Here is a summary of my academic publications. You may also find new articles in my Google Scholar or Semantic Scholar profiles.
Publications
[In review] Garg, N., Balafrej, I., Palhares, J., Begon-Lours, L., Florini, D., Falcone, D., Stecconi, T., Bragaglia, V., Offrein, B., Portal, J.-M., Querlioz, D., Beilliard, Y., Drouin, D., Alibart, F. Unsupervised Local Learning Based on Voltage-Dependent Synaptic Plasticity for Resistive and Ferroelectric Synapses. (2024) https://dx.doi.org/10.21203/rs.3.rs-5295706/v1
Balafrej, I., Dahmane, M. Enhancing Practicality and Efficiency of Deepfake Detection. Scientific Reports. (2024). https://doi.org/10.1038/s41598-024-82223-y.
Balafrej, I., Janzakova, K., Kumar, A., Garg, N., Scholaert, C., Rouat, J., Drouin, D., Coffinier, Y., Pecqueur, S., and Alibart, F. Structural Plasticity for Neuromorphic Networks with Electropolymerized Dendritic PEDOT Connections. Nature Communications. (2023) https://doi.org/10.1038/s41467-023-43887-8
[In review] Balafrej, I., Alibart, F. & Rouat, J. Expanding memory in recurrent spiking networks (2023) https://doi.org/10.48550/arXiv.2310.19067
Goupy, G., Juneau-Fecteau, A., Garg, N., Balafrej, I., Alibart, F., Frechette, L., Drouin, D., Beilliard, Y. Unsupervised and efficient learning in sparsely activated convolutional spiking neural networks enabled by voltage-dependent synaptic plasticity. Neuromorph. Comput. Eng. (2023) https://doi.org/10.1088/2634-4386/acad98
Garg, N., Balafrej, I., Stewart C. T., Portal, JM. Bocquet, M., Querlioz, D., Drouin, D., Rouat, J., Beillard, Y., Alibart, F. Voltage-Dependent Synaptic Plasticity (VDSP): Unsupervised probabilistic Hebbian plasticity rule based on neurons membrane potential. Frontiers in Neuroscience (2022) https://doi.org/10.3389/fnins.2022.983950.
Balafrej, I., Alibart, F. & Rouat, J. P-CRITICAL: a reservoir autoregulation plasticity rule for neuromorphic hardware. Neuromorph. Comput. Eng. (2022). https://doi.org/10.1088/2634-4386/ac6533.
Garg, N., Balafrej, I., Beilliard, Y., Drouin, D., Alibart, F., and Rouat, R. Signals to Spikes for Neuromorphic Regulated Reservoir Computing and EMG Hand Gesture Recognition. International Conference on Neuromorphic Systems 2021, 1–8. ICONS 2021 29. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3477145.3477267.
Celotti L., Balafrej, I., Calvet, E. Improving Zero-Shot Neural Architecture Search with Parameters Scoring (2020).
Presentations
Garg, N., Balafrej, I., Beilliard, Y., Drouin, D., Alibart, F., Rouat. Unsupervised Learning with Ferroelectric Synapses. IEEE 52nd European Solid-State Device Research Conference (2022).
Balafrej, I., Alibart, F. Rouat, J. Neuromorphic Reservoirs for Energy-Efficient Task-Abstract Machine Learning Devices. Neural Interfaces and Systems Symposium (2021).
Balafrej, I. Building hardware-friendly models for neuromorphic engineering. Workshop at the University of Montreal (2021).
Garg, N., Balafrej, I., Beilliard, Y., Drouin, D., Alibart, F., and Rouat, R. Signals to Spikes for Neuromorphic Regulated Reservoir Computing and EMG Hand Gesture Recognition. International Conference on Neuromorphic Systems 2021, 1–8. ICONS 2021 29. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3477145.3477267.
Balafrej, I., Rouat, J. P-CRITICAL: A Reservoir Autoregulation Plasticity Rule Designed for Loihi. Intel Neuromorphic Research Community Forum (2020).
Balafrej, I. Machine learning on neuromorphic processors. 88e Congrès de l’Acfas. (CANCELED - COVID, 2020)
Balafrej, I. Auto-regulation of Reservoirs with Potential Physiological Signal Monitoring. UNIQUE Symposium (2020)
Balafrej, I., Rouat, J. Running the CRITICAL Plasticity Rule for Loihi. 3rd Intel Neuromorphic Research Community Workshop, Austria (2019).
Tremblay, J.P., Balafrej, I., Labelle, F., Martel-Denis, F., Matte, É., Chouinard-Beaupré, J., Létourneau, A., Mercier-Nicol, A., Brodeur, S., Ferland, F., Rouat, J. A Cooperative Visually Grounded Dialogue Game with a Humanoid Robot. (2018). Demonstration track, Thirty-third Conference on Neural Information Processing Systems (NeurIPS). Abstract