​​This project explicitly aligns with two converging trajectories:

1. Bio-interface stimulation and recording (retinal stimulation today; optional future integration with brain interfaces such as Neuralink/Blindsight-class pipelines for higher-fidelity access to visual pathways), and

2. Neural decoding research (e.g., dream/imagery decoding demonstrations using fMRI/ML as a proof-of-principle that latent visual content can be inferred from neural signals—iNanoScope extends this concept by supplying a controlled stimulus and optimizing it with feedback).

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The provided photon image is the aesthetic/identity seed for the AngstromSoft visual language: a luminous, coherent “emission core” motif representing controlled photonic synthesis and angstrom-class ambition.

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Research Thesis

Thesis: If nanoscale sample interactions can be encoded into a time-resolved photonic stimulus (spectral + spatial + phase + modulation patterns) and delivered to a retinal stimulation array, then an iterative compute pipeline can optimize the stimulus such that the human visual system perceives stable, information-dense imagery corresponding to nanoscale structure—approaching angstrom-relevant interpretability through computational + neuro-perceptual amplification rather than mechanical scale.

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Two-Phase Program Structure

Phase 1 — Retinal Photonic Interface + Computational Rendering (Foundational)

Build a benchtop system that:

• Generates structured photonic stimuli (multi-wavelength, high refresh, controlled modulation).

• Presents stimuli through a retinal stimulation display path (non-implant external optical coupling and/or established retinal stimulation modalities).

• Measures response via eye tracking + pupillometry + EEG/MEG-adjacent surrogate signals (research-grade) to close the loop.

• Uses computational imaging to map “stimulus → perceived image quality” and optimize reconstruction fidelity.

Deliverable: iNanoScope v1 — a working closed-loop perception engine that renders synthetic nanoscale scenes (ground truth known) and demonstrably improves interpretability through iterative optimization.

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Phase 2 — Neural Interface Integration (Neuralink/Blindsight-Class Path)

Replace or augment retinal-level feedback with higher-bandwidth neural readout/write-in, enabling:

• Improved calibration of perceptual mapping (retina/cortex transfer functions).

• More precise decoding of what the user is actually “seeing” internally.

• A pathway to robust perception even when retinal optics become limiting.

Deliverable: iNanoScope v2 — a neural-calibrated perception system where stimulus design is optimized against decoded visual representations, not just external behavioral proxies.

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Core System Architecture

A) Nanoscale Information Acquisition (Front-End Encoders)

iNanoScope is compatible with multiple acquisition modalities. The key requirement is that the modality produces a signal that can be encoded into photonic stimulus space.

Candidate acquisition/encoder options:

• Optical near-field / evanescent approaches (NSOM-inspired constraints without copying legacy instrument assumptions).

• Interferometric/phase retrieval pipelines (convert phase information into renderable stimulus).

• Spectral signatures (multi-band reflectance/fluorescence; Raman-adjacent concepts as future expansions).

• Computed signal fusion from existing sensors (the platform can ingest external nanoscale datasets initially for development while hardware encoders mature).

Early strategy: start with synthetic + known datasets and incrementally bind real acquisition hardware once the perception loop is validated.

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B) Photonic Stimulus Synthesis (The “Perceptual Projector”)

Stimulus is the product. The engine generates:

• Spatiotemporal modulation (high refresh patterns, micro-contrast shaping).

• Spectral multiplexing (wavelength channels used as information carriers).

• Phase/temporal coding (where hardware permits) to carry sub-pixel cues.

This is where the AngstromSoft “glow core” concept becomes functional: controlled luminous emission as a data carrier.

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C) Bio-Interface Delivery (Retinal Stimulation Layer)

A safe, research-appropriate delivery path that prioritizes:

• High-resolution retinal targeting (within practical limits).

• Stable alignment (micro-saccade compensation).

• Calibrated luminance and spectral safety constraints.

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D) Feedback & Decoding (Closed-Loop Optimization)

Two tiers of feedback:

1. Behavioral/physio proxies: gaze stability, pupil response, task performance, subjective scoring.

2. Neural decoding (Phase 2): decoded latent visual representations used as an objective optimization target (dream/imagery decoding as precedent; brain-interface as the future high-bandwidth path).

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E) Reconstruction & Interpretation (Compute Core)

A model stack that:

• Solves inverse problems (deconvolution, phase retrieval, compressive sensing where relevant).

• Uses multi-frame super-resolution and Bayesian priors for stability.

• Produces human-interpretable renderings (not just raw sensor reconstructions).

• Outputs both “scientific view” and “perceptual view” as distinct products.

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Key Technologies (Detailed)

1) Computational Imaging & Inverse Methods

• Phase retrieval / interferometric reconstruction

• Multi-frame super-resolution and micro-motion exploitation (saccades become signal, not noise)

• Physics-informed reconstruction (explicit priors, forward models)

• Uncertainty quantification (confidence maps per pixel/feature)

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2) Neural-Perceptual Optimization (Human-in-the-Loop ML)

• Closed-loop stimulus optimization (reinforcement learning / Bayesian optimization)

• Perceptual loss functions (optimize for recognizability, edge stability, semantic consistency)

• Personal calibration models (each user has a unique transfer function retina→cortex)

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3) Retina-Targeted Display & Alignment

• High refresh micro-pattern projection

• Eye tracking + real-time warping (stabilize stimulus on retina)

• Safety-bounded luminance/spectral control

• Calibration routines (retinal map, distortion correction)

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4) Neural Decoding & Brain-Interface Path (Phase 2)

• Decoding pipelines inspired by dream/imagery reconstruction literature (used as conceptual validation)

• Neural interface integration plan (data acquisition, feature extraction, alignment with stimulus)

• Objective “decoded image similarity” metrics to guide stimulus refinement

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5) Data Infrastructure

• Dataset orchestration: synthetic nanoscale scenes → progressively real measurements

• Ground-truth benchmarking harness (resolution metrics, task performance, repeatability)

• Model registry and experiment tracking (reproducible science)

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Research Plan (Actionable Work Packages)

WP0 — Identity & Technical Spec Lock (1–2 weeks equivalent)

• Define “angstrom-relevant” success criteria: not just spatial resolution, but interpretability and repeatability.

• Establish the iNanoScope data formats, calibration standards, and safety envelopes.

• Convert the photon image motif into the AngstromSoft/iNanoScope brand asset set (logo/mark derived from the glow core).

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WP1 — Synthetic Pipeline Prototype (Compute-Only)

• Build the full closed loop in simulation:

  • Generate nanoscale scenes (atoms/lattices/defects/protein-like meshes as abstract targets).

  • Encode into stimulus patterns.

  • Simulate retinal response and noise.

  • Optimize stimulus to maximize reconstruction fidelity and perceptual metrics.

• Output: “iNanoScope Simulator v1” validating feasibility before hardware.

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WP2 — Retinal Delivery Bench Prototype (Phase 1 Hardware)

• Implement stable stimulus presentation with alignment compensation.

• Integrate eye tracking + calibration routines.

• Human factors: comfort, repeatability, safety constraints, session protocols.

• Output: demonstrable stable retinal stimulus delivery + measurement harness.

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WP3 — Closed-Loop Optimization with Humans (Phase 1 Validation)

• Run structured perception tasks:

  • detect edges/defects, classify patterns, compare to ground truth.

• Iterate model improvements based on real response data.

• Output: measurable improvement over baseline rendering without closed-loop optimization.

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WP4 — Real Acquisition Modality Binding (Incremental)

• Bind one real signal path (even if modest initially) to replace synthetic inputs.

• Demonstrate end-to-end: sample → encoded stimulus → perceived/decoded image.

• Output: “real-world iNanoScope demonstration” with traceable signal provenance.

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WP5 — Neural Interface Expansion (Phase 2)

• Add neural decoding objective:

  • decode stimulus-evoked representations (starting with non-invasive where practical; roadmap to implant-grade interfaces).

• Output: stimulus optimization guided by neural readout (Blindsight-class pathway).

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Success Metrics

Phase 1 (Perceptual Imaging):

• Stimulus stabilization accuracy on retina (arc-minute class targeting depending on hardware).

• Improvement curves: baseline vs optimized perception tasks (accuracy, time, confidence).

• Reconstruction fidelity to ground truth (SSIM/LPIPS-style metrics, plus task-based scoring).

• Repeatability across sessions and users after calibration.

Phase 2 (Neural Calibration/Decoding):

• Correlation between decoded visual representation and intended stimulus content.

• Reduction in required stimulus energy for equivalent perceptual clarity (efficiency metric).

• Higher-order perception: semantic stability, defect detection reliability, low-contrast feature resolution.

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Risk Register (High-Value, Addressed Up Front)

• Biological variability: solved via per-user calibration and adaptive models.

• Safety limits on stimulation: enforce strict luminance/spectral constraints; prioritize non-invasive methods first.

• “Seeing” vs “knowing”: ensure metrics include interpretability and decision performance, not just pretty reconstructions.

• Neural decoding generalization: treat decoding as an optimization signal, not a standalone truth oracle.

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Why Now (Strategic Rationale)

Multiple lines of modern progress converge on this concept: high-speed displays, eye-tracking stabilization, computational imaging maturity, and accelerating neural interface work.

 

Recent demonstrations that neural activity contains recoverable visual content validate the direction;

 

iNanoScope goes one step further

by supplying a controlled stimulus and optimizing it

 

Turning perception into an engineered channel rather than a Passive Endpoint.