P034 Role of Synaptic Plasticity in the Emergence of Temporal Complexity in a Izhikevich Spiking Neural Network
Marco Cafiso*1,2, Paolo Paradisi2,3
1Department of Physics 'E. Fermi', University of Pisa, Largo Bruno Pontecorvo 3, I-56127, Pisa, Italy
2Institute of Information Science and Technologies ‘A. Faedo’, ISTI-CNR, Via G. Moruzzi 1, I-56124, Pisa, Italy
3BCAM-Basque Center for Applied Mathematics, Alameda de Mazarredo 14, E-48009, Bilbao, BASQUE COUNTRY, Spain
*Email: marco.cafiso@phd.unipi.it
Introduction
Neural avalanches exemplify intermittent behavior in brain dynamics through large-scale regional interactions and are crucial elements of brain dynamical behaviors. Originally introduced in the Self-Organized Criticality framework, these intermittent complex behaviors can also be examined through Temporal Complexity (TC) theory. Computational neural network models have become central in the neuroscience field. Izhikevich’s neuron model [1] provides a powerful yet simple framework for simulating networks with over 20 brain-like dynamic patterns, enabling studies of normal and pathological conditions. Our work analyzes the temporal complexity of neural avalanches and coincidence events in an Izhikevich Spiking Neural Network, comparing systems with and without Spike-Time Dependent Plasticity (STDP) [2] processes.
Methods
A network of 1,000 Izhikevich neurons with an excitatory-to-inhibitory ratio of 4:1 was developed, designing inhibitory synaptic connections to exert a stronger influence than their excitatory counterparts, reflecting physiological neural circuit dynamics. We subjected the network to six distinct input signals, including two containing complex events. We then measured and compared the temporal complexity of network responses both with and without STDP plasticity mechanisms activated. Our temporal complexity assessment methodology leverages neural avalanche and coincidence events to estimate multiple scaling indices [3]. These metrics provide quantitative measures of the system’s complexity.
Results
The analysis of scaling indices related to temporal complexity reveals variations in complexity within neural avalanches and coincidences in simulations that incorporate the STDP plasticity rule, compared to those where it is absent. Furthermore, the extent of the change in temporal complexity depends on the simulation’s input signal. Specifically, strong and continuous signals lead to a substantial change in temporal complexity when the STDP rule is present, whereas intermittent signals exhibit smaller variations in complexity due to STDP.
Discussion
These preliminary results on the complexity behaviors of a spiking neural network with or without the STDP plasticity rule highlight how topological changes in the network configuration, due to time-dependent plasticity rules, lead to changes in temporal complexity behaviors. These results suggest that neural plasticity, defined as changes in the network’s spatial configuration, can influence the temporal complexity levels of a neuronal network, providing insights into the dynamic interplay between structural adaptation and the emergence of temporal complex behaviors in spiking neural networks.
Acknowledgements
This work was supported by the Next-Generation-EU programme under the funding schemes PNRR-PE-AI scheme (M4C2, investment 1.3, line on AI) FAIR “Future Artificial Intelligence Research”, grant id PE00000013, Spoke-8: Pervasive AI.
References
[1] Eugene M. Izhikevich. “Simple model of spiking neurons”. In: IEEE Transactions on Neural Networks 14.6 (2003), pp. 1569–1572.
[2] Natalia Caporale and Yang Dan. “Spike Timing–Dependent Plasticity: A Hebbian Learning Rule”. In: Annual Review of Neuroscience 31.Volume 31, 2008 (2008), pp. 25–46.
[3] P. Paradisi and P. Allegrini. “Intermittency-Driven Complexity in Signal Processing”. In: Complexity and Nonlinearity in Cardiovascular Signals. Ed. by Riccardo Barbieri, Enzo Pasquale Scilingo, and Gaetano Valenza. Cham: Springer, 2017, pp. 161–195.
