Preprint 38/2013

Hopf bifurcation in the evolution of STDP-driven networks

Quansheng Ren, Kiran M. Kolwankar, Areejit Samal, and Jürgen Jost

Contact the author: Please use for correspondence this email.
Submission date: 08. Apr. 2013
Pages: 13
published in: Physical review / E, 86 (2012) 5, art-no. 056103 
DOI number (of the published article): 10.1103/PhysRevE.86.056103
with the following different title: Hopf bifurcation in the evolution of networks driven by spike-timing-dependent plasticity
Download full preprint: PDF (1536 kB)

We study the interplay of topology and dynamics in a neural network connected with spike-timing-dependent plasticity (STDP) synapses. Stimulated with periodic spike trains, the STDP-driven network undergoes a synaptic pruning process and evolves to a residual network. We examine the variation of topological and dynamical properties of the residual network by varying two key parameters of STDP: Synaptic delay and the ratio between potentiation and depression. Our extensive numerical simulations of the Leaky Integrate-and-Fire model show that there exists two regions in the parameter space. The first corresponds to fixed point configurations, where the distribution of peak synaptic conductances and the firing rate of neurons remain constant over time. The second corresponds to oscillating configurations, where both topological and dynamical properties vary periodically which is a result of a fixed point becoming a limit cycle via a Hopf bifurcation. This leads to interesting questions regarding the implications of these rhythms in the topology and dynamics of the network for learning and cognitive processing.

18.10.2019, 02:15