Publications supported by NEUREKA funding

2024

  1. Bettamin, L., Mathieu, F., Marty, F. H., Blatche, M. C., Gonzalez‐Dunia, D., Suberbielle, E., & Larrieu, G. (2024). Real‐Time and High‐Resolution Monitoring of Neuronal Electrical Activity and pH Variations Based on the Co‐Integration of Nanoelectrodes and Chem‐FinFETs. Small, 20(27). https://doi.org/10.1002/smll.202309055
  2. Pagkalos, M., Makarov, R., & Poirazi, P. (2024). Leveraging dendritic properties to advance machine learning and neuro-inspired computing. Current Opinion in Neurobiology, 85, 102853. https://doi.org/10.1016/j.conb.2024.102853
  3. Chavlis, S., & Poirazi, P. (2024). Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning. ArXiv, abs/2404.03708.

2023

  1. Di Palma, V., Pianalto, A., Perego, M., Tallarida, G., Codegoni, D., & Fanciulli, M. (2023). Plasma-Assisted Atomic Layer Deposition of IrO2 for Neuroelectronics. Nanomaterials, 13(6), 976. https://doi.org/10.3390/nano13060976
  2. Kastellakis, G., Tasciotti, S., Pandi, I., & Poirazi, P. (2023). The dendritic engram. Frontiers in Behavioral Neuroscience, 17. https://doi.org/10.3389/fnbeh.2023.1212139
  3. Makarov, R., Pagkalos, M., & Poirazi, P. (2023). Dendrites and efficiency: Optimizing performance and resource utilization. Current Opinion in Neurobiology, 83, 102812. https://doi.org/10.1016/j.conb.2023.102812
  4. Malakasis, N., Chavlis, S., & Poirazi, P. (2023). Synaptic turnover promotes efficient learning in bio-realistic spiking neural networks. bioRxiv : the preprint server for biology, 2023.05.22.541722. https://doi.org/10.1101/2023.05.22.541722
  5. Pagkalos, M., Chavlis, S., & Poirazi, P. (2023). Introducing the Dendrify framework for incorporating dendrites to spiking neural networks. Nature Communications, 14(1), 131. https://doi.org/10.1038/s41467-022-35747-8
  6. Tzilivaki, A., Tukker, J. J., Maier, N., Poirazi, P., Sammons, R. P., & Schmitz, D. (2023). Hippocampal GABAergic interneurons and memory. Neuron, 111(20), 3154–3175. https://doi.org/10.1016/j.neuron.2023.06.016
  7. Bilash, O. M., Chavlis, S., Johnson, C. D., Poirazi, P., & Basu, J. (2023). Lateral entorhinal cortex inputs modulate hippocampal dendritic excitability by recruiting a local disinhibitory microcircuit. Cell Reports, 42(1), 111962. https://doi.org/10.1016/j.celrep.2022.111962
  8. Muguet, I., Maziz, A., Mathieu, F., Mazenq, L., & Larrieu, G. (2023). Combining PEDOT:PSS Polymer Coating with Metallic 3D Nanowires Electrodes to Achieve High Electrochemical Performances for Neuronal Interfacing Applications. Advanced Materials, 35(39). https://doi.org/10.1002/adma.202302472

2022

  1. Vallicelli, E. A., Di Palma, V., De Matteis, M., Baschirotto, A., & Fanciulli, M. (2022). A 0.46 nV/√Hz JFET Low-Noise Amplifier for Characterization of Nanoelectrode Coating Materials. 2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS), 1–4. https://doi.org/10.1109/ICECS202256217.2022.9970943
  2. Lecestre, A., Martin, M., Cristiano, F., Baron, T., & Larrieu, G. (2022). Large-Scale Monolithic Fabrication of III–V Vertical Nanowires on a Standard Si(100) Microelectronic Substrate. ACS Omega, 7(7), 5836–5843. https://doi.org/10.1021/acsomega.1c05876
  3. Chowdhury, A., Luchetti, A., Fernandes, G., Filho, D. A., Kastellakis, G., Tzilivaki, A., Ramirez, E. M., Tran, M. Y., Poirazi, P., & Silva, A. J. (2022). A locus coeruleus-dorsal CA1 dopaminergic circuit modulates memory linking. Neuron, 110(20), 3374-3388.e8. https://doi.org/10.1016/j.neuron.2022.08.001
  4. Sehgal, M., Almeida, Filho, D., Martin, S., Mejia, I.D., Kastellakis, G., Kim, S., Lee, J., Pekcan, A., Huang, S., Lavi, A., Do, Heo W., Poirazi, P., Trachtenberg, J.T., Silva, A.J. (2022). Co-allocation to overlapping dendritic branches in the retrosplenial cortex integrates contextual memories across time. bioRxiv : the preprint server for biology, https://doi.org/10.1101/2021.10.28.466343

2021

  1. Yuan, X., Hierlemann, A., & Frey, U. (2021). Extracellular Recording of Entire Neural Networks Using a Dual-Mode Microelectrode Array With 19 584 Electrodes and High SNR. IEEE Journal of Solid-State Circuits, 56(8), 2466–2475. https://doi.org/10.1109/JSSC.2021.3066043
  2. Chavlis, S., & Poirazi, P. (2021). Drawing inspiration from biological dendrites to empower artificial neural networks. Current Opinion in Neurobiology, 70, 1–10. https://doi.org/10.1016/j.conb.2021.04.007
  3. Pinitas, K., Chavlis, S., Poirazi, P. (2021). Dendritic Self-Organizing Maps for Continual Learning. arXivLabs, https://doi.org/10.48550/arXiv.2110.13611

2020

  1. Geiller, T., Vancura, B., Terada, S., Troullinou, E., Chavlis, S., Tsagkatakis, G., Tsakalides, P., Ócsai, K., Poirazi, P., Rózsa, B. J., & Losonczy, A. (2020). Large-Scale 3D Two-Photon Imaging of Molecularly Identified CA1 Interneuron Dynamics in Behaving Mice. Neuron, 108(5), 968-983.e9. https://doi.org/10.1016/j.neuron.2020.09.013