todayurl
      • Created an interface for typing with the power of thought. Forbesurl

133. Shenoy KV, Kao JC (2021) Measurement, manipulation and modeling of brain-wide neural population dynamics. Invited Commentary. Nature Communications. 12:633.   pdf   url

132. Rastogi A, Willett FR, Abreu J, Crowder DC, Murphy B, Memberg WD, Vargas-Irwin CE, Miller JP, Sweet J, Walter BL, Rezaii PG, Stavisky SD, Hochberg LR, Shenoy KV, Henderson JM, Kirsch RF, Ajiboye AB (2021) The neural representation of force across grasp types in motor cortex of humans with tetraplegia. eNeuro 10.1523/ENEURO.0231-20.2020.   pdf   url

131. Peixoto D*, Verhein JR*, Kiani R, Kao JC, Nuyujukian P, Chandrasekaran C, Brown J, Fong S, Ryu SI, Shenoy KV, Newsome WT (2021) Decoding and perturbing decision states in real time. Nature. 589:604-609.  pdf  supp_mats  url


2020

130. Al Borno M, Vyas S, Shenoy KV, Delp SL (2020) High-fidelity musculoskeletal modeling reveals a motor planning contribution to the speed-accuracy tradeoff. eLife. 9:e57021. pdf  supp_figs  url

129. Wilson GH*, Stavisky SD*, Willett FR, Avansino DT, Kelemen JN, Hochberg LR, Henderson JM**, Druckmann S,** Shenoy KV** (2020) Decoding spoken English phonemes from intracortical electrode arrays in dorsal precentral gyrus. Journal of Neural Engineering. 17:066007 pdf  url

128. Tremblay S, Acker L, Afraz A, Albaugh DL, Amita H, Andrei AR, Angelucci A, Aschner A, Balan PF, Basso MA, Benvenuti G, Bohlen MO, Caiola MJ, Calcedo R, Cavanaugh J, Chen Y,  Chen S, Chernov MM, Clark AM, Debes SR, Deisseroth K, Desimone R, Dragoi V, Egger SW, Eldridge M, El-Nahal HG, Fabbrini F, Federer F , Fetsch CR, Fortuna MG, Friedman RM, Fujii N, Gail A, Galvan A, Ghosh S, Gieselmann MA, Gulli RA, Hikosaka O, Hosseini EA, Hu X, Hüer J, Inoue K, Janz R, Jazayeri M, Jiang R, Ju N, Kar K, Klein C, Kohn A, Komatsu M, Maeda K, Martinez-Trujillo JC, Matsumoto M, Maunsell JHR, Mendoza-Halliday D, Monosov IE, Muers RS, Nurminen L, Ortiz-Rios M, O’Shea DJ, Palfi S, Petkov CI, Pojoga S, Rajalingham R, Ramakrishnan C, Remington ED, Revsine C, Roe AW, Sabes PN, Saunders R, Scherberger H, Schmid MC, Schultz W, Seidemann E, Senova Y-S, Shadlen MN, Siu C, Smith Y, Solomon SS, Sommer MA, Spudich JL,  Stauffer WR, Takada M, Tang S, Thiele A, Treue S, Vanduffel W, Vogels R, Whitmire MP, Wichmann T, Wurtz RH, Xu H, Yazdan-Shahmorad A,  Shenoy KV, DiCarlo J, Platt ML (2020) An open resource for non-human primate optogenetics. Neuron. 1075-1090.e6.  pdf  url  NHP_Optogenetics_Open_Database

  • Servick K (2020) Controlling monkey brains with light could get easier thanks to open data project. Science. 370:516–517. pdf url

127. Jiang X, Saggar H, Ryu SI, Shenoy KV, Kao JC (2020) Structure in neural activity during observed and executed movements is shared at the neural population level, not in single neurons. Cell Reports32:108006. pp. 1-14.  pdf  supp_mats  url

126. Even-Chen N*,  Muratore DG*, Stavisky SD, Hochberg LR, Henderson JM, Murmann B**, Shenoy KV** (2020) Power-saving design opportunities for wireless intracortical brain-computer interfaces. Nature Biomedical Engineering. 4:984-996. pdf  supp_mats  url

  • Editorial (2020) The painstaking pace of bioelectronic interfaces. Nature Biomedical Engineering. 4:933–934. pdf url
  • News & Views: Slutzky MW (2020) Increasing power efficiency. Nature Biomedical Engineering4:937–938 pdf url
  • Associated paper: Nason SR, Vaskov AK, Willsey MS, Welle EJ, An H, Vu PP, Bullard AJ, Nu CS, Kao JC, Shenoy KV, Jang T, Kim H-S, Blaauw D, Patil PG, Chestek CA (2020) A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain-machine interfaces. Nature Biomedical Engineering4:973–983. pdf url

125. Nason SR, Vaskov AK, Willsey MS, Welle EJ, An H, Vu PP, Bullard AJ, Nu CS, Kao JC, Shenoy KV, Jang T, Kim H-S, Blaauw D, Patil PG, CA Chestek (2020) A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain-machine interfaces. Nature Biomedical Engineering. pdf  supp_mats  supp_video_1  supp_video_2​.

124. Stavisky SD, Willett FR, Avansino DT, Hochberg LR, Shenoy KV**, Henderson JM** (2020) Speech-related dorsal motor cortex activity does not interfere with iBCI cursor control. Journal of Neural Engineering. 17:016049 (13pp). pdf url

123. Vyas S. Golub MD, Sussillo D, Shenoy KV (2020) Computation through neural population dynamics. Annual Review of Neuroscience. 43:249-275. pdf  url  Review Article

122. Vyas S, O'Shea DJ, Ryu SI, Sheony KV (2020) Causal role of motor preparation during error-driven learning. Neuron. 106:329-339. pdf  url

121. Willett FR*, Deo DR*, Avansino DT, Rezaii PG, Hochberg LR, Henderson JM**, Shenoy KV** (2020) Hand knob area of motor cortex in people with tetraplegia represents the whole body in a compositional way. Cell. 181:396–409. pdf  supp_mats  url

  • Supplemental video 1. Real-time, discrete neural decoding of attempted movements from among 16 possible directional movements spanning the wrists and ankles (related to Fig. 6).
  • Supplemental video 2. Real-time, discrete neural decoding of attempted movements from among 32 possible movements spanning the hands, arms, feet, and legs from both sides of the body (related to Fig. 6).​

120. Rastogi A, Vargas-Irwin C, Willett F, Abreu J, Crowder DC, Murphy B, Memberg W, Miller J, Sweet J, Walter B, Cash S, Rezaii PG, Franco B, Saab J, Stavisky SD, Shenoy KV**, Henderson J**, Hochberg LR, Kirsch R, Ajiboye AB (2020) Neural representation of observed, imagined, and attempted grasping force in motor cortex of individuals with chronic tetraplegia. Scientific Reports. 10:1429. pdf  url

119. Williams A, Poole B, Maheswaranathan N, Dhawale AK, Fisher T, Wilson CD, Brann DH, Trautmann E, Ryu SI, Shusterman R, Rinberg D, Ölveczkylveczky BP, Shenoy KV, Ganguli S (2020) Discovering precise temporal patterns in large-scale neural recordings through robust and interpretable time warping. Neuron. 105: 1–14. pdf  supp_mats  url


2019

118. Stavisky SD,  Willett FR, Wilson GH, Murphy BA, Rezaii PG, Avansino D, Memberg WD, Miller JP, Kirsch RF, Hochberg LR, Ajiboye AB, Druckmann S, Shenoy KV**, Henderson JM** (2019) Neural ensemble dynamics in dorsal motor cortex during speech in people with paralysis. eLife. 8:e46015. pdf  figure-supplements  url

117. Trautmann EM, Stavisky SD, Lahiri S, Ames KC, Kaufman MT, O’Shea DJ, Vyas S, Sun X, Ryu SI, Ganguli S, Shenoy KV (2019) Accurate estimation of neural population dynamics without spike sorting. Neuron. 103:1-17. pdf  supp_mats  url

  • Rossi-Pool R, Romo R (2019) Low dimensionality, high robustness in neural population dynamics. Neuron. Preview. 103:177-179. pdf  url

​116. Ames KC, Ryu SI, Shenoy KV (2019) Simultaneous movement preparation and execution in a last-moment reach correction task. Nature Communications. 10(1):2718. pdf  supp mats  url

115. Willett FR, Young DR, Murphy BA, Memberg WD, Blabe CH, Pandarinath C, Stavisky SD, Rezaii PG, Saab J, Walter BL, Sweet JA, Miller JP, Henderson JM, Shenoy KV, Simeral J, Jarosiewicz B, Hochberg LR, Kirsch RF, Ajiboye AB (2019) Principled BCI decoder design and parameter selection using a feedback control model. Scientific Reports. 9(1):8881. pdf  supp_mats  url

114. Milekovic T, Bacher D, Sarma A, Simeral J, Saab J, Pandarinath C, Yvert B, Sorice B, Blabe C, Oakley E, Tringale K, Eskandar E, Cash S, Shenoy KV, Henderson JM, Hochberg LR, Donoghue JP (2019) Volitional control of single-electrode high gamma local field potentials (LFPs) by people with paralysis. Journal of Neurophysiology. 121:1428-1450. pdf  url

113. Even-Chen N*, Sheffer B*, Vyas S, Ryu SI, Shenoy KV (2019) Structure and variability of delay activity in premotor cortex. PLoS Computational Biology. 15(2): e1006808. pdf  url

112. Wang M*, Montanede C*, Chandrasekaran C, Peixoto D, Shenoy KV** & Kalaska JF** (2019) Macaque dorsal premotor cortex exhibits decision-related activity only when specific stimulus-response associations are known. Nature Communications10:1793 pdf  supp_matsurl

111. Chandrasekaran C*, Bray IE*, Shenoy KV (2019) Frequency shifts and depth dependence of premotor beta band activity during perceptual decision-making. Journal of Neuroscience39:1420-1435. pdf  url

110. Young D, Willett F, Memberg W, Murphy B, Rezaii PG, Walter B, Sweet J, Miller J, Shenoy KV, Hochberg LR, Kirsch R, Ajiboye AB (2019) Closed-loop cortical control of virtual reach and posture using cartesian and joint velocity commands. Journal of Neural Engineering. 16:026011 (14pp). pdf  url


2018

109. Nuyujukian P*, Sanabria JA*, Saab J*, Pandarinath C, Jarosiewicz B, Blabe C, Franco B, Mernoff ST, Eskandar EN, Simeral JD, Hochberg LR**, Shenoy KV**, Henderson JM** (2018) Cortical control of a tablet computer by people with paralysis. PLoS One13:e0204566 pdf  url

108. Stavisky SD, Kao JC, Nuyujukian P, Pandarinath C, Blabe C, Ryu SI, Hochberg LR, Henderson JM, Shenoy KV (2018) Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements. Scientific Reports.8:1635.7pdf  url

107. Pandarinath C, O'Shea DJ, Collins J, Jozefowicz R, Stavisky SD, Kao JC, Trautmann EM, Kaufman MT, Ryu SI, Hochberg LR, Henderson JM, Shenoy KV, Abbott LF, Sussillo D (2018) Inferring single-trial neural population dynamics using sequential auto-encoders. Nature Methods.15:805-815. pdf  supp_mats  url

  • Batista AP, DiCarlo JJ (2018) Deep learning reaches the motor system. Nature Methods. News & Views. 15:772-773. pdf​
  • Seminar talk: Sussillo D (3/222018) LFADS seminar talk, Simons Institute for the Theory of Computing meeting, UC Berkeley. Video. url
  • SuppVideo1 Generator initial states inferred by LFADS are organized with respect to kinematics of the upcoming reach. The video depicts the initial conditions vectors for each individual trial of the ‘Maze’ reaching task for monkey J, mapped onto a low-dimensional space (3D) via t-SNE (as in Fig. 2c). Each point represents the initial conditions vector for an individual trial (2,296 trials are shown). Colors denote the angle of the endpoint of the upcoming reach (colors shown in Fig. 2a), and marker types denote the curvature of the reach (circles, squares, and triangles for straight, counter-clockwise curved, and clockwise curved reaches, respectively). As shown, the initial conditions exhibit similarity for trials with similar kinematic trajectories (both for trials whose reach endpoints have similar angles and for trials with similar reach curvature). Since structure in the initial conditions implies structure at the level of the generator’s dynamics, this analysis implies that LFADS produces dynamic trajectories that show similarity based on the kinematics of the reach type for a given trial, despite LFADS not having any information about reaching conditions.
  • SuppVideo2 LFADS reveals consistent rotational dynamics on individual trials. The video contains two sequential movies showing the trajectories in neural population state space during individual reach trials for monkey J (Fig. 3). The first movie illustrates the single-trial trajectories uncovered by smoothing the data with a Gaussian kernel. The second movie illustrates single-trial trajectories uncovered by LFADS. 2,296 trials are shown, representing the 108 conditions of the ‘Maze’ task.
  • SuppVideo3 Multisession LFADS finds consistent representations for individual trials across sessions. The video contains six sequential movies showing the trajectories in state space during individual reach trials for monkey P (Fig. 4). The first video shows single-trial GPFA factor trajectories for all trials estimated for a single session. The second and third videos show single-trial LFADS factor trajectories estimated from all trials using a single-session model and from the stitched model, respectively. The fourth, fifth, and sixth repeat this sequence but show single-trial trajectories for 42/44 sessions (2 were omitted for ease of presentation). Colors represent eight reach directions. Multisession movies include approximately 14,500 trials, 38 separate electrode penetration sites and spanned 162 d from the first to the last session. Each trajectory begins at the go cue and proceeds for 510 ms into movement, which occurs at varying times due to reaction time variability. For GPFA and single-session. LFADS factors, the trajectories from individual sessions were concatenated and the projection yielding the CIS and first jPCA plane was estimated (Methods). This provided a set of common trajectories against which each individual session’s data were regressed. These regression coefficients provided projections of the individual sessions’ trajectories that were maximally similar to the common trajectories. In contrast, for stitched LFADS factors, we simply estimated the projection yielding the CIS and first jPCA plane from all of the sessions together, as the factors are already in shared space.
  • Some shared resources:
    • Pandarinath C, O’Shea DJ, Collins J, Jozefowicz R, Stavisky SD, Kao JC, Trautmann EM, Kaufman MT, Ryu SI, Hochberg LR, Henderson JM, Shenoy KV, Abbott LF, Sussillo D (2018) Open-source software, code and data for LFADS (Pandarinath et al. Nature Methods. 2018).
    • Data:

106. Williams AH, Kim TH, Wang F, Vyas S, Ryu SI, Shenoy KV, Schnitzer M, Kolda TG, Ganguli S (2018) Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor components analysis. Neuron.98:1-17 pdf  url

105. Milekovic T, Sarma A, Bacher D, Simeral J, Saab J, Pandarinath C, Sorice B, Blabe C, Oakley E, Tringale K, Eskandar E, Cash S, Henderson JM, Shenoy KV, Donoghue JP, Hochberg LR (2018) Stable long-term BCI-enabled communication in ALS and locked-in syndrome using LFP signals. Journal of Neurophysiology. 120:343-360. pdf  url

104. O’Shea DJ*, Kalanithi P*, Ferenczi E, Hsueh B, Chandrasekaran C, Goo W, Diester I, Ramakrishnan C, Kaufman MT, Ryu SI, Yeom KW, Deisseroth K**, Shenoy KV** (2018) Development of an optogenetic toolkit for neural circuit dissection in squirrel monkeys. Scientific Reports. 8:1-20. pdf  url

103. Vyas S, Even-Chen N, Stavisky SD, Ryu SI,  Nuyujukian P, Shenoy KV (2018) Neural population dynamics underlying motor learning transfer. Neuron97: 1-10. pdf  supp_mats  url

  • ​​Preview piece by Natraj N & Ganguly K (2018) Shaping reality through mental rehearsal. Neuron. 97:988-1000. pdf  url

102. O’Shea DJ, Shenoy KV (2018) ERAASR: An algorithm for removing electrical stimulation artifacts from multielectrode array recordings. Journal of Neural Engineering. 15:026020 (17pp) pdf  url  github

101. Willett FR, Murphy BA, Young D, Memberg WD, Blabe CH, Pandarinath C, Franco B, Saab J, Walter BL, Sweet JA, Miller JP, Henderson JM, Shenoy KV, Simeral JD, Jarosiewicz B, Hochberg LR, Kirsch RF, Ajiboye AB (2018) A comparison of intention estimation methods for decoder calibration in intracortical brain-computer interfaces. IEEE Transactions in Biomedical Engineering. 65:2066-2078. pdf  url

100. Even-Chen N, Stavisky SD, Pandarinath C, Nuyujukian P, Blabe CH, Hochberg LR, Henderson* JM, Shenoy* KV (2018) Feasibility of automatic error detect-and-undo system in human intracortical brain-computer interfaces. IEEE Transactions in Biomedical Engineering. 65:1771-1784. pdf  url

99. Brandman D, Hosman T, Saab J, Burkhart M, Shanahan B, Ciancibello J, Sarma A, Milstein D, Vargas-Irwin C, Franco B, Kelemen J, Blabe C, Murphy B, Young D, Willett F, Pandarinath C, Stavisky S, Kirsch R, Walter B, Ajiboye A, Cash S, Eskandar E, Miller J, Sweet J, Shenoy KV, Henderson JM, Jarosiewicz B, Harrison M, Simeral J, Hochberg, LR (2018) Rapid calibration of an intracortical brain computer interface for people with tetraplegia. Journal of Neural Engineering. 15:026007. pdf  url


2017

98. Even-Chen N, Stavisky S, Kao J, Ryu SI, Shenoy KV (2017) Augmenting intracortical brain-machine interface with neurally driven error detectors. Journal of Neural Engineering14:066007 (16pp). pdf  url

97. Chandrasekaran C, Peixoto D, Newsome WT, Shenoy KV (2017) Laminar differences in decision-related neural activity in dorsal premotor cortex. Nature Communications. 8:614. pdf  supp_mats  url

96. Kao JC, Ryu SI, Shenoy KV (2017) Leveraging neural dynamics to extend functional lifetime of brain-machine interfaces. Scientific Reports. 7: 7395:1-16. pdf  supp_mats  movie 1  movie 2  url 

95. Stavisky SD, Kao JC, Ryu SI, Shenoy KV (2017) Motor cortical visuomotor feedback activity is initially isolated from downstream targets in output-null neural state space dimensions. Neuron95:195-208. pdf  supp_mats  url

  • Preview piece by Scott S (2017) Stalling for time: It’s not the magnitude, but the way neurons fire that matters. Neuron. 95:6-8. pdf  url

94. Pandarinath C*, Nuyujukian P*, Blabe CH, Sorice B, Saab J, Willett F, Hochberg LR, Shenoy KV**, Henderson JM** (2017) High performance communication by people with paralysis using an intracortical brain-computer interface. eLife. 6:e18554 pdf  url

93. Willett FR, Murphy B, Memberg W, Blabe C, Pandarinath C, Walter B, Sweet J, Miller J, Henderson JM, Shenoy KV, Hochberg LR, Kirsch R, Ajiboye AB (2017) Signal-independent noise in intracortical brain-computer interfaces causes movement time properties inconsistent with Fitts' law. Journal of Neural Engineering. 14:026010.  pdf

92. Stavisky SD, Kao, JC, Ryu SI, Shenoy KV (2017) Trial-by-trial motor cortical correlates of a rapidly adapting visuomotor internal model. Journal of Neuroscience. 37:1721-1732. pdf

91. Kao JC*, Nuyujukian P*, Ryu SI, Shenoy KV (2017) A high-performance neural prosthesis incorporating discrete state selection with hidden Markov models. IEEE Transactions on Biomedical Engineering. 64:935-945. pdf  supp_mats

90. Nuyujukian P, Kao JC, Ryu SI, Shenoy KV (2017) A non-human primate brain computer typing interface. Proceedings of the IEEE.105:66-72. video abstract  pdf  video 1  video 2  video 3

89. Willett F, Pandarinath C, Jarosiewicz B, Murphy B, Memberg W, Blabe C, Saab J, Walter B, Sweet J, Miller J, Henderson J, Shenoy KV, Simeral J, Hochberg LR, Kirsch R, Ajiboye AB. (2017) Feedback control policies employed by people using intracortical brain-computer interfaces. Journal of Neural Engineering. 14:016001 (16pp). pdf

88. O'Shea DJ, Trautmann EM, Chandrasekaran C, Stavisky SD, Kao JC, Sahani M, Ryu SI, Deisseroth K, Shenoy KV (2017) The need for calcium imaging in nonhuman primates: New motor neuroscience and brain-machine interfaces. Experimental Neurology. 287:437-451pdf


2016

87. Sussillo D*, Stavisky SD*, Kao JC*, Ryu SI, Shenoy KV (2016) Making brain-machine interfaces robust to future neural variability. Nature Communications. 7:13749. doi: 10.1038/ncomms13749 pdf  supp_mats  video1  video2  video  captions

86. O'Shea DJ, Shenoy KV (2016) The importance of planning in motor learning. Neuron. 92:669-671. pdf  Preview Article

  • Preview piece on Sheahan HR, Franklin DF, Wolpert DM (2016) Motor planning, not execution, separates motor memories. Neuron. 92:772-779. pdf

85. Seely J, Kaufman MT, Ryu SI, Shenoy KV, Cunningham JP, Churchland MM (2016) Tensor analysis reveals distinct population structure that parallels the different computational roles of areas M1 and V1. PLoS Computational Biology. 12 (11): e1005164. doi:10.1371/journal.pcbi.1005164 pdf

84. Kaufman MT, Seely J, Ryu SI, Shenoy KV, Churchland MM (2016) The largest response component in motor cortex reflects movement timing but not movement type. eNeuro. 3(4) e0085-16.2016: 1-25. pdf url

  • Three-dimensional view of Figure 10A (dataset JAD1), rotating to display structure. Axis labeled CIS corresponds to CIS1.  Video1.mp4
  • Three-dimensional view of Figure 10C (dataset NAD), rotating to display structure.  Video2.mp4
  • Three-dimensional view of Figure 10A, bottom (dataset JAD1), with events unfolding over time. Movie starts 300 ms before target onset, and ends 400 ms after movement onset.  Video3.mp4
  •  Three-dimensional view of Figure 10C, bottom (dataset NAD), with events unfolding over time. Movie starts 300 ms before target onset, and ends 600 ms after movement onset.  Video4.mp4


2015

83. Jarosiewicz B, Sarma AA, Bacher D, Masse NY, Simeral JD, Sorice B, Oakley EM, Blabe C, Pandarinath C, Gilja V, Cash SS, Eskandar E, Friehs G, Henderson JM, Shenoy KV, Donoghue JP, Hochberg LR (2015) Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface. Science Translational Medicine. 7:1-10. pdf  supp_mats

82. Gilja V*, Pandarinath C*, Blabe CH, Nuyujukian P, Simeral JD, Sarma AA, Sorice BL, Perge JA, Jarosiewicz B, Hochberg LR, Shenoy KV**, Henderson JM** (2015) Clinical translation of a high performance neural prosthesis. Nature Medicine. 21:1142-1145. pdf  url

  • Supp_mats
  • Composite video of representative Radial-8 and mFitts1 task trials from participant T6.  Video1.mp4
  • Composite video of representative Radial-8 and mFitts1 task trials from participant T7.  Video2.mp4
  • A free-pace free-choice typing task with the Dasher keyboard interface.  Video3.mp4

81. Adamantidis A, Arber S, Bains JS, Bamberg E, Bonci A, Buzsáki G, Cardin JA, Costa RM, Dan Y, Goda Y, Graybiel AM, Häusser M, Hegemann P, Huguenard JR, Insel TR, Janak PH, Johnston D, Josselyn SA, Koch C, Kreitzer AC, Lüscher C, Malenka RC, Miesenböck G, Nagel G, Roska B, Schnitzer MJ, Shenoy KV, Soltesz I, Sternson SM, Tsien RW, Tsien RY, Turrigiano GG, Tye KM, Wilson RI (2015) Nature Neuroscience. 18:1202-1212. pdf  editorial  intro

80. Kao JC, Nuyujukian P, Cunningham JP, Churchland MM, Ryu SI, Shenoy KV (2015) Single-trial dynamics of motor cortex and their applications to brain-machine interfaces. Nature Communications. 6:7759. doi: 10.1038/ncomms8759. pdf  supp_mats  movie1

79. Blabe C, Gilja V, Chestek CA, Shenoy KV, Anderson K, Henderson JM (2015) Assessment of brain-machine interfaces from the perspective of people with paralysis. Journal of Neural Engineering. 12:043002. pdf

78. Pandarinath C, Gilja V, Blabe CH, Nuyujukian P, Sarma AA, Sorice BL, Eskandar EN, Hochberg LR, Henderson JM*, Shenoy KV* (2015) Neural population dynamics in human motor cortex during movements in people with ALS. eLife. 4:e07436. pdf  video1

77. Sussillo D, Churchland MM, Kaufman MT, Shenoy KV (2015) A neural network that finds a naturalistic solution for the production of muscle activity. Nature Neuroscience. 18:1025-1033. pdf  supp_figs  supp  table

76. Kaufman MT, Churchland MM, Ryu SI, Shenoy KV (2015) Vacillation, indecision and hesitation in moment-by-moment decoding of monkey motor cortex. eLife. 4:e04677.  pdf  supp_mats  video1  video2  video3

75. Stavisky SD, Kao JC, Nuyujukian P, Ryu SI, Shenoy KV (2015) A high performing brain-machine interface driven by low-frequency local field potentials alone and together with spikes. Journal of Neural Engineering. 12:036009. pdf  supp_mats  movie1  movie2  video_abstract

74. Christie B, Tat D, Irwin Z, Gilja V, Nuyujukian P, Foster J, Ryu SI, Shenoy KV, Thompson D, Chestek CA (2015) Comparison of spike sorting and thresholding of voltage waveforms for intracortical brain-machine interface performance. Journal of Neural Engineering.12:006019. pdf

73. Nuyujukian P, Fan JM, Kao JC, Ryu SI, Shenoy KV (2015) A high-performance keyboard neural prosthesis enabled by task optimization. IEEE Transactions on Biomedical Engineering. 62:21-29. pdf


2014

72. Nuyujukian P, Kao JC, Fan J, Stavisky S, Ryu SI, Shenoy KV (2014) Performance sustaining intracortical neural prostheses. Journal of Neural Engineering. 11:066003. pdf  supp_mats video_abstract

71. Foster JD, Nuyujukian P, Freifeld O, Gao H, Walker R, Ryu SI, Meng TH, Murmann B, Black MJ, Shenoy KV (2014) A freely-moving monkey treadmill model. Journal of Neural Engineering. 11:046020. pdf  supp_mats  video_abstract

70. Bishop WE, Chestek CA, Gilja V, Nuyujukian P, Foster JD, Ryu SI, Shenoy KV, Yu BM (2014) Self-recalibrating classifiers for intracortical brain computer interfaces. Journal of Neural Engineering. 11:026001. pdf

69. Kaufman MT, Churchland MM, Ryu SI, Shenoy KV (2014) Cortical activity in the null space: permitting preparation without movement. Nature Neuroscience. 17:440-448. pdf  supp_mats

  • Sanger TD, Kalaska JF (2014) News & Views: "Crouching Tiger, Hidden Dimensions." Nature Neurocience17:338-340. pdf

68. Ames KC, Ryu SI, Shenoy KV (2014) Neural dynamics of reaching following incorrect or absent motor preparation. Neuron. 81:438-451. pdf  supp_mats  movie1  movie2  movie3

67. Fan JM, Nuyujukian P, Kao JC, Chestek CA, Ryu SI, Shenoy KV (2014) Intention estimation in brain-machine interfaces. Journal of Neural Engineering. 11:016004. pdf

66. Shenoy KV, Carmena JM (2014) Combining decoder design and neural adaptation in brain-machine interfaces. Neuron. 84:665-680. pdf

  • ​​Video abstract by Neuron: "Shenoy and Carmena introduce the concept of a brain-machine interface, how to desigin a decoder algorithm to convert neural activity into prosthetic control signals, and how to engage neural adaptation and plasticity. Combining decoder design and neural adaptation may lead to better BMI performance, robustness, and generalization."​

65. Kao JC, Stavisky SD, Sussillo D, Nuyujukian P, Shenoy KV (2014) Information systems opportunities in brain-machine interface decoders. Proceedings of the IEEE. 102:666-682. pdfReview Article

64. Shenoy KV (2014) Prostheses: hopes & hurdles. Voices article on "Studying circuits with therapy in mind," by Shenoy KV, Mayberg H, Brown P, Delp S, Nirenberg S, Walsh V, Shannon RV. Cell. 156:861-863. pdfReview Article


2013

63. Mante V*, Sussillo D*, Shenoy KV, Newsome WT (2013) Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature. 503:78-84. pdf  supp_mats

  • Erlich JC, Brody CD (2013) News & Views: "What to do and how." Nature503:45-47. pdf

62. Cowley BR, Kaufman MT, Butler ZS, Churchland MM, Ryu SI, Shenoy KV, Yu BM (2013) DataHigh: Graphical user interface for visualizing and interacting with high-dimensional neural activity. Journal of Neural Engineering.10:066012. pdf

61. Kaufman MT, Churchland MM, Shenoy KV (2013) The roles of monkey M1 neuron classes in movement preparation and execution. Journal of Neurophysiology. 110:817-825. pdf

60. Ozden I, Wang J, Lu Y, May T, Lee J, Goo W, O’Shea DJ, Kalanithi P, Diester I, Diagne M, Deisseroth K, Shenoy KV, Nurmikko AV (2013) A Coaxial Optrode As Multifunction Write-Read Probe for Optogenetic Studies in Non-Human Primates. Journal of Neuroscience Methods. 291:142-154. pdf

59. Dethier J*, Nuyujukian P*, Ryu SI, Shenoy KV, Boahen K (2013) Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces. Journal of Neural Engineering. 10:036008 (12pp). pdf

58. Chestek CA, Gilja V, Blabe CH, Foster BL, Shenoy KV, Parvizi J, Henderson JM (2013) Hand posture classification using electrocorticography signals in the gamma band over human sensorimotor brain areas. Journal of Neural Engineering. 10:02602 (11pp). pdf

57. Shenoy KV, Sahani M, Churchland MM (2013) Cortical control of arm movements: A dynamical systems perspective. Annual Review of Neuroscience36:337-359. pdf  Review Article


2012

56. Gilja V*, Nuyujukian P*, Chestek CA, Cunningham JP, Yu BM, Fan JM, Churchland MM, Kaufman MT, Kao JC, Ryu SI, Shenoy KV (2012) A high-performance neural prosthesis enabled by control algorithm design. Nature Neuroscience. 15:1752-1757. pdf  supp_mats movie1  movie2  movie3  movie4  movie5  movie6

54. Churchland MM*, Cunningham JP*, Kaufman MT, Foster JD, Nuyujukian P, Ryu SI, Shenoy KV (2012) Neural population dynamics during reaching. Nature. 487:51-56. pdf  supp_mats  movie1  movie2  movie3  movie4  movie5

53. Gao H, Walker RM, Nuyujukian P, Makinwa KAA, Shenoy KV, Murmann B, Meng TH (2012) HermesE: A 96-channel full data rate direct neural interface in 0.13 um CMOS. IEEE Journal of Solid State Circuits. 47:1043-1054. pdf

52. Zhao M, Batista AP, Cunningham JP, Chestek CA, Rivera-Alvidrez Z, Kalmar R, Ryu SI, Shenoy KV, Iyengar S (2012) An L1-regularized logistic model for detecting short-term neuronal interactions. Journal of Computational Neuroscience. 32:479-497. pdf

51. Sussillo D, Nuyujukian P, Fan JM, Kao JC, Stavisky SD, Ryu SI, Shenoy KV (2012) A recurrent neural network for closed-loop intracortical brain-machine interface decoders. Journal of Neural Engineering. 9:026027. pdf  supp_mats

  • Movie1 ("Movie demonstrating the quality of the FORCE decoder. Monkey J, 2/4/2011.")
  • Movie2 ("Movie demonstrating the quality of the FORCE decoder. Monkey L, 2/10/2011.")

50. Shenoy KV, Nurmikko AV (2012) Brain models enabled by next-generation neurotechnology. Pulse Magazine, IEEE Engineering in Medicine and Biology Society. 3: 31-36. pdf

49. Schnitzer JJ (2012) 2010 DARPA neural engineering, science, and technology forum [Guest Editorial]. Pulse Magazine, IEEE EMBS3:10. pdf  Review Article


2011

48. Afshar A, Santhanam G, Yu BM, Ryu SI, Sahani M*, Shenoy KV* (2011) Single-trial neural correlates of arm movement preparation. Neuron. 71:555-564. pdf  supp_mats

  • Previews: Graziano MSA (2011) New insights into motor cortex. Neuron71:387-388. pdf

47.Chestek CA, Gilja V, Nuyujukian P, Foster JD, Fan JM, Kaufman MT, Churchland MM, Rivera-Alvidrez Z, Cunningham JP, Ryu SI, Shenoy KV (2011) Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex. Journal of Neural Engineering. 8:045005. pdf

46. O'Driscoll S, Shenoy KV, Meng TH (2011) Adaptive resolution ADC array for an implantable neural sensor. IEEE Transactions on Biomedical Circuits and Systems. 5:120-130. pdf

45. Cunningham JP, Nuyujukian P, Gilja V, Chestek CA, Ryu SI, Shenoy KV (2011) A closed-loop human simulator for investigating the role of feedback-control in brain-machine interfaces. Journal of Neurophysiology. 105:1932-1949. pdf

44. Diester I, Kaufman MT, Mogri M, Pashaie R, Goo W, Yizhar O, Ramakrishnan C, Deisseroth K, Shenoy KV (2011) An optogenetic toolbox designed for primates. Nature Neuroscience. 14:387-397. pdf  supp_mats

43. Gilja V, Chestek CA, Diester I, Henderson JM, Deisseroth K, Shenoy KV (2011) Challenges and opportunities for next-generation intra-cortically based neural prostheses. IEEE Transactions on Biomedical Engineering. 58:1891-1899. pdf   Review Article


2010

42. Churchland MM, Cunningham JP, Kaufman MT, Ryu SI, Shenoy KV (2010) Cortical preparatory activity: Representation of movement or first cog in a dynamical machine? Neuron. 68:387-400. pdf

41. Kaufman MT, Churchland MM, Santhanam G, Yu BM, Afshar A, Ryu SI, Shenoy KV (2010) The roles of monkey premotor neuron classes in movement preparation and execution. Journal of Neurophysiology. 104:799-810. pdf

40. Miranda H, Gilja V, Chestek CA, Shenoy KV, Meng TH (2010) HermesD: A high-rate long-range wireless transmission system for simultaneous multichannel neural recording applications. IEEE Transactions on Biomedical Circuits and Systems4:181-191. pdf

39. Churchland MM*, Yu BM*, Cunningham JP, Sugrue LP, Cohen MR, Corrado GS, Newsome WT, Clark AM, Hosseini P, Scott BB, Bradley DC, Smith MA, Kohn A, Movshon JA, Armstrong KM, Moore T, Chang SW, Snyder LH, Lisberger SG, Priebe NJ, Finn IM, Ferster D, Ryu SI, Santhanam G, Sahani M, Shenoy KV (2010) Stimulus onset quenches neural variability: a widespread cortical phenomenon. Nature Neuroscience. 13:369-378. pdf  supp_mats  supp_video1  supp_video2   supp_video3  supp_video4

38. Gilja V*, Chestek CA*, Nuyujukian P, Foster JD, Shenoy KV (2010) Autonomous head-mounted electrophysiology systems for freely-behaving primates. Current Opinion in Neurobiology. 20:676-686. pdf  Review Article

  • Schuman E, Zhuang X, (2010) Editorial overview -- Special section on New Technologies. Current Opinion in Neurobiology20:608-609. pdf  Review Article

2009

37. Cunningham JP, Gilja V, Ryu SI, Shenoy KV (2009) Methods for estimating neural firing rates and their application to brain-machine interfaces. Neural Networks, special issue on brain-machine interfaces. 22:1235-1246. pdf

36. Chestek CA*, Gilja V*, Nuyujukian P, Kier R, Solzbacher F, Ryu SI, Harrison RA, Shenoy KV (2009) HermesC: Low-power wireless neural recording system for freely moving primates. IEEE Transactions in Neural Systems and Rehabilitation Engineering, special issue on wireless neurotechnology. 17:330-338. pdf

  • Judy JW, Markovic D (2009) Guest Editorial -- Special section on wireless neural interfaces. IEEE Transactions in Neural Systems and Rehabilitation Engineering. 17:309-311. pdf  Review Article

35. Harrison RR, Kier RJ, Chestek CA, Gilja V, Nuyujukian P, Ryu SI, Gregor B, Solzbacher F, Shenoy KV (2009) Wireless neural recording with single low-power integrated circuit. IEEE Transactions in Neural Systems and Rehabilitation Engineering, special issue on wireless neurotechnology. 17:322-329. pdf

  • Judy JW, Markovic D (2009) Guest Editorial -- Special section on wireless neural interfaces. IEEE Transactions in Neural Systems and Rehabilitation Engineering. 17:309-311pdf  Review Article

34. Santhanam G, Yu BM, Gilja V, Afshar A, Ryu SI, Sahani M, Shenoy KV (2009) Factor-analysis methods for higher-performance neural prostheses. Journal of Neurophysiology. 102:1315-1330. pdf

33. Yu BM, Cunningham JP, Santhanam G, Ryu SI, Shenoy KV*, Sahani M* (2009) Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. Journal of Neurophysiology102:614-635. pdf

32. Ryu SI, Shenoy KV (2009) Human cortical prostheses: Lost in translation? Neurosurgical Focus, special issue on advances in brain-machine interfaces, Parag Patil guest editor. 27:E5. pdf  Review Article


2008

31. Cunningham JP, Yu BM, Gilja V, Ryu SI, Shenoy KV (2008) Toward optimal target placement for neural prosthetic devices. Journal of Neurophysiology. 100:3445-3457. pdf

30. Kemere C, Santhanam G, Yu BM, Afshar A, Ryu SI, Meng TH, Shenoy KV (2008) Detecting neural state transitions using hidden Markov models for motor cortical prostheses. Journal of Neurophysiology. 100:2441-2452.  pdf

29. Batista AP, Yu BM, Santhanam G, Ryu SI, Afshar A, Shenoy KV (2008) Cortical neural prosthesis performance improves when eye position is monitored. IEEE Transactions in Neural Systems and Rehabilitation Engineering. 16:24-31.  pdf

28. Linderman MD, Santhanam G, Kemere CT, Gilja V, O'Driscoll S, Yu BM, Afshar A, Ryu SI, Shenoy KV, Meng TH (2008) Signal processing challenges for neural prostheses. IEEE Signal Processing Magazine, special issue on brain-computer interfaces. 25:18-28.  pdf

  • Sajda P, Muller K-R, Shenoy KV (2008) Brain-computer interfaces [from the guest editors]. IEEE Signal Processing Magazine. 25:16-17pdf  Review Article

2007

​27. Santhanam G*, Linderman MD*, Gilja V, Afshar A, Ryu SI, Meng TH, Shenoy KV (2007) HermesB: A continuous neural recording system for freely behaving primates. IEEE Transactions in Biomedical Engineering. 54:2037-2050. pdf

26. Chestek CA*, Batista AP*, Santhanam G, Yu BM, Afshar A, Cunningham JP, Gilja V, Ryu SI, Churchland MM, Shenoy KV (2007) Single-neuron stability during repeated reaching in macaque premotor cortex. Journal of Neuroscience. 27:10742-10750.  pdf  supp_mats

25. Achtman N*, Afshar A*, Santhanam G, Yu BM, Ryu SI, Shenoy KV (2007) Free-paced high-performance brain-computer interfaces. Journal of Neural Engineering. 4:336-347. pdf

24. Batista AP, Santhanam G, Yu BM, Ryu SI, Afshar A, Shenoy KV (2007) Reference frames for reach planning in macaque dorsal premotor cortex. Journal of Neurophysiology. 98:966-983. pdf

23. Churchland MM, Shenoy KV (2007) Temporal complexity and heterogeneity of single-neuron activity in premotor and motor cortex. Journal of Neurophysiology97:4235-4257.  pdf

22. Yu BM, Kemere C, Santhanam G, Afshar A, Ryu SI, Meng TH, Sahani M*, Shenoy KV* (2007) Mixture of trajectory models for neural decoding of goal-directed movements. Journal of Neurophysiology. 97:3763-3780.  pdf  Cover article

21. Churchland MM, Shenoy KV (2007) Delay of movement caused by disruption of cortical preparatory activity. Journal of Neurophysiology. 97:348-359.  pdf

20. Churchland MM, Yu BM, Sahani M, Shenoy KV (2007) Techniques for extracting single-trial activity patterns from large-scale neural recordings. Current Opinion in Neurobiology, special issue on new technologies. 17:609-618.  pdf  Review article


2006

19. Churchland MM, Afshar A, Shenoy KV (2006) A central source of movement variability. Neuron. 52:1085-1096.  pdf  supp_mat

18. Churchland MM, Santhanam G, Shenoy KV (2006) Preparatory activity in premotor and motor cortex reflects the speed of the upcoming reach. Journal of Neurophysiology. 96:3130-3146.  pdf

  • Cisek P (2006) Preparing for speed. Focus on: "Preparatory activity in premotor and motor cortex reflects the speed of the upcoming reach." Journal of Neurophysiology. 96:2842-2843.  pdf

17. Santhanam G*, Ryu SI*, Yu BM, Afshar A, Shenoy KV (2006) A high-performance brain-computer interface. Nature. 442:195-198.  pdf  supp_mats  video1  video2  video3

  • Nature Web Focus (url) -- Brain-Machine Interfaces introduction: "Brain-machine interfaces promise to aid paralyzed patients by re-routing movement-related signals around damaged parts of the nervous system. A new study in Nature demonstrates a human with spinal injury manipulating a screen cursor and robotic devices by thought alone (Hochberg et al. Nature 442:164-171, 2006). Implanted electrodes in his motor cortex recorded neural activity, and translated it into movement commands. A second study, in monkeys, shows that brain-machine interfaces can operate at high speed, greatly increasing their clinical potential (Santhanam et al. Nature 442:195-198, 2006). This Nature Web Focus includes exclusive interviews and video footage of experiments, alongside papers that paved the way for these recent advances."Nature News & Views -- Scott SH (2006) Neuroscience: Converting thoughts into action. Nature 442:141-142.  pdf
  • Nature News Features -- Abbott A (2006) Neuroprosthetics: In search of the sixth sense. Nature 442:125-127.  pdf
  • Nature Editorial -- Is this the bionic man? Nature 442:109, 2006.  pdf
  • Nature News -- Hopkin M (2006) Bionic brains become a reality.  url
  • Nature Video Streaming -- Brain Machine Interfaces.  videos
  • Nature Podcast -- Smith C & Lacey A (2006)  audio  transcript
  • News articles lists selected items (e.g., NY TimesThe Economist, NPR Talk of the Nation Science Friday)
  • Barton S (2006) Neurological disorders: Mind over machine. Nature Reviews Neuroscience 7:682-683.  pdf

16. Churchland MM, Yu BM, Ryu SI, Santhanam G, Shenoy KV (2006) Neural variability in premotor cortex provides a signature of motor preparation. Journal of Neuroscience. 26(14):3697-3712.  pdf   supp_mats


2005

15. Zumsteg ZS, Kemere C, O'Driscoll S, Santhanam G, Ahmed RE, Shenoy KV, Meng TH (2005) Power feasibility of implantable digital spike sorting circuits for neural prosthetic systems. IEEE Transactions in Neural Systems and Rehabilitation Engineering. 13:272-279.  pdf


2004

14. Kemere C, Shenoy KV, Meng TH (2004) Model-based neural decoding of reaching movements: a maximum likelihood approach. IEEE Transactions on Biomedical Engineering. 51:925-932.  pdf


2003

13. Shenoy KV, Meeker D, Cao S, Kureshi SA, Pesaran B, Mitra P, Buneo C A, Batista AP, Burdick JW, Andersen RA (2003) Neural prosthetic control signals from plan activity. NeuroReport. 14:591-596.  pdf


2002

12. Shenoy KV, Crowell JA, Andersen RA (2002) Pursuit-Speed Compensation in Cortical Area MSTd. Journal of Neurophysiology.88:2630-2647.  pdf

11. Sugihara H, Murakami I, Shenoy KV, Andersen RA, Komatsu H (2002) Response of MSTd neurons to simulated 3D-orientation of rotating planes. Journal of Neurophysiology 87:273-285.  pdf


1999

10. Shenoy KV, Bradley DC, Andersen RA (1999) Influence of gaze rotation on the visual response of primate MSTd neurons. Journal of Neurophysiology. 81:2764-2786.  pdf


1998

9. Crowell JA, Banks MS, Shenoy KV, Andersen RA (1998) Visual self-motion perception during head turns. Nature Neuroscience. 1:732-737.  pdf

  • Nature Neuroscience​​News & Views -- Warren WH (1998) Perception of heading is a brain in the neck. Nature Neuroscience. 1:647-649.  pdf

1997

 8. Wang H, Luo J, Shenoy KV, Fonstad Jr. CG, Psaltis D (1997) Monolithic integration of SEEDs and VLSI GaAs circuits by epitaxy on electronics. IEEE Photonic Technology Letters. 9:607-609.  pdf


1996

7. Bradley DC, Maxwell M, Andersen RA, Banks MS, Shenoy KV (1996) Neural mechanisms for heading perception in primate visual cortex. Science. 273:1544-1547.  pdf

  • Science -- Barinaga M (1996) Neuroscience: Researchers find neurons that may help us navigate. Science. 273:1489-1490.  pdf

​6. Braun EK, Shenoy KV, Fonstad Jr. CG, Mikkelson JM (1996) Elevated temperature stability of GaAs digital integrated circuits. IEEE Electron Device Lett. 17:37-39.  pdf


1995

5. Shenoy KV, Fonstad Jr. CG, Grot AC, Psaltis D (1995) Monolithic optoelectronic circuit design and fabrication by epitaxial growth on commercial VLSI GaAs MESFETs. IEEE Photon. Technol. Lett. 7:508-510.  pdf


1994

4. Grot AC, Psaltis D, Shenoy KV, Fonstad Jr. CG (1994) Integration of LEDs and GaAs circuits by MBE regrowth. IEEE Photon. Technol. Lett. 6:819-821.  pdf

3. Shenoy KV, Fonstad Jr. CG, Mikkelson JM (1994) High temperature stability of refractory-metal VLSI GaAs MESFETs. IEEE Electron Device Lett. 15:106-108.  pdf

2. McGrann JV, Shaw GL, Shenoy KV, Matthews RB (1994) Computation by symmetry operations in a highly structured model of the brain. Phys. Rev. E. 49:5830-5839.  pdf


1993

1. Shenoy KV, Kaufman J, McGrann JV, Shaw GL (1993) Learning by selection in the Trion model of cortical organization. Cerebral Cortex. 3:239-248.  pdf

 

Journal Papers

Last modified: 
Monday, October 18, 2021 - 11:28 pm

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