S. Bianchi

517 total citations
21 papers, 396 citations indexed

About

S. Bianchi is a scholar working on Electrical and Electronic Engineering, Cognitive Neuroscience and Artificial Intelligence. According to data from OpenAlex, S. Bianchi has authored 21 papers receiving a total of 396 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Electrical and Electronic Engineering, 10 papers in Cognitive Neuroscience and 9 papers in Artificial Intelligence. Recurrent topics in S. Bianchi's work include Advanced Memory and Neural Computing (18 papers), Neural dynamics and brain function (10 papers) and Ferroelectric and Negative Capacitance Devices (6 papers). S. Bianchi is often cited by papers focused on Advanced Memory and Neural Computing (18 papers), Neural dynamics and brain function (10 papers) and Ferroelectric and Negative Capacitance Devices (6 papers). S. Bianchi collaborates with scholars based in Italy, United States and France. S. Bianchi's co-authors include Daniele Ielmini, Giacomo Pedretti, Stefano Ambrogio, Nirmal Ramaswamy, Alessandro Calderoni, Valerio Milo, Roberto Carboni, Alessandro S. Spinelli, Shahin Hashemkhani and Alessandro Bricalli and has published in prestigious journals such as Nature Communications, Scientific Reports and IEEE Transactions on Electron Devices.

In The Last Decade

S. Bianchi

21 papers receiving 391 citations

Peers

S. Bianchi

EEE

CMN

Shaogang Hu China

Cory Merkel United States

Yaoyuan Wang China

Juseong Park South Korea

Xiangwen Wang China

Rajkumar Kubendran United States

Yann Beilliard Canada

Stanisław Woźniak Switzerland

Shaogang Hu China

S. Bianchi

409 ×1.2

EEE

166 ×1.2

CMN

73 ×0.6

80 ×0.8

44 ×1.7

Citations per year, relative to S. Bianchi S. Bianchi (= 1×) peers Shaogang Hu

Countries citing papers authored by S. Bianchi

Since Specialization

Citations

This map shows the geographic impact of S. Bianchi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by S. Bianchi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites S. Bianchi more than expected).

Fields of papers citing papers by S. Bianchi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by S. Bianchi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by S. Bianchi. The network helps show where S. Bianchi may publish in the future.

Co-authorship network of co-authors of S. Bianchi

This figure shows the co-authorship network connecting the top 25 collaborators of S. Bianchi. A scholar is included among the top collaborators of S. Bianchi based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with S. Bianchi. S. Bianchi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Ravelli, L., Marisa López‐Vallejo, M. Pasotti, et al.. (2024). Differential Phase Change Memory (PCM) Cell for Drift-Compensated In-Memory Computing. IEEE Transactions on Electron Devices. 71(12). 7447–7453. 3 indexed citations

López‐Vallejo, Marisa, M. Pasotti, P.L. Rolandi, et al.. (2024). Drift Compensation in Multilevel PCM for in-Memory Computing Accelerators. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 1–4. 5 indexed citations

Bianchi, S., Erika Covi, Alessandro Bricalli, et al.. (2023). A self-adaptive hardware with resistive switching synapses for experience-based neurocomputing. Nature Communications. 14(1). 1565–1565. 29 indexed citations

Bianchi, S., et al.. (2021). A Brain-Inspired Homeostatic Neuron Based on Phase-Change Memories for Efficient Neuromorphic Computing. Frontiers in Neuroscience. 15. 709053–709053. 15 indexed citations

Bianchi, S., et al.. (2021). A Drift-Resilient Hardware Implementation of Neural Accelerators Based on Phase Change Memory Devices. IEEE Transactions on Electron Devices. 68(12). 6076–6081. 11 indexed citations

Bianchi, S., Erika Covi, Alessandro Bricalli, et al.. (2021). Combining Accuracy and Plasticity in Convolutional Neural Networks Based on Resistive Memory Arrays for Autonomous Learning. IEEE Journal on Exploratory Solid-State Computational Devices and Circuits. 7(2). 132–140. 3 indexed citations

Bianchi, S., et al.. (2020). Bio-Inspired Techniques in a Fully Digital Approach for Lifelong Learning. Frontiers in Neuroscience. 14. 379–379. 9 indexed citations

Bianchi, S., Erika Covi, Alessandro Bricalli, et al.. (2020). A SiOx RRAM-Based Hardware with Spike Frequency Adaptation for Power-Saving Continual Learning in Convolutional Neural Networks. SPIRE - Sciences Po Institutional REpository. 1–2. 8 indexed citations

Bianchi, S., et al.. (2020). Hardware Implementation of PCM-Based Neurons with Self-Regulating Threshold for Homeostatic Scaling in Unsupervised Learning. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 529. 1–5. 10 indexed citations

10.

Bianchi, S., et al.. (2020). A Bio-Inspired Recurrent Neural Network with Self-Adaptive Neurons and PCM Synapses for Solving Reinforcement Learning Tasks. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 1–5. 9 indexed citations

11.

Demestichas, Konstantinos, et al.. (2020). Bringing Trust to Autonomous Mobility. Zenodo (CERN European Organization for Nuclear Research). 1–6. 5 indexed citations

12.

Bianchi, S., Giacomo Pedretti, Alessandro Calderoni, et al.. (2020). A Compact Model for Stochastic Spike-Timing-Dependent Plasticity (STDP) Based on Resistive Switching Memory (RRAM) Synapses. IEEE Transactions on Electron Devices. 67(7). 2800–2806. 14 indexed citations

13.

Bianchi, S., et al.. (2019). Energy-efficient continual learning in hybrid supervised-unsupervised neural networks with PCM synapses. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 1–2. 6 indexed citations

14.

Bianchi, S., et al.. (2019). Unsupervised Learning to Overcome Catastrophic Forgetting in Neural Networks. IEEE Journal on Exploratory Solid-State Computational Devices and Circuits. 5(1). 58–66. 25 indexed citations

15.

Bianchi, S., et al.. (2019). Energy-efficient continual learning in hybrid supervised-unsupervised neural networks with PCM synapses. T172–T173. 10 indexed citations

16.

Milo, Valerio, Giacomo Pedretti, Mario Laudato, et al.. (2018). Resistive switching synapses for unsupervised learning in feed-forward and recurrent neural networks. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 1–5. 12 indexed citations

17.

Pedretti, Giacomo, Valerio Milo, Stefano Ambrogio, et al.. (2017). Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity. Scientific Reports. 7(1). 132 indexed citations

18.

Pedretti, Giacomo, S. Bianchi, Valerio Milo, et al.. (2017). Modeling-based design of brain-inspired spiking neural networks with RRAM learning synapses. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 28.1.1–28.1.4. 20 indexed citations

19.

Pedretti, Giacomo, Valerio Milo, Stefano Ambrogio, et al.. (2017). Stochastic Learning in Neuromorphic Hardware via Spike Timing Dependent Plasticity With RRAM Synapses. IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 8(1). 77–85. 41 indexed citations

20.

Armillotta, Antonio, et al.. (2017). Edge quality in fused deposition modeling: II. experimental verification. Rapid Prototyping Journal. 23(4). 686–695. 18 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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