The DOI matches its Crossref record; 152 of the 152 references it cites were found in a registry or an index.
Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems
Open accessAbstract
Several analog and digital brain-inspired electronic systems have been recently proposed as dedicated solutions for fast simulations of spiking neural networks. While these architectures are useful for exploring the computational properties of large-scale models of the nervous system, the challenge of building low-power compact physical artifacts that can behave intelligently in the real world and exhibit cognitive abilities still remains open. In this paper, we propose a set of neuromorphic engineering solutions to address this challenge. In particular, we review neuromorphic circuits for emulating neural and synaptic dynamics in real time and discuss the role of biophysically realistic temporal dynamics in hardware neural processing architectures; we review the challenges of realizing spike-based plasticity mechanisms in real physical systems and present examples of analog electronic circuits that implement them;we describe the computational properties of recurrent neural networks and show how neuromorphic winner-take-all circuits can implement working-memory and decision-making mechanisms. We validate the neuromorphic approach proposed with experimental results obtained from our own circuits and systems, and argue how the circuits and networks presented in this work represent a useful set of components for efficiently and elegantly implementing neuromorphic cognition.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Adaptive Mixtures of Local Experts
Jacobs, R. A., Jordan, M. I., Nowlan, S. J., & Hinton, G. E. (1991) · Neural Computation · doi:10.1162/neco.1991.3.1.79 · Cited by 5,384
Short-Term Synaptic Plasticity
Zucker, R. S., & Regehr, W. G. (2002) · Annual Review of Physiology · doi:10.1146/annurev.physiol.64.092501.114547 · Cited by 5,068
Simple model of spiking neurons
Izhikevich, E. M. (2003) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2003.820440 · Cited by 4,828
Maass, W., Natschläger, T., & Markram, H. (2002) · Neural Computation · doi:10.1162/089976602760407955 · Cited by 4,025
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Dayan, P., & Abbott, L. F. (2001) · Cited by 3,153
Neuromorphic electronic systems
Mead, C. (1990) · Proceedings of the IEEE · doi:10.1109/5.58356 · Cited by 2,368
Synaptic plasticity: taming the beast
Abbott, L. F., & Nelson, S. B. (2000) · Nature Neuroscience · doi:10.1038/81453 · Cited by 2,263 · cited 1×
“This is the condition for linearity, under which the circuit starts to behave as a first order low-pass filter, as described in Section II.”
MethodModeling Brain Function: The World of Attractor Neural Networks
Amit, D. J. (1989) · Cambridge University Press eBooks · doi:10.1017/cbo9780511623257 · Cited by 2,227
Activity-dependent scaling of quantal amplitude in neocortical neurons
Turrigiano, G. G., Leslie, K. R., Desai, N. S., Rutherford, L. C., & Nelson, S. B. (1998) · Nature · doi:10.1038/36103 · Cited by 2,226
Cited by
Qiao, N., Mostafa, H., Corradi, F., Osswald, M., Stefanini, F., Sumislawska, D., & Indiveri, G. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00141 · Cited by 698
Self-citationMoradi, S., Qiao, N., Stefanini, F., & Indiveri, G. (2018) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2017.2759700 · Cited by 650
Self-citationNeural networks: An overview of early research, current frameworks and new challenges
Prieto, A., Prieto, B., Ortigosa, E. M., Ros, E., Pelayo, F., Ortega, J., & Rojas, I. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.06.014 · Cited by 391
Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing
John, R. A., Demirağ, Y., Shynkarenko, Y., Berezovska, Y., Ohannessian, N., Payvand, M., Zeng, P., Bodnarchuk, M. I., Krumeich, F., Kara, G., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29727-1 · Cited by 292
Self-citationExploring Neuromorphic Computing Based on Spiking Neural Networks: Algorithms to Hardware
Rathi, N., Chakraborty, I., Kosta, A., Sengupta, A., Ankit, A., Panda, P., & Roy, K. (2023) · ACM Computing Surveys · doi:10.1145/3571155 · Cited by 267
Metadata
- Authors
- Chicca, E., Stefanini, F., Bartolozzi, C., & Indiveri, G.
- Venue
- Proceedings of the IEEE
- Published
- 2014-09
- Volume
- 102
- Issue
- 9
- Pages
- 1367-1388
- ISSN
- 0018-9219 (ISSN-L)
- 1558-2256
- OpenAlex
- W2034602202
- Cited by
- 552
- References
- 152
- FWCI
- 18.93
- Type
- journal article
- Language
- en
- Open access
- green
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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems. https://app.cochat.ai/papers/10.1109/jproc.2014.2313954
- The paper
- Chicca, E., Stefanini, F., Bartolozzi, C., & Indiveri, G. (2014). Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems. Proceedings of the IEEE. https://doi.org/10.1109/jproc.2014.2313954
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
