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Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit
Abstract
Digital circuits such as the flip-flop use feedback to achieve multistability and nonlinearity to restore signals to logical levels, for example 0 and 1. Analogue feedback circuits are generally designed to operate linearly, so that signals are over a range, and the response is unique. By contrast, the response of cortical circuits to sensory stimulation can be both multistable and graded. We propose that the neocortex combines digital selection of an active set of neurons with analogue response by dynamically varying the positive feedback inherent in its recurrent connections. Strong positive feedback causes differential instabilities that drive the selection of a set of active neurons under the constraints embedded in the synaptic weights. Once selected, the active neurons generate weaker, stable feedback that provides analogue amplification of the input. Here we present our model of cortical processing as an electronic circuit that emulates this hybrid operation, and so is able to perform computations that are similar to stimulus selection, gain modulation and spatiotemporal pattern generation in the neocortex.
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“This network with ReLUs has the ability to get trained several times faster than its equivalent with tanh units [37] as well as it also allows to go deeper with vanishing gradient problems.”
From Medical image retrieval using deep convolutional neural network (2017) · cites this paper as [37]
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Metadata
- Authors
- Hahnloser, R. H. R., Sarpeshkar, R., Mahowald, M. A., Douglas, R. J., & Seung, H. S.
- Affiliations
- ETH Zurich (CH)
- Massachusetts Institute of Technology (US)
- Institute of Neuroinformatics (CH)
- Corresponding author
- Hahnloser, Richard H. R.
- Venue
- Nature
- Published
- 2000-06-22
- Volume
- 405
- Issue
- 6789
- Pages
- 947-951
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W1572063013
- Cited by
- 1,293
- References
- 28
- Citations by year
- 2026: 31 · 2025: 57 · 2024: 90 · 2023: 94 · 2022: 109 · 2021: 188 · 2020: 217 · 2019: 163 · 2018: 127 · 2017: 60 · 2016: 3 · 2015: 9 · 2014: 12 · 2013: 5 · 2012: 3
- FWCI
- 6.38
- Type
- journal article
- Language
- en
- Update notices
- erratum (2000-12-21)
- Open access
- closed
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Analog and Mixed-Signal Circuit Design
- Keywords
- cortical circuits, positive feedback, multistability, stimulus selection, gain modulation, recurrent connectivity, synaptic weights, neuromorphic hardware
- MeSH terms
- Electrophysiology, Models, Neurological, Nerve Net, Neurons, Silicon, Neural Networks, Computer, Neocortex
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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