A Gentle Introduction to Neural Prostheses¶
- Introduction
- This section will acquaint readers with concrete use cases of neural prostheses.
- ENGRAM: The Tool
- This section will detail the background and features of ENGRAM.
- How to Build a Memory Prosthesis
- This section is an extensive walkthrough of how to build a memory prosthesis: a specific application of cortical prostheses.
- Conclusion
- This section will synthesize what we’ve learned into projections for the future of neural engineering.
- Additional Resources
- This section gives additional resources for the curious to explore.
Introduction¶
Our minds function best unconsciously. Only when we become conscious, however, can we take ownership of their processes —– and control them.
“Dementia is troubling because, at the same time as it erodes someone’s memory, it also eats away at th[e] capacity to create shared meaning. If someone cannot remember not just where the milk bottle goes, but what a milk bottle is for, then the shared pre-suppositions on which communication, meaning, and identity depend become badly strained [Leadbeater2015].
Dr. Pauley is a dementia neurologist treating patients with anterograde amnesia (the inability to form new memories) as a result of Alzheimer’s disease (AD), traumatic brain injury (TBI), and stroke.
Entry Vignette¶
To provide the reader with an inviting Introduction to the feel of the context in which the case takes place
Characters
The plan to provide narrative flow from the perspective of the user 1. “Dr. Pauley”: Dementia neurologist confronting concrete cases with new tools and unprecedented ethics. 2. Me: Addressed in asides to show my own growth - Impetus: Neuromancer. To walk upon an electronic ground. - Ungeneralized Idea: Cognitive states (epi./phen.) are upheld by multiscale neural activity - Generalized Idea: Time and space change, but the ground retains memory. 3. The CNE: Theodore Berger, Dong Song, Xiwei She 4. The Entrepreneurs:Bryan Johnson, Elon Musk 5. The Visionaries: Ed Boyden, Rajesh Rao 6. The Affected:Those with dementia 7. The Practiced:Epilepsy neurologists who already use RNS devices 8. The Concerned: FDA (regulators) and INS (ethicists)
An Introduction¶
To familiarize the reader with the central features including rationale and research procedures
An Extensive Narrative Description¶
To of the case(s) and its context, which may involve historical or organizational information important for understanding the case
The Computational Basis of Memory Encoding Engrams are memory codes stored someplace else than the hippocampus.
Draw from Additional Data Sources¶
Integrate with the researcher’s own interpretations of the issues and both confirming and disproving evidence are presented followed by the presentation of the overall case assertions
A Closing Vignette¶
As a way of cautoning the reader to the specific case context saying “I like to close on an experiential note, reminding the reader that this report is just one person’s encounter with a complex case”
ENGRAM: The Tool¶
Origins and Early Visions¶
Theodore Berger¶
CNE From rats to primates to humans
What is a Cortical Prosthesis? The General Architecture Replacement parts for the brain must be (1) truly biomimetic, (2) network models, (3) bidirectional, and (4) adaptive, both to individual patients and their disease progression [Berger2001].
The core concepts & underlying technologies of our lab (ML/NC/CL-DBS)
Berger had the vision
Song had the math
You must outline the end-user
Ed Boyden¶
Neural Coprocessors
Rajesh Rao¶
BTBI
Core Features¶
Data Containers¶
ID: All data from a single individual - Bin: Binary data - Cont: Continuous data - Events: Event data
Signal Comparison Module¶
For use comparing (1) within individuals (i.e. between channels) or between multiple individuals - Rats vs humans signal quality
Mathematical Modeling Techniques¶
Minimal Dependencies - Classic Multi-Input Multi-Output (MIMO) Modeling - Classic Memory Decoding (An L1-regularized logistic regression model) - Closed Loop Hippocampal Prosthesis
Integration with Other Software Packages¶
Tensorflow - Deep MIMO and MD Models
Vispy/Visbrain - Novel visualization techniques
Brainflow - Online analysis of OpenBCI streams
ROOTS - Realistic neural growth between functionally connected sources
Ethical Considerations¶
Coming soon…
Note
Ethical concerns with neural prostheses should differ considerably from DBS, aDBS, and clDBS. This paper builds on existing models and literature on implantable neurological devices to distill unique ethical concerns associated with the design, development, and implementation of neural prostheses. In doing so, we hope that the resulting recommendations will be of use to guide this emerging field of neural engineering as it matures.
For instance, a recent review of the ethical issues related to neuroprosthetics, Walter Glannon questions whether a hippocampal prosthesis could be integrated into the brain’s memory circuits to maintain important aspects of autobiographical memory, such as the interaction between emotional and episodic memory, selective meaning attribution, and place cell function (Glannon, 2016). In reference to case of neurodegenerative diseases such as Alzheimer’s disease, Fabrice Jotterand has also pointed out that restoring psychological continuity (i.e. memory encoding) to patients would not repair the memories lost to neurodegeneration—-and that clinicians have an obligation to help restore the integrity of the patient’s personal identity through a relational narrative with past events where memory had failed (Jotterand, 2019). As more generalizable conclusions are drawn about neural prostheses as a whole, however, a deeper understanding of the core technology behind these devices will be increasingly beneficial. Glannon: “A person with anterograde or retrograde amnesia for many years might have difficulty adjusting cognitively and emotionally to what could be a substantial change in the content of his mental states” (Glannon 2019, 164)].
In order to effectively design devices that intend to benefit disabled people, researchers must, as a matter of justice, begin to pay close attention to the actual needs and desires of their end-users (Goering & Klein, 2019). And what aspects of neural prostheses can UCD affect? [
Consider the following: 1. Identification of end users 2. Determination of timing and responsibility for end user engagement 3. Assessment of the significance of personal interactions with end users 4. Comparison of methods for obtaining end user views Principled considerations: 1. Specification of the values underlying BCI research (e.g., sophistication vs. accessibility) 2. Reflection on the ethical reasons to engage end user perspectives (Sullivan et al., 2018)] In order to be most effective, qualitative instruments should be used to account for potential phenomenological changes resulting from implanted devices, as well as patient preference information to inform later risk-benefit assessment (FDA, 2016; Gilbert et al., 2019).
In such cases, the role of scientists, clinicians, and engineers in risk assessment is to estimate the probability of a beneficial or adverse event based on data provided by sponsors or available in the published literature—-but patient input is what improves our estimates on the weight or importance of an event (Benz and Civillico, 2017).
How to Build a Memory Prosthesis¶
Coming soon…
Conclusion¶
A New Era of Open-Source Neuroscience¶
Coming soon…
Registries + Standardization: The Need for Speed¶
Coming soon…
Additional Resources¶
- CLARITY Technique (Karl Diesseroth)
Elephant (Electrophysiology Analysis Toolkit) is an emerging open-source, community centered library for the analysis of electrophysiological data in the Python programming language.
Neo is a Python package for working with electrophysiology data in Python, together with support for reading a wide range of neurophysiology file formats, including Spike2, NeuroExplorer, AlphaOmega, Axon, Blackrock, Plexon, Tdt, and support for writing to a subset of these formats plus non-proprietary formats including HDF5. [Garcia2014]
Neurotic is an app for Windows, macOS, and Linux that allows you to easily review and annotate your electrophysiology data and simultaneously captured video.
Ephyviewer is a Python library based on pyqtgraph for building custom viewers for electrophysiological signals, video, events, epochs, spike trains, data tables, and time-frequency representations of signals.
EEGLearn is a set of functions for supervised feature learning/classification of mental states from EEG based on “EEG images” idea. [Bashivan2016]
Wagner Lab is a memory lab at Stanford University that releases all of their code with extensive documentation and enough functionality to reproduce publication results. [Gagnon2018] [Waskom2017]
References¶
| [BRYC16] | Pouya Bashivan, Irina Rish, Mohammed Yeasin, and Noel Codella. Learning representations from EEG with deep recurrent-convolutional neural networks. In 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings. 2016. arXiv:1511.06448. |
| [BBB+01] | T W Berger, M Baudry, R D Brinton, J S Liaw, V Z Marmarelis, and A Y Park. Brain-implantable biomimetic electronics as the next era in neural prosthetics. Proceedings of the. Ieee, 89(7):993–1012, 2001. |
| [GKM+18] | G. Gagnon, S. Kumar, J. R. Maltais, A. N. Voineskos, B. H. Mulsant, and T. K. Rajji. Superior memory performance in healthy individuals with subclinical psychotic symptoms but without genetic load for schizophrenia. Schizophrenia Research: Cognition, 2018. doi:10.1016/j.scog.2018.06.001. |
| [GGJ+14] | Samuel Garcia, Domenico Guarino, Florent Jaillet, Todd Jennings, Robert Pröpper, Philipp L. Rautenberg, Chris C. Rodgers, Andrey Sobolev, Thomas Wachtler, Pierre Yger, and Andrew P. Davison. Neo: An object model for handling electrophysiology data in multiple formats. Frontiers in Neuroinformatics, 2014. doi:10.3389/fninf.2014.00010. |
| [Lea15] | Charles Leadbeater. The Disremembered. 2015. URL: https://aeon.co/essays/if-your-memory-fails-are-you-still-the-same-person. |
| [WFW17] | Michael L. Waskom, Michael C. Frank, and Anthony D. Wagner. Adaptive Engagement of Cognitive Control in Context-Dependent Decision Making. Cerebral cortex (New York, N.Y. : 1991), 2017. doi:10.1093/cercor/bhv333. |