Backcasting is Magical
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Yablos Paradox is the idea that there is no way to coherently assign a truth value to any of the sentences in a countably infinite sequence of sentences, when these sentences all state that “all of the subsequent sentences are false”.
"The point of these observations is not the reduction of the familiar to the unfamiliar[...] but the extension of the familiar to cover many more cases." - Saunders MacLane, Categories for the Working Mathematician, Page 226
Most truths cannot be expressed in language
The Period-doubling cascades en route to chaos remains one of the most puzzling features of nature
Was Floquet Engineering to identify nonlinear resonances in Floquet‑Driven Reservoirs all night long. Exhausted but unbothered. moisturized. happy. in my lane. focused. flourishing. periodically driven.
Neural waves study provides evidence that brain's rhythmic patterns play key role in information processing
The authors of the study suggest that the brain uses the superposition & interference patterns of waves to represent and process information in a highly distributed way, exploiting the unique properties of coupled oscillator networks such as resonance and synchronization.
Papers & Code:
Code: Harmonic oscillator recurrent network (HORN) Pytorch Implementation
Overview:
High-level concepts:
Recast neural processing as continuous‐time dynamics rather than pure feed-forward stateless layers.
Embedding physical, continuous dynamics—oscillations, waves, vortices, and damping—directly into the network’s layers unlocks powerful, implicit mechanisms for attention, multi-scale integration, and long-range memory, without the combinatorial bloat of explicit attention heads or deeper stacks.
1. Controlled Oscillations as an Inductive Bias
Damped Harmonic Oscillator (DHO) nodes
In
model.py, each hidden unit integrates its “position” x and “velocity” y via:
Key Insight: forcing every node into an oscillatory regime makes phase, frequency and amplitude available for coding from the very first forward pass—no need to “discover” oscillations through recurrent loops alone.
Why it matters:
- Leaky‐integrator RNNs can oscillate only indirectly (if you tweak weights).
- By baking oscillations into the node, you guarantee resonance‐based feature extraction and a rich dynamical repertoire from the start.
2. Wave‐Based Representations & Interference
Traveling waves & standing waves
- External input → local standing oscillation in each DHO
- Recurrent coupling → traveling waves that propagate, collide, and interfere
High‐Dimensional Mapping:
- Low‐dimensional time-series (e.g. pixel sweep in sMNIST) → high-dimensional spatiotemporal interference pattern across the N oscillators.
- After training, these patterns carve out stimulus‐specific trajectories in the N-dimensional state space that are linearly separable by your final
h2olayer.
Code touch-point: The
record=Trueflag inforward()gives yourec_x_tandrec_y_t. Visualizing these (as indynamics.pyortrain.py) shows exactly how different input classes sculpt different wave‐interference signatures.
3. Resonance & Intrinsic Receptive Fields
The intrinsic gain curve of each DHO node is defined as:
In the PNAS work, a grid‐search found optimal \omega \approx 2\pi/28 to match the dominant frequency of straight strokes in sMNIST.
Computational payoff: Nodes automatically amplify (resonate) in-band inputs—so you’re baking in priors about the signal’s spectral content at the architectural level, not just via learned weights.
How to explore in your code:
- In
dynamics.py, drive a single DHO with a pure sine wave at different frequencies. - Measure steady‐state amplitude &bar; x &bar; after transients → plot &bar; x &bar; vs. frequency.
- That’s your empirical G(\omega_{\text{in}}) curve.
4. Heterogeneity → Near‐Critical Dynamics
Biological motif: neurons/pyramidal microcircuits have tunings and delays that vary across the network.
HORN’s version: drawing each unit’s \omega_i and \gamma_i (and later, delays) from a distribution.
Why it helps:
- Expands the repertoire of possible wave‐interference patterns
- Brings network closer to a “critical” regime: balance of order/disorder, long memory tails, richer transient dynamics
Simple test in code:
- In
dynamics.pyyou already sampleomegaandgamma. Try:- Homogeneous run (all ω = ω_base, γ = γ_base) vs.
- Heterogeneous run (sampled ω, γ)
- Quantify:
- Pairwise phase‐locking values between units over time (look at how PLV distribution broadens)
- Linear separability: train a small linear SVM on the recorded
rec_x_tsnapshots and compare classification accuracy.
5. Hebbian Alignment of Backprop
Surprising finding: gradient‐based BPTT weight updates on
h2hend up correlating strongly with pre-post activity correlations—i.e. they “look Hebbian.”Takeaway: Even though you’re minimizing a cross‐entropy loss at the readout, the recurrent weight updates reorganize the network to amplify stimulus‐specific synchrony patterns.
Next step in code:
- After an epoch of supervised training, scatter‐plot before & after training to see the emergence of a positive correlation between weight strength and firing‐correlation
6. Spontaneous → Evoked State Manifolds
Biological signature: cortex at rest floats through a “rich club” of states; stimuli transiently collapse it into a low‐variance, stimulus‐specific subspace.
HORN mimic:
- Add small random kicks to
y_t(Poisson noise) whenrecord=False - Observe via PCA on the recorded trajectories that:
- Rest: clouds of points filling a large manifold
- During input: rapid collapse to distinct clusters for each class
- After: rebound back to the resting manifold
In summary
- Oscillatory nodes give access to phase, frequency, and amplitude as first‐class coding variables.
- Wave interference provides a massively parallel, high‐dimensional embedding of inputs.
- Resonance priors let you tune the network at the architectural level to known signal statistics.
- Heterogeneity & delays enrich the dynamic repertoire and promote near‐critical, richly transient behaviors.
- Hebbian‐style learning emerges naturally even under supervised BPTT, hinting at fully unsupervised alternatives.
- Rest–stimulus manifold dynamics in HORN match key cortical phenomenology.
Weekend immersed in Deep Oscillator Neural Networks has been a resonant journey.
Phase-encoded information is elusive: no single wave holds meaning. It’s the alignment, the relationship, that reveals it. It dominates in systems across nature where precision, interference, or efficiency matter.
Nivida - Microsoft Revenues Round Tripping scheme here in all its beauty
In case you wanted any proof of $MSFT round tripping revenues at a massive scale in order to inflate its financial results here you have it right out its own last 10-Q
Kaznacheyev effect
Introduction: Ever thought cells could communicate like a cell phone but with light? Enter the fascinating and controversial realm of the Kaznacheyev effect!
The Discovery: In the 1970s, a Russian scientist named Vlail Kaznacheyev, along with his colleagues, stumbled upon a phenomenon that would spark decades of debate. They observed that cells don’t go quietly into the night; instead, as they perish, they emit ultraviolet (UV) photons, like desperate SOS signals.
The Experiment: Kaznacheyev’s team placed two sets of cell cultures close to each other, separated only by a quartz barrier that let UV light pass through. When one set of cells was inflicted with doom (think viruses, toxins, or harmful radiation), these dying cells reportedly sent out UV light signals. The astonishing part? The neighboring cells, previously healthy, started showing signs of the same fate as if the light carried a morbid message.
The Twist: A glass barrier instead of quartz? The second set of cells remained unaffected, living their best cellular lives. It seemed that the UV light, with its mysterious cargo, couldn’t pass through glass, halting the transmission of the deadly message.
The Controversy: Dubbed the “cytopathogenic effect,” this phenomenon suggested something revolutionary—that diseases could potentially be transmitted electromagnetically. But here’s the catch: science thrives on replication, and the Kaznacheyev effect has been notoriously shy in other labs.
The Implications: If the effect is real, it could hint at an intricate bio-communication system and electromagnetic influences on health, a topic that’s gaining traction in today’s tech-filled world.
The Fun Fact: While the scientific jury is still out on the Kaznacheyev effect, it opens up a world of sci-fi-esque possibilities. Could our cells be gossiping about their demise? Are they warning their neighbors through a UV light group chat? The ideas are as intriguing as they are controversial.
Whether a quirk of experimental conditions or a genuine biological phenomenon, the Kaznacheyev effect reminds us that there are still mysteries within our own cells waiting to be understood. It’s a cellular conundrum that keeps the conversation glowing—quite literally!
Conclusion: Vlail Petrovich Kaznacheyev was a Russian biologist and a known figure in the field of biophysics. He is most recognized for his work related to the controversial phenomenon known as the Kaznacheyev effect, which he claimed to have discovered in the 1970s alongside his colleagues. According to his research, cells that were dying emitted ultraviolet (UV) photons, and these photons could transmit the “information” of cellular death to neighboring cells, causing similar effects in those cells if they were in quartz containers that allowed the passage of UV light. If a barrier that blocked UV light, such as glass, was used, the effect was not observed.
Regarding the proof to back up the Kaznacheyev effect, it’s important to note that this phenomenon has been met with skepticism within the scientific community. One of the main criticisms is the lack of independent replication of his results, which is a cornerstone of scientific validation. The experiments conducted by Kaznacheyev and his team were indeed numerous, but the methodology and the results were not widely accepted or reproduced by other researchers.
The idea that cells could communicate distress through electromagnetic signals, especially in the form of light, is certainly fascinating and has implications for our understanding of cellular processes and disease transmission. However, the evidence supporting the Kaznacheyev effect is not robust within the mainstream scientific literature.
There are fields of study such as bioelectromagnetics and biophotonics that explore the role of electromagnetic fields and light in biological processes, and some research within these disciplines has indicated that cells can emit light (biophotons) and that these emissions can vary with the state of the cell’s health. However, whether these emissions can induce pathological changes in other cells as described by the Kaznacheyev effect remains unconfirmed.
"Time saved by AI offset by new work created, study suggests"
By now everyone could be living on a 2-day workweek in fully automated luxury - but instead we’re slaving away at bullshit jobs harder than ever. The game’s rigged.
