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8. Recent Technological Advances in Natural Language Processing and Artificial Intelligence

Author

Shah, Nishal Pradeepkumar

Subjects
Computer Science - Computation and Language, I.2.7
Abstract

A recent advance in computer technology has permitted scientists to implement and test algorithms that were known from quite some time (or not) but which were computationally expensive. Two such projects are IBM's Jeopardy as a part of its DeepQA project [1] and Wolfram's Wolframalpha[2]. Both these methods implement natural language processing (another goal of AI scientists) and try to answer questions as asked by the user. Though the goal of the two projects is similar, both of them have a different procedure at it's core. In the following sections, the mechanism and history of IBM's Jeopardy and Wolfram alpha has been explained followed by the implications of these projects in realizing Ray Kurzweil's [3] dream of passing the Turing test by 2029. A recipe of taking the above projects to a new level is also explained., Comment: 6 pages,0 figures

Published
2012

9. Decomposition of retinal ganglion cell electrical images for cell type and functional inference

Author

Wu, Eric Gene, primary, Rudzite, Andra M, additional, Bohlen, Martin O, additional, Li, Peter H, additional, Kling, Alexandra, additional, Cooler, Sam, additional, Rhoades, Colleen E, additional, Brackbill, Nora, additional, Gogliettino, Alex R, additional, Shah, Nishal P, additional, Madugula, Sasidhar S, additional, Sher, Alexander, additional, Litke, Alan M, additional, Field, Greg D, additional, and Chichilnisky, E.J., additional

Published
2023
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12. Inferring light responses of primate retinal ganglion cells using intrinsic electrical signatures

Author

Zaidi, Moosa, primary, Aggarwal, Gorish, additional, Shah, Nishal P, additional, Karniol-Tambour, Orren, additional, Goetz, Georges, additional, Madugula, Sasidhar S, additional, Gogliettino, Alex R, additional, Wu, Eric G, additional, Kling, Alexandra, additional, Brackbill, Nora, additional, Sher, Alexander, additional, Litke, Alan M, additional, and Chichilnisky, E J, additional

Published
2023
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14. Efficient Modeling and Calibration of Multi-Electrode Stimuli for Epiretinal Implants

Author

Vasireddy, Praful K., primary, Gogliettino, Alex R., additional, Brown, Jeff B., additional, Vilkhu, Ramandeep S., additional, Madugula, Sasi S., additional, Phillips, A. J., additional, Mitral, Subhasish, additional, Hottowy, Pawel, additional, Sher, Alexander, additional, Litke, Alan, additional, Shah, Nishal P., additional, and Chichilnisky, E.J., additional

Published
2023
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17. Data Compression Versus Signal Fidelity Tradeoff in Wired-OR Analog-to-Digital Compressive Arrays for Neural Recording

Author

Yan, Pumiao (author), Akhoundi, A. (author), Shah, Nishal P. (author), Tandon, Pulkit (author), Muratore, D.G. (author), Chichilnisky, E. J. (author), Murmann, Boris (author), Yan, Pumiao (author), Akhoundi, A. (author), Shah, Nishal P. (author), Tandon, Pulkit (author), Muratore, D.G. (author), Chichilnisky, E. J. (author), and Murmann, Boris (author)

Abstract

Future high-density and high channel count neural interfaces that enable simultaneous recording of tens of thousands of neurons will provide a gateway to study, restore and augment neural functions. However, building such technology within the bit-rate limit and power budget of a fully implantable device is challenging. The wired-OR compressive readout architecture addresses the data deluge challenge of a high channel count neural interface using lossy compression at the analog-to-digital interface. In this article, we assess the suitability of wired-OR for several steps that are important for neuroengineering, including spike detection, spike assignment and waveform estimation. For various wiring configurations of wired-OR and assumptions about the quality of the underlying signal, we characterize the trade-off between compression ratio and task-specific signal fidelity metrics. Using data from 18 large-scale microelectrode array recordings in macaque retina ex vivo, we find that for an event SNR of 7-10, wired-OR correctly detects and assigns at least 80% of the spikes with at least 50× compression. The wired-OR approach also robustly encodes action potential waveform information, enabling downstream processing such as cell-type classification. Finally, we show that by applying an LZ77-based lossless compressor (gzip) to the output of the wired-OR architecture, 1000× compression can be achieved over the baseline recordings., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Bio-Electronics

Published
2023
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18. Focal electrical stimulation of human retinal ganglion cells for vision restoration

Author

Madugula, Sasidhar S, primary, Gogliettino, Alex R, additional, Zaidi, Moosa, additional, Aggarwal, Gorish, additional, Kling, Alexandra, additional, Shah, Nishal P, additional, Brown, Jeff B, additional, Vilkhu, Ramandeep, additional, Hays, Madeline R, additional, Nguyen, Huy, additional, Fan, Victoria, additional, Wu, Eric G, additional, Hottowy, Pawel, additional, Sher, Alexander, additional, Litke, Alan M, additional, Silva, Ruwan A, additional, and Chichilnisky, E J, additional

Published
2022
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20. Precise control of neural activity using temporally dithered and spatially multiplexed electrical stimulation

Author

Shah, Nishal P., primary, Madugula, Sasi, additional, Phillips, A.J., additional, Grosberg, Lauren, additional, Gogliettino, Alex R., additional, Dusi, Aditya, additional, Tandon, Pulkit, additional, Brown, Jeff, additional, Hottowy, Pawel, additional, Dabrowski, Wladyslaw, additional, Sher, Alexander, additional, Litke, Alan M., additional, Mitra, Subhasish, additional, and Chichilnisky, E.J., additional

Published
2022
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21. Data Compression versus Signal Fidelity Trade-off in Wired-OR ADC Arrays for Neural Recording

Author

Yan, Pumiao (author), Shah, Nishal P. (author), Muratore, D.G. (author), Tandon, Pulkit (author), Chichilnisky, E.J. (author), Murmann, Boris (author), Yan, Pumiao (author), Shah, Nishal P. (author), Muratore, D.G. (author), Tandon, Pulkit (author), Chichilnisky, E.J. (author), and Murmann, Boris (author)

Abstract

This paper investigates the efficacy of a wired-OR compressive readout architecture for neural recording, which enables simultaneous data compression of action potential signals for high channel count electrode arrays. We consider a range of wiring configurations to assess the trade-offs between compression ratio and various task-specific signal fidelity metrics. We consider the fidelity in threshold crossing detection, spike assignment, and waveform estimation, and find that for an event SNR of 7-10 the readout captures at least 80% of the spike waveforms at ∼150x data compression., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Bio-Electronics

Published
2022
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22. Inference of Electrical Stimulation Sensitivity from Recorded Activity of Primate Retinal Ganglion Cells.

Author

Madugula, Sasidhar S., Vilkhu, Ramandeep, Shah, Nishal P., Grosberg, Lauren E., Kling, Alexandra, Gogliettino, Alex R., Huy Nguyen, Hottowy, Paweł, Sher, Alexander, Litke, Alan M., and Chichilnisky, E. J.

Subjects
RETINAL ganglion cells, ELECTRIC stimulation, NEURAL circuitry, PRIMATES, RETINA, RETINAL blood vessels, CONTRAST sensitivity (Vision)
Abstract

High-fidelity electronic implants can in principle restore the function of neural circuits by precisely activating neurons via extracellular stimulation. However, direct characterization of the individual electrical sensitivity of a large population of target neurons, to precisely control their activity, can be difficult or impossible. A potential solution is to leverage biophysical principles to infer sensitivity to electrical stimulation from features of spontaneous electrical activity, which can be recorded relatively easily. Here, this approach is developed and its potential value for vision restoration is tested quantitatively using large-scale multielectrode stimulation and recording from retinal ganglion cells (RGCs) of male and female macaque monkeys ex vivo. Electrodes recording larger spikes from a given cell exhibited lower stimulation thresholds across cell types, retinas, and eccentricities, with systematic and distinct trends for somas and axons. Thresholds for somatic stimulation increased with distance from the axon initial segment. The dependence of spike probability on injected current was inversely related to threshold, and was substantially steeper for axonal than somatic compartments, which could be identified by their recorded electrical signatures. Dendritic stimulation was largely ineffective for eliciting spikes. These trends were quantitatively reproduced with biophysical simulations. Results from human RGCs were broadly similar. The inference of stimulation sensitivity from recorded electrical features was tested in a data-driven simulation of visual reconstruction, revealing that the approach could significantly improve the function of future high-fidelity retinal implants. [ABSTRACT FROM AUTHOR]

Published
2023
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23. Inferring light responses of primate retinal ganglion cells using intrinsic electrical signatures

Author

Zaidi, Moosa, primary, Aggarwal, Gorish, additional, Shah, Nishal P., additional, Karniol-Tambour, Orren, additional, Goetz, Georges, additional, Madugula, Sasi, additional, Gogliettino, Alex R., additional, Wu, Eric G., additional, Kling, Alexandra, additional, Brackbill, Nora, additional, Sher, Alexander, additional, Litke, Alan M., additional, and Chichilnisky, E.J., additional

Published
2022
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28. Focal Electrical Stimulation of Human Retinal Ganglion Cells for Vision Restoration

Author

Madugula, Sasidhar S., primary, Gogliettino, Alex R., additional, Zaidi, Moosa, additional, Aggarwal, Gorish, additional, Kling, Alexandra, additional, Shah, Nishal P., additional, Vilkhu, Ramandeep, additional, Hays, Madeline R., additional, Nguyen, Huy, additional, Fan, Victoria, additional, Wu, Eric G., additional, Hottowy, Pawel, additional, Sher, Alexander, additional, Litke, Alan M., additional, Silva, Ruwan A., additional, and Chichilnisky, E.J., additional

Published
2020
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37. Fixational Eye Movements Enhance the Precision of Visual Information Transmitted by the Primate Retina.

Author

Wu EG, Brackbill N, Rhoades C, Kling A, Gogliettino AR, Shah NP, Sher A, Litke AM, Simoncelli EP, and Chichilnisky EJ

Abstract

Fixational eye movements alter the number and timing of spikes transmitted from the retina to the brain, but whether these changes enhance or degrade the retinal signal is unclear. To quantify this, we developed a Bayesian method for reconstructing natural images from the recorded spikes of hundreds of retinal ganglion cells (RGCs) in the macaque retina (male), combining a likelihood model for RGC light responses with the natural image prior implicitly embedded in an artificial neural network optimized for denoising. The method matched or surpassed the performance of previous reconstruction algorithms, and provides an interpretable framework for characterizing the retinal signal. Reconstructions were improved with artificial stimulus jitter that emulated fixational eye movements, even when the eye movement trajectory was assumed to be unknown and had to be inferred from retinal spikes. Reconstructions were degraded by small artificial perturbations of spike times, revealing more precise temporal encoding than suggested by previous studies. Finally, reconstructions were substantially degraded when derived from a model that ignored cell-to-cell interactions, indicating the importance of stimulus-evoked correlations. Thus, fixational eye movements enhance the precision of the retinal representation., Competing Interests: 9Competing Interests Statement The authors declare no competing interests.

Published
2024
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38. Gesture encoding in human left precentral gyrus neuronal ensembles.

Author

Vargas-Irwin CE, Hosman T, Gusman JT, Pun TK, Simeral JD, Singer-Clark T, Kapitonava A, Nicolas C, Shah NP, Avansino D, Kamdar F, Williams Z, Henderson JM, and Hochberg LR

Abstract

Understanding the cortical activity patterns driving dexterous upper limb motion has the potential to benefit a broad clinical population living with limited mobility through the development of novel brain-computer interface (BCI) technology. The present study examines the activity of ensembles of motor cortical neurons recorded using microelectrode arrays in the dominant hemisphere of two BrainGate clinical trial participants with cervical spinal cord injury as they attempted to perform a set of 48 different hand gestures. Although each participant displayed a unique organization of their respective neural latent spaces, it was possible to achieve classification accuracies of ~70% for all 48 gestures (and ~90% for sets of 10). Our results show that single unit ensemble activity recorded in a single hemisphere of human precentral gyrus has the potential to generate a wide range of gesture-related signals across both hands, providing an intuitive and diverse set of potential command signals for intracortical BCI use.

Published
2024
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39. A flexible intracortical brain-computer interface for typing using finger movements.

Author

Shah NP, Willsey MS, Hahn N, Kamdar F, Avansino DT, Fan C, Hochberg LR, Willett FR, and Henderson JM

Abstract

Keyboard typing with finger movements is a versatile digital interface for users with diverse skills, needs, and preferences. Currently, such an interface does not exist for people with paralysis. We developed an intracortical brain-computer interface (BCI) for typing with attempted flexion/extension movements of three finger groups on the right hand, or both hands, and demonstrated its flexibility in two dominant typing paradigms. The first paradigm is "point-and-click" typing, where a BCI user selects one key at a time using continuous real-time control, allowing selection of arbitrary sequences of symbols. During cued character selection with this paradigm, a human research participant with paralysis achieved 30-40 selections per minute with nearly 90% accuracy. The second paradigm is "keystroke" typing, where the BCI user selects each character by a discrete movement without real-time feedback, often giving a faster speed for natural language sentences. With 90 cued characters per minute, decoding attempted finger movements and correcting errors using a language model resulted in more than 90% accuracy. Notably, both paradigms matched the state-of-the-art for BCI performance and enabled further flexibility by the simultaneous selection of multiple characters as well as efficient decoder estimation across paradigms. Overall, the high-performance interface is a step towards the wider accessibility of BCI technology by addressing unmet user needs for flexibility.

Published
2024
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40. A real-time, high-performance brain-computer interface for finger decoding and quadcopter control.

Author

Willsey MS, Shah NP, Avansino DT, Hahn NV, Jamiolkowski RM, Kamdar FB, Hochberg LR, Willett FR, and Henderson JM

Abstract

People with paralysis express unmet needs for peer support, leisure activities, and sporting activities. Many within the general population rely on social media and massively multiplayer video games to address these needs. We developed a high-performance finger brain-computer-interface system allowing continuous control of 3 independent finger groups with 2D thumb movements. The system was tested in a human research participant over sequential trials requiring fingers to reach and hold on targets, with an average acquisition rate of 76 targets/minute and completion time of 1.58 ± 0.06 seconds. Performance compared favorably to previous animal studies, despite a 2-fold increase in the decoded degrees-of-freedom (DOF). Finger positions were then used for 4-DOF velocity control of a virtual quadcopter, demonstrating functionality over both fixed and random obstacle courses. This approach shows promise for controlling multiple-DOF end-effectors, such as robotic fingers or digital interfaces for work, entertainment, and socialization.

Published
2024
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41. Decomposition of retinal ganglion cell electrical images for cell type and functional inference.

Author

Wu EG, Rudzite AM, Bohlen MO, Li PH, Kling A, Cooler S, Rhoades C, Brackbill N, Gogliettino AR, Shah NP, Madugula SS, Sher A, Litke AM, Field GD, and Chichilnisky EJ

Abstract

Identifying neuronal cell types and their biophysical properties based on their extracellular electrical features is a major challenge for experimental neuroscience and the development of high-resolution brain-machine interfaces. One example is identification of retinal ganglion cell (RGC) types and their visual response properties, which is fundamental for developing future electronic implants that can restore vision. The electrical image (EI) of a RGC, or the mean spatio-temporal voltage footprint of its recorded spikes on a high-density electrode array, contains substantial information about its anatomical, morphological, and functional properties. However, the analysis of these properties is complex because of the high-dimensional nature of the EI. We present a novel optimization-based algorithm to decompose electrical image into a low-dimensional, biophysically-based representation: the temporally-shifted superposition of three learned basis waveforms corresponding to spike waveforms produced in the somatic, dendritic and axonal cellular compartments. Large-scale multi-electrode recordings from the macaque retina were used to test the effectiveness of the decomposition. The decomposition accurately localized the somatic and dendritic compartments of the cell. The imputed dendritic fields of RGCs correctly predicted the location and shape of their visual receptive fields. The inferred waveform amplitudes and shapes accurately identified the four major primate RGC types (ON and OFF midget and parasol cells), a substantial advance. Together, these findings may contribute to more accurate inference of RGC types and their original light responses in the degenerated retina, with possible implications for other electrical imaging applications.

Published
2023
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42. Pseudo-linear Summation explains Neural Geometry of Multi-finger Movements in Human Premotor Cortex.

Author

Shah NP, Avansino D, Kamdar F, Nicolas C, Kapitonava A, Vargas-Irwin C, Hochberg L, Pandarinath C, Shenoy K, Willett FR, and Henderson J

Abstract

How does the motor cortex combine simple movements (such as single finger flexion/extension) into complex movements (such hand gestures or playing piano)? Motor cortical activity was recorded using intracortical multi-electrode arrays in two people with tetraplegia as they attempted single, pairwise and higher order finger movements. Neural activity for simultaneous movements was largely aligned with linear summation of corresponding single finger movement activities, with two violations. First, the neural activity was normalized, preventing a large magnitude with an increasing number of moving fingers. Second, the neural tuning direction of weakly represented fingers (e.g. middle) changed significantly as a result of the movement of other fingers. These deviations from linearity resulted in non-linear methods outperforming linear methods for neural decoding. Overall, simultaneous finger movements are thus represented by the combination of individual finger movements by pseudo-linear summation.

Published
2023
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43. A brain-computer typing interface using finger movements.

Author

Shah NP, Willsey MS, Hahn N, Kamdar F, Avansino DT, Hochberg LR, Shenoy KV, and Henderson JM

Abstract

Intracortical brain computer interfaces (iBCIs) decode neural activity from the cortex and enable motor and communication prostheses, such as cursor control, handwriting and speech, for people with paralysis. This paper introduces a new iBCI communication prosthesis using a 3D keyboard interface for typing using continuous, closed loop movement of multiple fingers. A participant-specific BCI keyboard prototype was developed for a BrainGate2 clinical trial participant (T5) using neural recordings from the hand-knob area of the left premotor cortex. We assessed the relative decoding accuracy of flexion/extension movements of individual single fingers (5 degrees of freedom (DOF)) vs. three groups of fingers (thumb, index-middle, and ring-small fingers, 3 DOF). Neural decoding using 3 independent DOF was more accurate (95%) than that using 5 DOF (76%). A virtual keyboard was then developed where each finger group moved along a flexion-extension arc to acquire targets that corresponded to English letters and symbols. The locations of these letter/symbols were optimized using natural language statistics, resulting in an approximately a 2× reduction in distance traveled by fingers on average compared to a random keyboard layout. This keyboard was tested using a simple real-time closed loop decoder enabling T5 to type with 31 symbols at 90% accuracy and approximately 2.3 sec/symbol (excluding a 2 second hold time) on average.

Published
2023
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