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REVIEW 2 major objections 1 minor 34 references

Tactile Hallucinations on Artificial Skin Induced by Homeostasis in a Deep Boltzmann Machine

T0 review · 2 major / 1 minor · reviewed 2026-05-25 · grok-4.3

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Pith's one-line read Homeostasis in a Deep Boltzmann Machine induces hallucinations of previously learned tactile patterns on artificial skin without sensory input.

desk verdict The paper shows that adding homeostasis to a DBM on tactile skin data makes the model output learned patterns with no input and improves reconstruction, but the results stay qualitative and the biological claim has no grounding. read the letter →

arxiv 1906.10592 v2 pith:KC42LXXX submitted 2019-06-25 cs.NE cs.LGq-bio.NC

Michael Deistler , Yagmur Yener , Florian Bergner , Pablo Lanillos , Gordon Cheng This is my paper · ORCID
classification cs.NEcs.LGq-bio.NC
keywords tactilehallucinationsdeepBoltzmannmachinehomeostasisartificialskinsensorydeprivationgenerativemodelslatentrepresentationsperception
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper applies a homeostasis rule that changes neuron excitability during sensory deprivation inside a Deep Boltzmann Machine trained on patterns from artificial skin. This produces hallucinations of the learned patterns when input is removed and also improves how well the network reconstructs its internal latent states. The work treats this as a computational account of tactile hallucinations reported in neurological disorders and amputees. A reader would see it as evidence that perception can be generative and that homeostatic adjustment is one concrete way the generation occurs.

What carries the argument

Homeostasis rule that adjusts neuron excitability in the Deep Boltzmann Machine to simulate effects of sensory deprivation.

What would settle it

If neural recordings from biological tactile pathways during sensory deprivation show different patterns from those generated by the homeostatic DBM, or if removing homeostasis does not reduce reconstruction quality, the central claim would be falsified.

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Extended reading notes

Core claim

In a Deep Boltzmann Machine trained on tactile patterns from artificial skin, introducing homeostasis during periods without sensory input causes the network to generate hallucinations of previously learned patterns, induces the formation of meaningful latent representations, and significantly increases the quality of the reconstruction of these latent states.

Load-bearing premise

The Deep Boltzmann Machine together with the chosen homeostasis rule is a sufficient model of the generative processes that produce tactile hallucinations in biological systems.

Editorial extensions

If this is right

  • Hallucinations of learned patterns appear on the artificial skin when sensory input is absent.
  • Meaningful latent representations form under the homeostasis rule.
  • Reconstruction quality of the latent states increases significantly.
  • The model supplies one possible explanation for the nature of tactile hallucinations.
  • Homeostatic processes are indicated as a candidate mechanism underlying such hallucinations.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same homeostasis mechanism could be inserted into other generative models to test whether it produces hallucinations in visual or auditory domains.
  • Comparing the DBM output directly to spike recordings from deprived skin nerves would provide a concrete test of biological plausibility.
  • Prosthetic skins might need explicit homeostatic compensation to suppress unwanted phantom patterns during periods of low input.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces homeostasis as an excitability change in a Deep Boltzmann Machine trained on tactile patterns to model hallucinations on artificial skin. It claims that, in the absence of sensory input, the homeostasis rule induces hallucinations of previously learned patterns, promotes formation of meaningful latent representations, and significantly improves reconstruction quality of those states, thereby supplying a possible mechanistic explanation for tactile hallucinations in biological systems.

Significance. If the reported effects prove robust under quantitative controls and the DBM dynamics can be shown to be at least a plausible proxy for somatosensory generative processes, the work would link homeostatic regulation to perceptual inference in a generative model, adding a computational perspective on how sensory deprivation can produce structured hallucinations. The absence of metrics, ablations, and biological grounding currently limits the strength of that contribution.

major comments (2)
  1. [Abstract] Abstract: the assertion that homeostasis 'significantly increases the quality of the reconstruction of these latent states' is presented without quantitative metrics, error bars, statistical tests, or ablation controls, which is load-bearing for the central performance claim.
  2. [Abstract] Abstract: the further claim that the model 'provides a possible explanation for the nature of tactile hallucinations' rests on the untested assumption that the chosen homeostasis rule and resulting sampling statistics constitute a plausible proxy for biological generative mechanisms; no comparison to electrophysiological recordings (e.g., firing-rate distributions or correlations under sensory deprivation) or to alternative generative architectures is supplied.
minor comments (1)
  1. The abstract supplies only qualitative outcomes; the methods section should explicitly state the homeostasis update rule, all free parameters, training protocol, and the precise definition of 'meaningful latent representations' to permit independent verification.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed review and the opportunity to address the concerns raised. Below we respond point-by-point to the major comments.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the assertion that homeostasis 'significantly increases the quality of the reconstruction of these latent states' is presented without quantitative metrics, error bars, statistical tests, or ablation controls, which is load-bearing for the central performance claim.

    Authors: The abstract is a concise summary; the manuscript contains the supporting quantitative results, including reconstruction-error metrics, comparisons with and without homeostasis, and visualizations of the effects. To strengthen the abstract we will add a short clause referencing the observed quantitative improvement in reconstruction quality. revision: yes

  2. Referee: [Abstract] Abstract: the further claim that the model 'provides a possible explanation for the nature of tactile hallucinations' rests on the untested assumption that the chosen homeostasis rule and resulting sampling statistics constitute a plausible proxy for biological generative mechanisms; no comparison to electrophysiological recordings (e.g., firing-rate distributions or correlations under sensory deprivation) or to alternative generative architectures is supplied.

    Authors: The work demonstrates, within a generative model (DBM) trained on tactile data, that a biologically motivated homeostasis rule produces structured hallucinations of learned patterns when external input is removed. This supplies a concrete computational mechanism that can be viewed as one possible explanation for tactile hallucinations under sensory deprivation. The discussion section already notes the biological motivation of the homeostasis rule and its relation to perceptual inference; we do not claim the model is a direct replica of cortical circuitry, only that it illustrates a plausible generative-process account. revision: no

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; simulation results are direct consequences of added mechanism

full rationale

The paper introduces homeostasis as an external rule applied to a pre-trained DBM and reports simulation outcomes (hallucinations and improved reconstruction) under zero-input conditions. These are not reductions by construction to the inputs, as the rule is an independent addition whose effects are measured rather than presupposed. No self-definitional equations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the derivation chain. The biological explanatory claim is framed as a possible model rather than a forced mathematical identity, leaving the work self-contained against external benchmarks.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

The central claim rests on the untested premise that a DBM plus a simple excitability shift is an adequate generative model of biological tactile perception. No external benchmarks or independent data are supplied in the abstract.

assumptions (1)
  • domain assumption A DBM trained on tactile patterns can serve as a proxy for the generative processes underlying biological tactile perception.
    Invoked when the authors equate model output to 'tactile hallucinations' without additional validation.

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Cite this review

Pith. "Pith review of Tactile Hallucinations on Artificial Skin Induced by Homeostasis in a Deep Boltzmann Machine." pith.science (2026). https://pith.science/paper/KC42LXXX

@misc{pith2026190610592,
  author       = {Pith},
  title        = {Pith review of: Tactile Hallucinations on Artificial Skin Induced by Homeostasis in a Deep Boltzmann Machine},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KC42LXXX}},
  note         = {Machine review of arXiv:1906.10592}
}
read the original abstract

Perceptual hallucinations are present in neurological and psychiatric disorders and amputees. While the hallucinations can be drug-induced, it has been described that they can even be provoked in healthy subjects. Understanding their manifestation could thus unveil how the brain processes sensory information and might evidence the generative nature of perception. In this work, we investigate the generation of tactile hallucinations on biologically inspired, artificial skin. To model tactile hallucinations, we apply homeostasis, a change in the excitability of neurons during sensory deprivation, in a Deep Boltzmann Machine (DBM). We find that homeostasis prompts hallucinations of previously learned patterns on the artificial skin in the absence of sensory input. Moreover, we show that homeostasis is capable of inducing the formation of meaningful latent representations in a DBM and that it significantly increases the quality of the reconstruction of these latent states. Through this, our work provides a possible explanation for the nature of tactile hallucinations and highlights homeostatic processes as a potential underlying mechanism.

Figures

Figures reproduced from arXiv: 1906.10592 by the authors.

Figure 1
Figure 1. Deep Boltzmann Machine (DBM) for modelling tactile hallucinations. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Biologically inspired multi-modal skin [14], [24]. The skin is [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 4
Figure 4. Training accuracy curves as measured by the performance measure [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗
Figures from the paper (4 more)
Figure 5
Figure 5. Figure 5: Decoded states when clamping to one of the training patterns for circular connectivity. The measure of performance Q is 0.87. Training Homeostasis Blanking the input [PITH_FULL_IMAGE:figures/full_fig_p004_5.png]
Figure 6
Figure 6. Figure 6: Visualization of the measures [PITH_FULL_IMAGE:figures/full_fig_p004_6.png]
Figure 9
Figure 9. Figure 9: Correlation between Qloss and Qgain. On the x-axis, the difference in reconstruction performance between clamping to a training pattern or a blank pattern Qloss is shown. On the y-axis, the gain in performance through hallucinations Qgain is shown. The two traces corre…
Figure 8
Figure 8. Figure 8: Demonstration of the reconstructed patterns given a sample trace dur [PITH_FULL_IMAGE:figures/full_fig_p005_8.png]

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Reference graph

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