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Neurotechnology & Human-Machine Interfaces

  • Exit capability

    Decode, model, stimulate, and close loops around nervous-system and physiological signals while treating adaptation, safety, identity, and user utility as first-class.

  • Frontier targets

    BCIs; neural prostheses; sensory augmentation; speech restoration; adaptive exoskeletons; memory interfaces.

  • Choose this track if

    Best if you want computation and machinery to become extensions of human capability.

Neural dynamics

Week 1: Biophysics of neurons

Reading/source: Neuronal Dynamics Ch. 1-2 + physiology membrane concepts

Know: Own membrane RC analogy, ionic currents, resting potential and spike-generation models.

Reconstruct: Derive leaky integrate-and-fire equation and time constant.

Do: Simulate current-to-spike response; compare LIF with richer nonlinear/spiking model.

Context: Neuronal Dynamics provides online theory plus Python exercises.

Defend: What biological features does LIF deliberately discard?

Gate: Pass if simplification is tied to intended decoding/control task.

Source: source

Week 2: Hodgkin-Huxley and excitability

Reading/source: Neuronal Dynamics Ch. 2-4

Know: Understand conductance-based dynamics, threshold, refractory behavior and adaptation.

Reconstruct: Reconstruct current-balance equation and gating-variable concept.

Do: Simulate HH-like model under current steps; map excitability regimes.

Context: Mechanistic models are useful for stimulation and interpretation even when decoders are learned.

Defend: Which parameters are physiologically identifiable from extracellular signals?

Gate: Pass if model/measurement observability is discussed.

Source: source

Coding

Week 3: Spike trains and neural coding

Reading/source: Neuronal Dynamics coding chapters

Know: Represent spike timing/rates, tuning curves and variability.

Reconstruct: Derive firing-rate estimate and Poisson likelihood/basic information intuition.

Do: Generate synthetic population code and decode stimulus from rate vs timing features.

Context: BCI design depends on what information neural activity carries and how stable it is.

Defend: What information did binning destroy?

Gate: Pass if decoder comparison uses same latency/data budget.

Source: source

Week 4: Population models and GLMs

Reading/source: Neuronal Dynamics GLM/population material

Know: Fit probabilistic encoding models and quantify predictive uncertainty.

Reconstruct: Derive Poisson-GLM log likelihood/gradient intuition.

Do: Fit GLM to synthetic spikes; inspect residuals and misspecification.

Context: Modern neural decoding often combines statistical models and deep learning.

Defend: Does predictive accuracy imply mechanistic truth?

Gate: Pass if residual structure is checked.

Source: source

Decoding

Week 5: State estimation from neural signals

Reading/source: Wave-1 estimation + Neuronal Dynamics

Know: Decode continuous/discrete intention while propagating uncertainty.

Reconstruct: Derive linear/Kalman decoder and Bayesian classification formulation.

Do: Build cursor/kinematic decoder from synthetic neural population data; add drift.

Context: BCIs are estimators embedded in feedback loops.

Defend: What state is user intending versus what state your labels assume?

Gate: Pass if calibration/uncertainty is reported, not accuracy only.

Source: source

Week 6: Adaptive decoders and nonstationarity

Reading/source: Neuronal plasticity + online learning

Know: Handle electrode/unit drift, learning, fatigue and nonstationary mappings.

Reconstruct: Derive recursive/online parameter update and forgetting-factor tradeoff.

Do: Simulate decoder drift; compare static, periodically recalibrated and adaptive models.

Context: Long-term practical BCIs must work outside one lab session.

Defend: When does adaptation chase noise or override user learning?

Gate: Pass if adaptation improves long-horizon utility without catastrophic instability.

Source: source

Interfaces

Week 7: Signal acquisition and artifacts

Reading/source: Wave-2 electronics/signals + neurophysiology

Know: Understand invasive/non-invasive signal bandwidth, noise, referencing and artifacts conceptually.

Reconstruct: Build full signal chain/noise budget from neural source to ADC/decoder.

Do: Simulate EMG/ECoG/EEG-like signals with line noise/motion artifact; design filtering without erasing target signal.

Context: Measurement modality constrains achievable information before ML begins.

Defend: Which 'neural' feature could actually be artifact?

Gate: Pass if artifact control/negative control is designed.

Source: source

Week 8: Latency, bandwidth and closed-loop utility

Reading/source: Control/information + BCI context

Know: Treat communication rate, latency, error correction and user correction as system variables.

Reconstruct: Derive simple bits/minute or task-time utility metric and latency stability effect.

Do: Closed-loop cursor/typing simulator: vary decoder error vs latency vs correction ability.

Context: Clinical usefulness is not captured by offline classification accuracy.

Defend: Would you choose a slower, more accurate decoder?

Gate: Pass if metric maps to user task, not abstract model score.

Source: source

Human systems

Week 9: Motor control and sensorimotor loops

Reading/source: A&P motor systems + control theory

Know: Understand spinal/cortical motor pathways, proprioception and feedback/feedforward control.

Reconstruct: Draw nested human-device control loops and delays.

Do: Model powered-assistance/exoskeleton control with human adaptation.

Context: Human operator is part of the controller, not an external disturbance.

Defend: Who adapts to whom?

Gate: Pass if coupled adaptation is represented.

Source: source

Week 10: Sensory coding and augmentation

Reading/source: A&P sensory systems + neural coding

Know: Map stimulus transduction, receptive fields and perceptual adaptation to artificial sensory channels.

Reconstruct: Derive dynamic-range/compression mapping for an augmented sensor.

Do: Design simulation converting IR/ultrasound sensor values into haptic/auditory code; quantify learnability/information.

Context: Sensory augmentation requires a human-learning channel, not just more sensors.

Defend: What makes a code intuitive versus merely decodable after training?

Gate: Pass if user-learning burden is an explicit metric.

Source: source

Stimulation

Week 11: Neural stimulation as control input

Reading/source: Neurophysiology + safety literature conceptually

Know: Understand stimulation as uncertain input to excitable/adaptive tissue; distinguish activation from desired function.

Reconstruct: Build simplified stimulus-response and safety-envelope model.

Do: Simulate closed-loop stimulation controller with uncertain thresholds/adaptation; no operational clinical settings.

Context: Bidirectional interfaces require both decoding and controlled perturbation.

Defend: What unintended populations/effects can the stimulation recruit?

Gate: Pass if control uses conservative constraint independent of performance objective.

Source: source

Replication

Week 12: Speech/motor BCI system reconstruction

Reading/source: NIH 2026 home speech-BCI summary + underlying public paper if available

Know: Backchain a real practical BCI from implant/signal -> decoder -> synthesis -> home use.

Reconstruct: Reconstruct architecture, training, latency and evaluation metrics from sources.

Do: Build reduced speech/sequence-decoding analogue on public/synthetic data.

Context: NIH reported July 2026 home use of a speech BCI by a person with paralysis.

Defend: What changed when the system moved from lab to home?

Gate: Replication Gate: distinguish reported evidence from your extrapolation.

Source: source

Physiology

Week 13: Cardiovascular and respiratory control

Reading/source: OpenStax A&P cardiovascular/respiratory chapters

Know: Understand circulation/gas exchange and compensatory control as engineered-system analogues.

Reconstruct: Derive cardiac output, oxygen delivery and ventilation mass-balance relations.

Do: Build lumped cardio-respiratory model under exercise/altitude/device assistance.

Context: Neurotech often interacts with whole-body physiology.

Defend: Which compensation hides device failure until late?

Gate: Pass if multiple feedback loops interact.

Source: source

Week 14: Endocrine and autonomic regulation

Reading/source: A&P endocrine/autonomic sections

Know: Model slow hormonal/autonomic loops and multi-timescale homeostasis.

Reconstruct: Derive negative-feedback hormone toy model.

Do: Simulate fast neural + slow endocrine control of one physiological variable.

Context: Human augmentation cannot assume one control timescale.

Defend: How does delay produce overshoot/oscillation in physiology?

Gate: Pass if timescale separation guides controller design.

Source: source

Week 15: Renal/metabolic homeostasis and artificial organs

Reading/source: A&P renal/fluid/electrolyte chapters

Know: Understand clearance, fluid balance and biochemical regulation relevant to artificial-organ interfaces.

Reconstruct: Derive clearance/mass-balance model.

Do: Simulate dialysis/insulin-like control abstraction with sensor delay and safety bounds.

Context: Artificial organs are cyber-physiological systems.

Defend: What does replacing average function miss about homeostatic regulation?

Gate: Pass if disturbance recovery and fault state are tested.

Source: source

Evaluation

Week 16: Clinical/user-centered endpoints

Reading/source: NIH BCI reports + causal/statistical core

Know: Define user utility, communication independence, fatigue, training burden and durability endpoints.

Reconstruct: Write target-trial/longitudinal evaluation for assistive neurotechnology.

Do: Design synthetic longitudinal dataset and analyze dropout/adaptation/confounding.

Context: Practical utility must survive months, context shifts and user priorities.

Defend: Whose definition of success is in the objective?

Gate: Pass if endpoint set includes patient/user-reported and functional measures.

Source: source

Ethics/safety

Week 17: Agency, privacy and mental data

Reading/source: Security/humanities core + neurotechnology

Know: Treat neural/physiological data as high-stakes signals with consent, access, inference and misuse risks.

Reconstruct: Build threat model and authorization/recourse map.

Do: Red-team a BCI data pipeline for inference, insider, model-update and device-control abuse.

Context: Neural interfaces collapse boundaries between measurement and intervention.

Defend: What data/inference should never be required to use the device?

Gate: Pass if local fail-safe and user override are explicit.

Source: source

Safety

Week 18: Closed-loop safety and graceful degradation

Reading/source: Control/safety + physiology

Know: Guarantee bounded behavior under decoder/stimulator/sensor failure.

Reconstruct: Derive invariant/safe-set constraint for simplified assistive controller.

Do: Inject drift, dropout, adversarial artifact and actuator saturation into closed-loop simulator.

Context: Useful neurotech must fail safely while biological state remains partially hidden.

Defend: Which failure mode is detectable before harm?

Gate: Pass if independent monitor catches unsafe command path.

Source: source

Research

Week 19: Failure mining in BCI/neuroprosthetic loops

Reading/source: All track traces/current literature

Know: Create taxonomy: signal, decoder, user adaptation, physiology, interface, task, safety.

Reconstruct: Build causal graph and rank by severity/frequency/tractability.

Do: Generate 100 simulated failure episodes and classify mechanisms.

Context: The research target should be persistent failure, not leaderboard fashion.

Defend: Which failures are fundamentally information-limited?

Gate: Pass if taxonomy predicts intervention class.

Source: source

Week 20: Reproduce an open neurotech analysis

Reading/source: Choose public neural dataset/paper

Know: Practice reproducible decoding/encoding research.

Reconstruct: Reconstruct preprocessing, train/test split, temporal leakage and metric.

Do: Reproduce central decoding result and rerun with session/subject-held-out split.

Context: Neural datasets are especially prone to non-independent samples.

Defend: Does performance survive new-day/new-subject evaluation?

Gate: Extension Gate: reproduction + leakage/OOD audit.

Source: source

Week 21: Independent adaptive-interface extension

Reading/source: Selected failure mechanism

Know: Design extension improving robust user utility rather than offline score.

Reconstruct: Write mechanism and expected benefit/cost.

Do: Implement adaptation, uncertainty, active calibration or feedback change; pre-register test.

Context: A good extension changes closed-loop behavior.

Defend: What user population/context could be harmed by your adaptation?

Gate: Pass if improvement holds under drift and user-model variation.

Source: source

Translation

Week 22: Hardware, packaging and long-term operations

Reading/source: Electronics/materials/manufacturing/safety core

Know: Backchain prototype to power, telemetry, packaging, calibration, maintenance and update lifecycle.

Reconstruct: Create reliability/maintenance/firmware-update trust model.

Do: Design field-deployment architecture for non-clinical BCI analogue or assistive interface.

Context: Home deployment exposes support/reliability problems hidden in lab studies.

Defend: Who restores safe function after software/hardware update failure?

Gate: Pass if lifecycle and rollback are explicit.

Source: source

Capstone

Week 23: Integrated human-machine interface

Reading/source: All track sources

Know: Integrate signal -> estimator -> controller/feedback -> human adaptation -> safety -> utility.

Reconstruct: Regenerate closed-loop architecture and objective hierarchy.

Do: Capstone: public/synthetic-data BCI, adaptive assistive controller or sensory augmentation simulator with failure tests.

Context: No claims of medical efficacy beyond evidence.

Defend: Where does the human compensate for your technology?

Gate: Systems Gate: signal-processing + control + human-factors + safety reviewers.

Source: source

Week 24: Research and deployment defense

Reading/source: NIH current reality + all work

Know: Produce roadmap that distinguishes lab capability, assistive utility and speculative augmentation.

Reconstruct: Write evidence ladder and kill/revision criteria.

Do: Technical paper + reproducible code + user-value metrics + privacy/safety case + 12-month program.

Context: Current speech BCI progress shows practical home use is becoming a real benchmark, not sci-fi alone.

Defend: What would make the user choose not to use your system?

Gate: Capstone Gate: technical reviewer + clinician/human-factors proxy + privacy/safety reviewer.

Source: source

Research gates

G1 Replication

Required performance: Reproduce public neural decoding/encoding result with strict session/subject/time split.

Minimum artifacts: Data/code; preprocessing; leakage audit; latency/calibration metrics.

Pass criterion: Offline random-split accuracy is insufficient.

G2 Extension

Required performance: Improve closed-loop user utility under drift/adaptation rather than benchmark score alone.

Minimum artifacts: Adaptive/uncertainty method; simulated user variation; OOD sessions; safety constraint.

Pass criterion: Must report latency, calibration, robustness and user-task metric.

G3 System Closure

Required performance: Close signal->estimator->feedback/stimulation abstraction->human adaptation->device lifecycle/safety.

Minimum artifacts: Noise/latency budget; privacy threat model; safe-state monitor; maintenance/update plan.

Pass criterion: Independent safety constraint cannot be overridden by learned policy.

G4 Research Defense

Required performance: Separate assistive evidence from speculative augmentation and defend user agency.

Minimum artifacts: Technical paper; longitudinal eval design; privacy/safety case; 12-month roadmap.

Pass criterion: Must answer why a user might rationally reject the system.

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