Abstract
The use of neural modeling tools is becoming increasingly common in the exploration of human brain behavior, enabling effective simulations through event-driven methodologies. As a result, years of study and advancements in the field of neurotechnology have led to the creation of several artificial neural network approaches that mimic biological neural networks. The event-driven approach provides an effective method for mimicking large-scale spiking neural networks (SNNs), by taking advantage of the brain’s sparse processing. This paper investigates SNN employing a leaky integrate-and-fire neuron model with latency estimation through FNS. A three-layer feedforward network (FFN) is constructed, incorporating design parameters from Config Wizard. Notably, our study sheds light on the impact of synchrony within a simple FFN. Through the incorporation of biologically plausible delay effects, our model offers novel insights that complement the existing literature. Neural activity is organized in CSV format files, facilitating the reconstruction of electrophysiological-like signals. FNS enables a comprehensive exploration of interactions within and between populations of spiking neurons. In the near future, we intend to use these findings in situations where this particular class of neural networks and digital signal processing (DSP) applications can be combined to create potent nonlinear DSP techniques.
Similar content being viewed by others
References
F. Ponulak and A. Kasinski, Introduction to spiking neural networks: information processing, learning and applications. Acta Neurobiol. Exp. 71, 409 (2011).
W. Maass, Networks of spiking neurons: the third generation of neural network models. Neural Netw. 10, 1659 (1997).
R. Brette, M. Rudolph, T. Carnevale, M. Hines, D. Beeman, J.M. Bower, M. Diesmann, A. Morrison, P.H. Goodman, F.C. Harris Jr., M. Zirpe, T. Natschläger, D. Pecevski, B. Ermentrout, M. Djurfeldt, A. Lansner, O. Rochel, T. Vieville, E. Muller, A.P. Davison, S. El Boustani, and A. Destexhe, Simulation of networks of spiking neurons: a review of tools and strategies. J. Comput. Neurosci. 23, 349 (2007).
A. Aertsen, M. Diesmann, and M.-O. Gewaltig, Propagation of synchronous spiking activity in feedforward neural networks. J. Physiol. Paris 90, 243 (1996).
M. Diesmann, M.-O. Gewaltig, and A. Aertsen, Stable propagation of synchronous spiking in cortical neural networks. Nature 402, 529 (1999).
M.C. Van Rossum, G.G. Turrigiano, and S.B. Nelson, Fast propagation of firing rates through layered networks of noisy neurons. J. Neurosci. 22, 1956 (2002).
W.M. Kistler and W. Gerstner, Stable propagation of activity pulses in populations of spiking neurons. Neural Comput. 14, 987 (2002).
W. Maass, Paradigms for computing with spiking neurons, in Models of Neural Networks IV: Early Vision and Attention, p. 373–402 (Springer, 2002).
A. Kumar, S. Rotter, and A. Aertsen, Spiking activity propagation in neuronal networks: reconciling different perspectives on neural coding. Nat. Rev. Neurosci. 11, 615 (2010).
G.C. Cardarilli, A. Cristini, L. Di Nunzio, M. Re, M. Salerno, and G. Susi, Spiking neural networks based on LIF with latency: simulation and synchronization effects, in Conference Record—Asilomar Conference on Signals, Systems and Computers, p. 1838–1842 (IEEE, 2013).
G. Susi, A. Cristini, and M. Salerno, Path multimodality in a feedforward SNN module, using LIF with latency model. Neural Netw. World 26, 363 (2016).
G. Susi, Bio-inspired temporal-decoding network topologies for the accurate recognition of spike patterns. Trans. Mach. Learn. Artif. Intell. 3, 27 (2015).
A.N. Burkitt, A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input. Biol. Cybern. 95, 1 (2006).
P. Maršálek, C. Koch, and J. Maunsell, On the relationship between synaptic input and spike output jitter in individual neurons. Proc. Natl. Acad. Sci. 94, 735 (1997).
A.N. Burkitt and G.M. Clark, Analysis of integrate-and-fire neurons: synchronization of synaptic input and spike output. Neural Comput. 11, 871 (1999).
C.E. Carr, Processing of temporal information in the brain. Annu. Rev. Neurosci. 16, 223 (1993).
Y.-X. Guo and M. Kawasaki, Representation of accurate temporal information in the electrosensory system of the African electric fish, Gymnarchus niloticus. J. Neurosci. 17, 1761 (1997).
C.E. Carr, W. Heiligenberg, and G.J. Rose, A time-comparison circuit in the electric fish midbrain. I. Behavior and physiology. J. Neurosci. 6, 107 (1986).
G. Susi, P. Garcés, E. Paracone, A. Cristini, M. Salerno, F. Maestú, and E. Pereda, FNS allows efficient event-driven spiking neural network simulations based on a neuron model supporting spike latency. Sci. Rep. 11, 12160 (2021).
M. Forrester, J. Crofts, S. Sotiropoulos, S. Coombes, and R. O’Dea, The role of node dynamics in shaping emergent functional connectivity patterns in the brain. Netw. Neurosci. 4(2), 467 (2020).
R. FitzHugh, Mathematical models of threshold phenomena in the nerve membrane. Bull. Math. Biophys. 17, 257 (1955).
M. Salerno, G. Susi, and A. Cristini, Accurate latency characterization for very large asynchronous spiking neural networks. In BIOINFORMATICS 2011-Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms, p. 116–124 (SciTePress, 2011).
M. Salerno, G. Susi, A. Cristini, Y. Sanfelice, and A. D’Annessa, Spiking neural networks as continuous-time dynamical systems: fundamentals, elementary structures and simple applications. ACEEE Int. J. Inf. Technol. 3, 80 (2013).
A. Citri and R.C. Malenka, Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology 33, 18 (2008).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Hussain, S.A., Dhanush, K.S.S., Eswar, K.A. et al. Leaky Integrate-and-Fire Neuron Model-Based SNN Latency Estimation Using FNS. J. Electron. Mater. 53, 3560–3568 (2024). https://doi.org/10.1007/s11664-024-11078-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11664-024-11078-w