# Deep Dream

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Description: Deep Dream explains how machine-vision hallucination, community visual taxonomies, VR research, and cyberdelic systems shaped simulated psychedelic vision.
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---

Simulated psychedelic vision

# Deep Dream

 Psychedelic visual simulation is the attempt to make altered vision
 visible from the outside: breathing surfaces, tracers, lattices,
 tunnels, recursive textures, faces in noise, and scene-like imagery
 translated into images, video, VR, shaders, neural networks, and
 toolkits. DeepDream is one of the central bridges in that story. It
 began as Google's 2015 way of looking inside a vision network, became
 a viral image style, was stabilized by open-source video developers,
 and then entered laboratory VR as the Hallucination Machine
 ([Mordvintsev et al., 2015](https://research.google/blog/inceptionism-going-deeper-into-neural-networks/);
 [Suzuki et al., 2017](https://doi.org/10.1038/s41598-017-16316-2)).
 But the field around it is larger than DeepDream. Community taxonomies,
 replication artists, psychonaut wikis, QRI tools, immersive designers,
 and current cyberdelic researchers have all shaped what counts as a
 plausible visual target
 ([Kins, 2011](https://disregardeverythingisay.com/post/9331287956/the-visual-components-of-a-psychedelic-experience);
 [PsychonautWiki](https://psychonautwiki.org/wiki/Replication_index);
 [Hartogsohn, 2023](https://doi.org/10.3389/fpsyg.2022.1073235)).

 [Start with the basics](https://mesmerprism.com/projects/deep-dream.html#field-guide)
 [Community role](https://mesmerprism.com/projects/deep-dream.html#community)
 [Evidence](https://mesmerprism.com/projects/deep-dream.html#evidence)
 [V1 form constants](https://mesmerprism.com/projects/bressloff-v1-form-constants.html)
 [References](https://mesmerprism.com/projects/deep-dream.html#references)
 [Back to work](https://mesmerprism.com/#work)

 Field guide

## What is being simulated?

 Psychedelic visuals include more than "weird images." People repeatedly
 report families of altered visual experience: geometric form constants
 such as lattices, cobwebs, tunnels, spirals, and honeycomb fields;
 open-eye distortions such as breathing walls, flowing textures, and
 intensified color; motion effects such as tracers; and, at higher
 intensities, faces, entities, landscapes, and dream-like scenes
 ([Bressloff et al., 2002](https://doi.org/10.1162/089976602317250861);
 [Shanon, 2002](https://cris.huji.ac.il/en/publications/ayahuasca-visualizations-a-structural-typology);
 [Kometer et al., 2013](https://doi.org/10.1523/JNEUROSCI.3007-12.2013)).

 That repeatability is why simulation is possible at all. A visual
 system, artist, or algorithm can be asked to target specific
 signatures instead of producing a generic psychedelic-looking surface.
 The target might be a wall that drifts, a texture that becomes
 symmetrically patterned, a scene that develops pareidolic faces, or a
 VR world whose geometry and color become unstable while the viewer
 remains sober
 ([Effect Index](https://www.effectindex.com/effects);
 [PsychonautWiki](https://psychonautwiki.org/wiki/Replication_index);
 [Kaup et al., 2023](https://doi.org/10.3389/fpsyt.2023.1088896)).

 The hardest part is keeping the visual scope at the right scale. A simulation
 can resemble one visual effect without reproducing the whole
 psychedelic state. Pharmacological psychedelics also alter emotion,
 time, selfhood, memory, music, social meaning, bodily sensation, and
 interpretation. A visual simulator can isolate part of that system,
 but it cannot inherit the whole state just because the image looks
 convincing
 ([Suzuki et al., 2017](https://doi.org/10.1038/s41598-017-16316-2);
 [Suzuki, 2026](https://doi.org/10.3389/fpsyg.2026.1819038)).

### Common visual targets

- Form constants: lattices, tunnels, spirals, cobwebs, funnels

- Surface effects: breathing, melting, drifting, flowing, texture crawl

- Motion effects: tracers, after-images, smear, persistence

- Pattern effects: symmetry, recursive detail, fractal-like repetition

- Pareidolia: faces, animals, eyes, figures, objects emerging from the scene

### Comparison levels

- Visual resemblance to reports or replications

- Overlap with selected questionnaire dimensions

- Measured EEG, physiological, or behavioral changes

- Use as a controllable stimulus in a study

- Whole-state recreation remains outside visual simulation

 Community lineage

## The target set was named before it was stabilized in the lab

 Formal papers are only one part of this history. A large part of the
 field came from people trying to describe, draw, animate, and compare
 subjective visual experiences with each other.

### From trip reports to visual vocabulary

 Josie Kins's early Disregard Every Thing I Say work matters because it
 pushed psychedelic description away from metaphor alone. The 2011
 visual-components taxonomy argued for simple titles, descriptions,
 and intensity levels so that effects such as drifting, tracers,
 geometry, and hallucination complexity could be discussed more
 precisely. The later Effect Index and related PsychonautWiki material
 extended that impulse into a public reference culture
 ([Kins, 2011](https://disregardeverythingisay.com/post/9331287956/the-visual-components-of-a-psychedelic-experience);
 [Kins, 2022](https://www.effectindex.com/articles/funding-proposal);
 [PsychonautWiki](https://psychonautwiki.org/wiki/Replication_index)).

 That vocabulary changed the design problem. Once a community has words
 for symmetrical texture repetition, environmental patterning, drifting,
 tracers, and visual geometry, a simulator can be judged against a
 more specific target than "does this look trippy?" It can be asked
 which effect family it captures, which one it misses, and which source
 image or parameter choice made the difference
 ([Effect Index](https://www.effectindex.com/effects);
 [Loka](https://www.lokavision.com/psychic-rendering)).

### Replications are craft evidence

 Community replications need a modest role. A convincing animation on Reddit,
 a wiki page, or a tutorial cannot prove what happens in the brain or establish
 therapeutic value. It can make a personal perceptual report inspectable enough
 for other people to say:
 that is close, that is too fast, that surface should breathe before it
 melts, that geometry needs more symmetry, or that the scene should
 remain input-bound rather than become free-floating fantasy
 ([r/Replications](https://www.reddit.com/r/replications/);
 [PsychonautWiki](https://psychonautwiki.org/wiki/Replication_index)).

 That feedback loop is part of the field's infrastructure. Effect
 Index, PsychonautWiki's Replication Index, r/Replications, Loka's
 teaching material, Symmetric Vision's public simulations, Scry's
 generative geometry, QRI's contests and OscillEditor, and older
 DeepDream fractal tutorials all helped circulate practical knowledge.
 Many of these systems do not use DeepDream at all. They still belong
 in the DeepDream story because they shaped the broader ecosystem of
 visual targets, craft standards, and comparison methods
 ([QRI, hyperbolic DMT](https://qri.org/blog/hyperbolic-geometry-dmt);
 [QRI OscillEditor](https://qri.org/oscilleditor/doc/reference-manual);
 [Symmetric Vision](https://www.symmetric-vision.xyz/);
 [Scry](https://scry.art/blog/public/posts/atomic-discovery-collection/);
 [PsyKick](https://psykick.de/deepdream_fractals/Tutorial_deepdream_fractals_360_VR_4K_UltraHD.html)).

### Credit matters because methods move through people

 A clean academic timeline can make the field look as if it moved
 straight from Google to Sussex to later VR studies. The historical
 record is messier and more interesting. Community artists and
 developers built taxonomies, examples, tutorials, and video pipelines;
 researchers then selected a tractable subset and wrapped it in
 experimental measures ([Graphific, 2015](https://github.com/graphific/DeepDreamVideo);
 [Winiger, 2015](https://github.com/samim23/DeepDreamAnim);
 [Suzuki et al., 2017](https://doi.org/10.1038/s41598-017-16316-2)).
 That messier path is how a young interdisciplinary method often forms.

 A fair history should name the community contribution without overstating it.
 Community work supplied vocabulary, craft,
 examples, and open-source machinery. Academic work supplied controlled
 tasks, questionnaires, EEG, behavioral measures, and peer-reviewed
 comparison. The field needs both, and the sources need to say which
 claim each one supports.

 DeepDream lineage

## How a feature-visualization trick became a moving hallucination machine

 DeepDream's core move is simple to state. Start with a trained vision
 network, choose a layer or feature objective, and adjust the input
 image so the selected activations grow stronger. The network is not
 asked to classify the image. The image is pushed toward whatever the
 network is already ready to see
 ([Mordvintsev et al., 2015](https://research.google/blog/inceptionism-going-deeper-into-neural-networks/);
 [Olah et al., 2017](https://distill.pub/2017/feature-visualization/);
 [TensorFlow, 2024](https://www.tensorflow.org/tutorials/generative/deepdream)).

 In early ImageNet-based DeepDream, that meant dog faces, fur, eyes,
 towers, insects, and architectural fragments appearing inside clouds,
 leaves, buildings, and skin. The result was not a human hallucination,
 but it externalized a recognizable perceptual operation:
 over-interpret ambiguous input, then amplify the interpretation until
 the world seems to answer back
 ([Mordvintsev et al., 2015](https://research.google/blog/inceptionism-going-deeper-into-neural-networks/);
 [Suzuki, 2026](https://doi.org/10.3389/fpsyg.2026.1819038)).

 Still images were not enough for research VR. Frame-by-frame
 DeepDream flickers. The practical breakthrough was temporal
 continuity: frame inheritance, blending, and optical flow so that
 hallucinated structure persists and moves with the scene. Graphific's
 DeepDreamVideo, Samim Winiger's DeepDreamAnim, and Suzuki's
 DeepDreamVideoOpticalFlow belong in the credit line because they mark
 the open-code bridge from viral still images to coherent moving
 hallucinations
 ([Graphific, 2015](https://github.com/graphific/DeepDreamVideo);
 [Winiger, 2015](https://github.com/samim23/DeepDreamAnim);
 [Suzuki code](https://github.com/ksk-S/DeepDreamVideoOpticalFlow)).

### Compressed timeline

- 2011: Kins begins public visual-effect taxonomy work ([Kins, 2011](https://disregardeverythingisay.com/post/9331287956/the-visual-components-of-a-psychedelic-experience)).

- 2015: Google publishes Inceptionism and the DeepDream notebook ([Mordvintsev et al., 2015](https://research.google/blog/inceptionism-going-deeper-into-neural-networks/)).

- 2015: Graphific and Samim Winiger release public DeepDream video tools ([Graphific](https://github.com/graphific/DeepDreamVideo); [Winiger](https://github.com/samim23/DeepDreamAnim)).

- 2017: Suzuki, Roseboom, Schwartzman, and Seth publish the Hallucination Machine ([Suzuki et al., 2017](https://doi.org/10.1038/s41598-017-16316-2)).

- 2021-2025: EEG, cognitive-flexibility, cognition, and cognitive-affective studies extend the paradigm ([Greco et al., 2021](https://doi.org/10.3390/e23070839); [Rastelli et al., 2022](https://doi.org/10.1038/s41598-022-08047-w); [Brizzi et al., 2025](https://doi.org/10.1080/19585969.2025.2499459)).

- 2023-2026: cyberdelic, generative-model, oscillator, and clinical-facing VR branches widen the field ([Hartogsohn, 2023](https://doi.org/10.3389/fpsyg.2022.1073235); [Suzuki, 2026](https://doi.org/10.3389/fpsyg.2026.1819038)).

### Technical levers

- Network and training set

- Layer or feature target

- Octaves, step size, iterations, and jitter

- Source footage texture, motion, color, and semantic density

- Frame blending, optical flow, and adaptive carryover

 Synthesis

## DeepDream is useful because it is partial

 DeepDream isolates a tractable slice of altered visual interpretation.
 That is the strongest claim.

### Simulation has levels

 A system can resemble a report, match a community category, overlap
 with selected questionnaire dimensions, alter a neural or behavioral
 measure, or help researchers build a controlled stimulus. Those are
 different claims. DeepDream is strongest when the claim stays on the
 right rung of that ladder
 ([Suzuki et al., 2017](https://doi.org/10.1038/s41598-017-16316-2);
 [Suzuki, 2026](https://doi.org/10.3389/fpsyg.2026.1819038)).

 The Hallucination Machine showed that DeepDream-transformed VR could
 raise selected altered-perception ratings relative to unaltered
 video. It also showed a boundary: visual stimulation did not reproduce
 the temporal-production effects associated with fuller psychedelic
 states. That dissociation gives the paradigm its use: it lets
 researchers ask what altered vision alone can do
 ([Suzuki et al., 2017](https://doi.org/10.1038/s41598-017-16316-2)).

### The studies show selected effects

 Later papers extended the platform into EEG, cognition, and
 cognitive-affective work. Greco and colleagues reported changes in
 particular entropy, complexity, and functional-connectivity measures
 during DeepDream exposure. Rastelli and colleagues reported more
 flexible semantic-network structure and altered decision dynamics
 after DeepDream VR. A later cognition study found reduced switch
 costs and visually grounded effects, but not a broad shift in
 language-based automatic associations. Brizzi and colleagues reported
 cognitive-affective and autonomic changes under hallucinatory visual
 virtual experiences
 ([Greco et al., 2021](https://doi.org/10.3390/e23070839);
 [Rastelli et al., 2022](https://doi.org/10.1038/s41598-022-08047-w);
 [Greco et al., 2025](https://doi.org/10.1016/j.concog.2025.103808);
 [Brizzi et al., 2025](https://doi.org/10.1080/19585969.2025.2499459)).

 Those findings matter, but they should not be compressed into
 "DeepDream produces psychedelic cognition." They are measure-specific
 results from particular stimuli, tasks, samples, and parameter
 choices. The field gets stronger when the wording stays that exact.

### What DeepDream captures and what it misses

 DeepDream is naturally good at input-bound transformation:
 pareidolia, recursive texture, saturated patterning, object-rich
 intrusion, and the sense that visible surfaces are actively morphing.
 Its weakness follows from the same mechanism. It tends to inherit the
 priors of the trained network and the source footage. Early
 ImageNet-based results overproduce dogs, eyes, fur, and object
 fragments because those are strong handles for that model
 ([Mordvintsev et al., 2015](https://research.google/blog/inceptionism-going-deeper-into-neural-networks/);
 [Olah et al., 2017](https://distill.pub/2017/feature-visualization/)).

 Human psychedelic vision includes more than feature amplification.
 It can involve clean geometric form constants, closed-eye imagery,
 multisensory coupling, autobiographical content, emotional weight,
 symbolic interpretation, social setting, and changes in self and
 time. DeepDream can touch part of the visual field. It cannot carry
 the full pharmacological and personal context by itself
 ([Roseman et al., 2016](https://doi.org/10.1002/hbm.23224);
 [Suzuki, 2026](https://doi.org/10.3389/fpsyg.2026.1819038)).

### The 2026 update clarifies the next experiment

 Suzuki's 2026 theory paper gives the field a useful vocabulary for
 separating three roles: classifier feature exposure, generator or
 image-prior constraint, and discriminator-like source monitoring. In
 plainer terms: what features are made visible, what kind of image
 world constrains them, and when a viewer treats the generated content
 as perceptually compelling
 ([Suzuki, 2026](https://doi.org/10.3389/fpsyg.2026.1819038)).

 That framework does not validate DeepDream as a full psychedelic
 simulator. It does something better for future work: it makes the
 experimental question sharper. A study can specify whether it is
 manipulating feature exposure, image priors, source-scene continuity,
 intensity, or the viewer's judgment of what feels real.

 Current state

## The field after DeepDream

 As of May 22, 2026, DeepDream is no longer the frontier of generative
 image culture. Its importance is more historical and methodological:
 it made machine pareidolia vivid, gave researchers a controllable
 altered-vision stimulus, and forced the field to ask what kind of
 similarity a simulation claim actually names
 ([Mordvintsev et al., 2015](https://research.google/blog/inceptionism-going-deeper-into-neural-networks/);
 [Suzuki et al., 2017](https://doi.org/10.1038/s41598-017-16316-2);
 [Suzuki, 2026](https://doi.org/10.3389/fpsyg.2026.1819038)).

 The broader field now includes style-transfer and intensity-varied
 Hallucination Machine variants, shader-based VR systems, generative
 hallucination models, stroboscopic and oscillator tools, QRI's
 OscillEditor, Psyrreal, Ayahuasca Kosmik Journey, Si-PHI, and other
 cyberdelic or psychedelic-adjacent immersive systems. Some are
 research instruments, some are artworks, some are public education
 tools, and some are early clinical-facing platforms. They should be
 compared, not collapsed
 ([Suzuki et al., 2024](https://doi.org/10.3389/fnhum.2023.1159821);
 [Hewitt et al., 2025](https://doi.org/10.1093/nc/niaf020);
 [QRI OscillEditor](https://qri.org/oscilleditor/doc/reference-manual);
 [Kaup et al., 2023](https://doi.org/10.3389/fpsyt.2023.1088896);
 [Hartogsohn, 2023](https://doi.org/10.3389/fpsyg.2022.1073235)).

 Current public records also need careful separation. ClinicalTrials.gov
 lists Yale's NCT06581263 "Psychedelic Virtual Reality" record as
 completed, with primary completion and completion dates of March 22,
 2026, and an update posted on March 25, 2026. The University of
 Tartu's NCT06174285 Psyrreal record is listed as recruiting, with
 primary completion and completion dates planned for June 30, 2026.
 Those are registry-status claims, not outcome claims
 ([ClinicalTrials.gov, NCT06581263](https://clinicaltrials.gov/study/NCT06581263);
 [ClinicalTrials.gov, NCT06174285](https://clinicaltrials.gov/study/NCT06174285)).

### Keep distinct

- DeepDream: feature amplification in a trained vision network

- Hallucination Machine: DeepDream transformed into panoramic VR research

- Community replication: taxonomies, examples, tutorials, and feedback loops

- Cyberdelics: broader digital altered-state design and research frame

- Clinical records: trial status, not efficacy unless results are published

### What to watch

- Comparisons between DeepDream, style transfer, shaders, and oscillator tools

- Better reporting of source footage and stimulus statistics

- Fractal dimension, symmetry strength, texture class, and motion-field measures

- Participant history, expectation, absorption, and source-monitoring thresholds

- Credit and provenance as community techniques move into institutional settings

 Evidence audit

## What is solid, partial, and open

 The field becomes easier to read when each source is kept in its
 lane. Repositories support technical lineage. Community pages support
 vocabulary, practice, and public craft. Papers support controlled
 claims about subjective reports, EEG, cognition, or affect. Trial
 registries support current status. None of these source types should
 be asked to do all the work
 ([Graphific, 2015](https://github.com/graphific/DeepDreamVideo);
 [Effect Index](https://www.effectindex.com/effects);
 [Greco et al., 2021](https://doi.org/10.3390/e23070839);
 [ClinicalTrials.gov](https://clinicaltrials.gov/study/NCT06581263)).

### Solid

- DeepDream began as feature visualization and model inspection ([Mordvintsev et al., 2015](https://research.google/blog/inceptionism-going-deeper-into-neural-networks/); [Olah et al., 2017](https://distill.pub/2017/feature-visualization/)).

- Open repositories helped make moving DeepDream coherent enough for video and VR ([Graphific](https://github.com/graphific/DeepDreamVideo); [Winiger](https://github.com/samim23/DeepDreamAnim); [Suzuki code](https://github.com/ksk-S/DeepDreamVideoOpticalFlow)).

- Community taxonomies made psychedelic visual effects more nameable and buildable ([Kins, 2011](https://disregardeverythingisay.com/post/9331287956/the-visual-components-of-a-psychedelic-experience); [Effect Index](https://www.effectindex.com/effects)).

- DeepDream VR can alter selected subjective, EEG, cognitive, and affective measures ([Suzuki et al., 2017](https://doi.org/10.1038/s41598-017-16316-2); [Greco et al., 2021](https://doi.org/10.3390/e23070839); [Brizzi et al., 2025](https://doi.org/10.1080/19585969.2025.2499459)).

- Suzuki 2026 gives a cleaner C x G x D vocabulary for future experiments ([Suzuki, 2026](https://doi.org/10.3389/fpsyg.2026.1819038)).

### Partial or open

- Community replications can guide target selection, but they are not controlled validation ([PsychonautWiki](https://psychonautwiki.org/wiki/Replication_index); [r/Replications](https://www.reddit.com/r/replications/)).

- Most DeepDream VR studies still occupy a narrow parameter family ([Suzuki et al., 2017](https://doi.org/10.1038/s41598-017-16316-2); [Greco et al., 2021](https://doi.org/10.3390/e23070839)).

- Fractal-like and symmetry-rich stimuli are central to the design logic but rarely quantified in DeepDream studies ([Bressloff et al., 2002](https://doi.org/10.1162/089976602317250861); [Pangburn, 2015](https://www.vice.com/en/article/deepdreaming-a-fully-explorable-fractal-planet/); [PsyKick](https://psykick.de/deepdream_fractals/Tutorial_deepdream_fractals_360_VR_4K_UltraHD.html)).

- Current clinical-facing records show interest and study activity, not established therapeutic efficacy ([NCT06581263](https://clinicaltrials.gov/study/NCT06581263); [NCT06174285](https://clinicaltrials.gov/study/NCT06174285)).

- The field still needs direct comparisons across DeepDream, shaders, style transfer, flicker, oscillator tools, and generative models ([Suzuki, 2026](https://doi.org/10.3389/fpsyg.2026.1819038); [Hewitt et al., 2025](https://doi.org/10.1093/nc/niaf020)).

 Open direction

## A better next comparison

 The next useful generation of studies would stop treating each system
 as a single condition label. It would compare induced-vision methods
 on shared axes: feature exposure, image prior, source-scene texture,
 motion continuity, symmetry, fractal statistics, semantic density,
 intensity, embodiment, and source-monitoring judgment. A DeepDream
 video, a stroboscopic field, an OscillEditor patch, a shader-based
 Psyrreal scene, and a style-transfer hallucination may all feel
 "psychedelic" in public language, but they reach that feeling through
 different mechanisms
 ([Suzuki, 2026](https://doi.org/10.3389/fpsyg.2026.1819038);
 [Hewitt et al., 2025](https://doi.org/10.1093/nc/niaf020);
 [QRI OscillEditor](https://qri.org/oscilleditor/doc/reference-manual);
 [Kaup et al., 2023](https://doi.org/10.3389/fpsyt.2023.1088896)).

 That comparison would let DeepDream take its proper place: the first widely
 visible machine-vision method that made computational psychedelic simulation
 concrete enough for artists, developers, and scientists to argue with
 ([Mordvintsev et al., 2015](https://research.google/blog/inceptionism-going-deeper-into-neural-networks/);
 [Suzuki et al., 2017](https://doi.org/10.1038/s41598-017-16316-2)).

 References

## Sources and entry points

 These references are grouped by role. Living pages, repositories,
 and trial records were checked on May 22, 2026 unless a more specific
 update date is listed.

### Visual phenomenology

- Kins, Josie. "[The Visual Components of a Psychedelic Experience](https://disregardeverythingisay.com/post/9331287956/the-visual-components-of-a-psychedelic-experience)." Disregard Every Thing I Say (2011).

- Effect Index. "[Subjective Effect Index](https://www.effectindex.com/effects)." Visual-effect taxonomy and effect index.

- PsychonautWiki. "[Replication Index](https://psychonautwiki.org/wiki/Replication_index)." Community visual-replication archive.

- Bressloff et al. "[What Geometric Visual Hallucinations Tell Us about the Visual Cortex](https://doi.org/10.1162/089976602317250861)." Neural Computation 14(3) (2002).

- Shanon. "[Ayahuasca Visualizations: A Structural Typology](https://cris.huji.ac.il/en/publications/ayahuasca-visualizations-a-structural-typology)." Journal of Consciousness Studies 9(2) (2002).

- Kometer et al. "[Activation of Serotonin 2A Receptors Underlies the Psilocybin-Induced Effects on Alpha Oscillations, N170 Visual-Evoked Potentials, and Visual Hallucinations](https://doi.org/10.1523/JNEUROSCI.3007-12.2013)." Journal of Neuroscience 33(25) (2013).

- Roseman et al. "[LSD Alters Eyes-Closed Functional Connectivity within the Early Visual Cortex in a Retinotopic Fashion](https://doi.org/10.1002/hbm.23224)." Human Brain Mapping 37(8) (2016).

### Community practice

- Kins, Josie. "[JosieKins.xyz](https://josiekins.xyz/)." Public project history for Disregard Every Thing I Say, Effect Index, PsychonautWiki, and r/Replications.

- Kins, Josie. "[Intercollegiate Funding Proposal](https://www.effectindex.com/articles/funding-proposal)." Effect Index (2022).

- Reddit community. "[r/Replications](https://www.reddit.com/r/replications/)." Community hub for image, video, and audio replications of altered states.

- Qualia Research Institute. "[The Hyperbolic Geometry of DMT Experiences](https://qri.org/blog/hyperbolic-geometry-dmt)." QRI blog.

- Qualia Research Institute. "[Unveiling QRI's Consciousness Art Contests](https://qri.org/blog/contest)." QRI blog (2023).

- Qualia Research Institute. "[Reference Manual for QRI's Oscilleditor](https://qri.org/oscilleditor/doc/reference-manual)." Tool documentation.

- Loka. "[Psychic Rendering](https://www.lokavision.com/psychic-rendering)." Practical hallucination-replication design page.

- Symmetric Vision. "[Symmetric Vision](https://www.symmetric-vision.xyz/)." Altered-state simulation portfolio.

- Scry Visuals. "[Atomic Discovery Collection](https://scry.art/blog/public/posts/atomic-discovery-collection/)." Generative visual-replication project note.

### Technical origin

- Mordvintsev, Olah, and Tyka. "[Inceptionism: Going Deeper into Neural Networks](https://research.google/blog/inceptionism-going-deeper-into-neural-networks/)." Google Research (2015).

- Google. "[google/deepdream](https://github.com/google/deepdream)." Canonical public DeepDream repository.

- Olah, Mordvintsev, and Schubert. "[Feature Visualization](https://distill.pub/2017/feature-visualization/)." Distill (2017).

- TensorFlow. "[DeepDream](https://www.tensorflow.org/tutorials/generative/deepdream)." Official tutorial, last updated 2024-08-16.

- Graphific. "[DeepDreamVideo](https://github.com/graphific/DeepDreamVideo)." Public repository (2015).

- Winiger, Samim. "[DeepDreamAnim](https://github.com/samim23/DeepDreamAnim)." Public repository (2015).

- Suzuki, Keisuke. "[DeepDreamVideoOpticalFlow](https://github.com/ksk-S/DeepDreamVideoOpticalFlow)." Public repository.

### DeepDream VR studies

- Suzuki et al. "[A Deep-Dream Virtual Reality Platform for Studying Altered Perceptual Phenomenology](https://doi.org/10.1038/s41598-017-16316-2)." Scientific Reports 7 (2017).

- Greco et al. "[Increased Entropic Brain Dynamics during DeepDream-Induced Altered Perceptual Phenomenology](https://doi.org/10.3390/e23070839)." Entropy 23(7) (2021).

- Rastelli et al. "[Simulated Visual Hallucinations in Virtual Reality Enhance Cognitive Flexibility](https://doi.org/10.1038/s41598-022-08047-w)." Scientific Reports 12 (2022).

- Greco et al. "[Immersive Exposure to Simulated Visual Hallucinations Modulates High-Level Human Cognition](https://doi.org/10.1016/j.concog.2025.103808)." Consciousness and Cognition 128 (2025).

- Brizzi et al. "[Cyberdelics: Virtual Reality Hallucinations Modulate Cognitive-Affective Processes](https://doi.org/10.1080/19585969.2025.2499459)." Dialogues in Clinical Neuroscience 27(1) (2025).

- Motyka et al. "[Phenomenologically Distinct and Intensity-Varied VR Hallucinations](https://osf.io/dj6rp/overview)." OSF project (2025).

### Beyond DeepDream

- Suzuki, Seth, and Schwartzman. "[Modelling Phenomenological Differences in Aetiologically Distinct Visual Hallucinations Using Deep Neural Networks](https://doi.org/10.3389/fnhum.2023.1159821)." Frontiers in Human Neuroscience (2024).

- Suzuki. "[Beyond the Reducing Valve: Towards a Computational Neurophenomenology of Altered States via Deep Neural Networks](https://doi.org/10.3389/fpsyg.2026.1819038)." Frontiers in Psychology (2026).

- Schartner and Timmermann. "[Neural Network Models for DMT-Induced Visual Hallucinations](https://doi.org/10.1093/nc/niaa024)." Neuroscience of Consciousness (2020).

- Bredenberg et al. "[The Oneirogen Hypothesis](https://doi.org/10.7554/eLife.105968)." eLife reviewed preprint (2026).

- Hewitt et al. "[Stroboscopically Induced Visual Hallucinations](https://doi.org/10.1093/nc/niaf020)." Neuroscience of Consciousness (2025).

- PsyKick. "[360 Degrees 4K Virtual Reality Deep Dream Fractal Tutorial](https://psykick.de/deepdream_fractals/Tutorial_deepdream_fractals_360_VR_4K_UltraHD.html)." Practical tutorial.

- Pangburn. "[Deepdreaming a Fully-Explorable Fractal Planet](https://www.vice.com/en/article/deepdreaming-a-fully-explorable-fractal-planet/)." VICE (2015).

### Cyberdelic and current records

- Hartogsohn. "[Cyberdelics in Context](https://doi.org/10.3389/fpsyg.2022.1073235)." Frontiers in Psychology (2023).

- Smith and Warner. "[Cyberdelics: Context Engineering Psychedelics for Altered Traits](https://doi.org/10.14236/ewic/EVA2022.48)." EVA London (2022).

- Kaup et al. "[Psychedelic Replications in Virtual Reality and their Potential as a Therapeutic Instrument](https://doi.org/10.3389/fpsyt.2023.1088896)." Frontiers in Psychiatry 14 (2023).

- Psyrreal. "[Psyrreal VR](https://psyrreal.mozellosite.com/)." Public project page.

- ClinicalTrials.gov. "[NCT06174285: Effect of Psychedelic VR-augmented Therapy on Patients With Clinical Depression](https://clinicaltrials.gov/study/NCT06174285)." University of Tartu registry record; status checked 2026-05-22.

- Yale Medicine. "[Can Virtual Reality Help with Psychedelic Research?](https://www.yalemedicine.org/clinical-trials/psychedelic-virtual-reality)" DeepDream VRP study page.

- ClinicalTrials.gov. "[NCT06581263: Psychedelic Virtual Reality](https://clinicaltrials.gov/study/NCT06581263)." Yale University registry record; status checked 2026-05-22.

- Yale Ventures. "[Simulated Psychedelic Immersive Experience: Healthcare Intervention for Depression (Si-PHI)](https://ventures.yale.edu/yale-technologies/simulated-psychedelic-immersive-experience-healthcare-intervention-depression-si)." Technology page.