FNS

Diffusion

When you use FNS please cite:

Susi G, Garcés P, Paracone E, Cristini A, Salerno M, Maestú F, Pereda E. "FNS allows efficient event-driven spiking neural network simulations based on a neuron model supporting spike latency," Scientific Reports 11, 12160 (2021). open access.

Note that FNS can be used without limitation: you can change any part you want and re-distribute it, but always acknowledging the original authorship of the software and citing the original paper. Please read the license.

RELATED WORKS:

• Hussain, S.A., Dhanush, K.S.S., Eswar, K.A., Vaishnavi C., Sujith Surya Kaveti, Prasad V · Samanta S., Kumar Sanki, P. "Leaky Integrate-and-Fire Neuron Model-Based SNN Latency Estimation Using FNS" Journal of Electronic Materials, vol.53, 2024.
• Pimpini A., Piccione A., Ciciani B., and Pellegrini A. "Speculative Distributed Simulation of Very Large Spiking Neural Networks" SIGSIM-PADS '22: Proceedings of the 2022 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, 2022.
• Kamal M. S. , Dey N. , Chowdhury L. , Hasan S. I. and Santosh K. "Explainable AI for Glaucoma Prediction Analysis to Understand Risk Factors in Treatment Planning," IEEE Transactions on Instrumentation and Measurement, vol. 71, 2022.
• Yamazaki K., Vo-Ho V.K., Bulsara D., Le N. "Spiking Neural Networks and Their Applications: A Review," Brain Sciences, vol. 12, n.7, 2022.
• Susi G., Anton-Toro L., Canuet L., Lopez M.E., Maestu F., Mirasso C.R., and Pereda E. "A neuro-inspired system for online learning and recognition of parallel spike trains, based on spike latency and heterosynaptic STDP," Frontiers in Neuroscience, 12, 2018.
• Acciarito S., Cardarilli G.C., Cristini A., Di Nunzio L., Fazzolari R., Khanal G.M., Re M., Susi G., "Hardware design of LIF with latency neuron model with memristive STDP synapses," Integration, the VLSI Journal, 2017. Elsevier.
• Susi G., Cristini A., Salerno M., "Path multimodality in a feedforward SNN module, using LIF with Latency model," Neural Network World, vol. 26, n.4, 2016.
• Susi G., "Bio-inspired temporal-decoding network topologies for the accurate recognition of spike patterns," Transactions on Machine Learning and Artificial Intelligence, vol. 3 - No. 4, 2015.
• Cristini A., Salerno M., Susi G., "A continuous-time spiking neural network paradigm," Advances in Neural Networks: Computational and Theoretical Issues. Springer, 2015.
• Salerno M., Susi G., Cristini A., Sanfelice Y., D’Annessa A., "Spiking neural networks as continuous-time dynamical systems: fundamentals, elementary structures and simple applications," ACEEE Int. J. on Information Technology, Vol. 3 - No. 1. ACEEE, 2013.
• Salerno M., Susi G., D’Annessa A., Cristini A., Sanfelice Y., "Spiking neural networks as analog dynamical systems: basic paradigm and simple applications," in ACEEE ACE International Conference on Computer Science and Communication Devices, Amsterdam, The Netherlands, 2012.
• Salerno M., Susi G., Cristini A., "Accurate latency characterization for very large asynchronous spiking neural networks," in BIOSTEC BIOINFORMATICS International Conference on Bioinformatics Models, Methods and Algorithms , Rome, Italy, 2011.

RELATED AWARDS & RECOGNITIONS:

• Best Poster Award, "Continuous-time spiking neural networks: general paradigm and event-driven simulation" at 29st Annual Meeting of Electrical Engineering Researchers (ET2013), 2013. Padova, Italy.
• Best Paper Award, "Spiking neural networks as analog dynamical systems: basic paradigm and simple applications", in International Conference on Computer Science and Communication Devices, ACE (ACEEE), 2012. Amsterdam, The Netherlands.