Tissue-like interfacing of planar electrochemical organic neuromorphic devices
Bibliographic record
Abstract
Abstract Electrochemical organic neuromorphic devices (ENODes) are rapidly developing as platforms for computing, automation, and biointerfacing. Resembling short- and long-term synaptic plasticity is a key characteristic in creating functional neuromorphic interfaces that showcase spiking activity and learning capabilities. This potentially enables ENODes to couple with biological systems, such as living neuronal cells and ultimately the brain. Before coupling ENODes with the brain, it is worth investigating the neuromorphic behavior of ENODes when they interface with electrolytes that have a consistency similar to brain tissue in mechanical properties, as this can affect the modulation of ion and neurotransmitter diffusion. Here, we present ENODEs based on different PEDOT:PSS formulations with various geometries interfacing with gel-electrolytes loaded with a neurotransmitter to mimic brain-chip interfacing. Short-term plasticity and neurotransmitter-mediated long-term plasticity have been characterized in contact with diverse gel electrolytes. We found that both the composition of the electrolyte and the PEDOT:PSS formulation used as gate and channel material play a crucial role in the diffusion and trapping of cations that ultimately modulate the conductance of the transistor channels. It was shown that paired pulse facilitation can be achieved in both devices, while long-term plasticity can be achieved with a tissue-like soft electrolyte, such as agarose gel electrolyte, on spin-coated ENODes. Our work on ENODe-gel coupling could pave the way for effective brain interfacing for computing and neuroelectronic applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".