Concept Graph & Resume using Claude 3 Opus | Chat GPT4 | Llama 3:
Resume:
1.-Existing BCIs are slow and unreliable, presenting the "Grand Canyon problem" - they are not trusted for critical tasks.
2.-The prevailing notion is that BCIs decode CNS signals to "read the mind", but this view has limitations.
3.-The brain acquires adaptive behaviors/skills which are normally produced by muscles. BCIs convert CNS activity into new non-muscular skills.
4.-BCI skills, like natural skills, are learned and acquired through practice, as evidenced by animal and human studies.
5.-BCIs aim to create new non-muscular skills, so the predominant approach should be neuroscience to integrate them into the CNS.
6.-A skill is not performed the same way each time - trajectories vary but the result is stable and predictable.
7.-The CNS changes continually throughout life via neurogenesis, synaptic changes, glial changes, etc. Skills are somehow maintained despite this.
8.-The CNS substrate of a skill is a distributed network of neurons and synapses, not a specific synaptic location.
9.-fMRI studies show skill acquisition involves overlapping plasticity in cortex, subcortical areas, cerebellum and spinal cord.
10.-Simple skills like knee-jerk reflex conditioning also involve a distributed network of brain and spinal cord plasticity.
11.-Key questions: How are skills maintained in a changing CNS? How do skill networks retain key attributes and negotiate properties?
12.-New paradigm: Skill networks have two special properties - 1) Changing continually to maintain key skill attributes, 2) Concurrently negotiating shared neurons/synapses.
13.-The aggregate process is a negotiation, keeping the CNS in a negotiated equilibrium that maintains skills, similar to a Nash equilibrium.
14.-The CNS substrate of a skill is termed a "hexer" - a network that produces and maintains a skill.
15.-Together, hexers maintain a negotiated equilibrium that enables skill preservation and acquisition - a property noted by Nikolai Bernstein in 1967.
16.-Hexers are named after the Greek word meaning "essentially perfect" to imply their special skill-maintaining properties.
17.-Examples of hexers include the finger flexion sequence learning network and the spinal reflex conditioning network.
18.-Hexers must negotiate as the nervous system is affected by acquisition of new asymmetric skills like discus throwing.
19.-Axon initial segment of spinal motor neurons, the final gatekeeper of behavior, is negotiated between hexers to maintain skills.
20.-Soleus H-reflex conditioning in rats shows negotiation between the new reflex hexer and old locomotor hexer to maintain symmetrical gait.
21.-A hexer is a network that produces a muscle-based skill and changes as needed to maintain the skill's key attributes.
22.-A BCI creates a synthetic hexer - a network of neurons, synapses and software that produces and maintains a BCI skill.
23.-Synthetic hexers currently lack advantages of natural hexers, limiting BCI reliability. Relevant neuroscience questions about hexers remain unanswered.
24.-Potential improvements: Appropriate starting point, better sensory feedback, signals from multiple areas, redundancy, ability to negotiate, ongoing updating.
25.-Creating better BCIs is foremost a neuroscience problem of integrating synthetic hexers into the natural hexer negotiated equilibrium.
26.-The talk covered work by many collaborators and was supported by several funding organizations. The late Dennis McFarlane was especially acknowledged.
27.-Key conclusions: BCIs create non-muscular skills. The CNS substrate of a skill is a plastic network called a hexer.
28.-Hexers change continually to maintain key skill attributes and keep the CNS in a negotiated equilibrium. BCIs create synthetic hexers.
29.-Synthetic hexers are currently slow and unreliable. Integrating them into the expanded negotiated equilibrium is key to improvement.
30.-While bioengineering and signal analysis are important, creating better BCIs is primarily a neuroscience problem. Difficult scientific issues remain unresolved.
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