Evidence map›Paper›PMID 38468067›Full record

ArticleAdvances in neurobiology2024

Fractal Electronics for Stimulating and Sensing Neural Networks: Enhanced Electrical, Optical, and Cell Interaction Properties.

S Moslehi, C Rowland, J H Smith, W J Watterson, W Griffiths, R D Montgomery, S Philliber, C A Marlow, M-T Perez, R P Taylor

Abstract read
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Article in Advances in neurobiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

S MoslehiPhysics Department, University of Oregon, Eugene, OR, USA.
C RowlandPhysics Department, University of Oregon, Eugene, OR, USA.
J H SmithPhysics Department, University of Oregon, Eugene, OR, USA.
W J WattersonPhysics Department, University of Oregon, Eugene, OR, USA.
W GriffithsPhysics Department, University of Oregon, Eugene, OR, USA.
R D MontgomeryPhysics Department, University of Oregon, Eugene, OR, USA.
S PhilliberPhysics Department, University of Oregon, Eugene, OR, USA.
C A MarlowPhysics Department, California Polytechnic State University, San Luis Obispo, CA, USA.
M-T PerezDepartment of Clinical Sciences Lund, Division of Ophthalmology, Lund University, Lund, Sweden.
R P TaylorPhysics Department, University of Oregon, Eugene, OR, USA. rpt@uoregon.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Imagine a world in which damaged parts of the body - an arm, an eye, and ultimately a region of the brain - can be replaced by artificial implants capable of restoring or even enhancing human performance. The associated improvements in the quality of human life would revolutionize the medical world and produce sweeping changes across society. In this chapter, we discuss several approaches to the fabrication of fractal electronics designed to interface with neural networks. We consider two fundamental functions - stimulating electrical signals in the neural networks and sensing the location of the signals as they pass through the network. Using experiments and simulations, we discuss the favorable electrical performances that arise from adopting fractal rather than traditional Euclidean architectures. We also demonstrate how the fractal architecture induces favorable physical interactions with the cells they interact with, including the ability to direct the growth of neurons and glia to specific regions of the neural-electronic interface.

Indexed as

BrainFractalsElectronicsHumansNeural Networks, ComputerNeuronsElectronicsFractalsHuman implantsNeurons

Identifiers

PMID38468067

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.