Self-organization of feed-forward structure and entrainment in excitatory neural networks with spike-timing-dependent plasticity

Abstract

Spike-timing dependent plasticity (STDP) is an organizing principle of biological neural networks. While synchronous firing of neurons is considered to be an important functional block in the brain, how STDP shapes neural networks possibly toward synchrony is not entirely clear.We examine relations between STDP and synchronous firing in spontaneously firing neural populations. Using coupled heterogeneous phase oscillators placed on initial networks, we show numerically that STDP prunes some synapses and promotes formation of a feedforward network.Eventually a pacemaker, which is the neuron with the fastest inherent frequency in our numerical simulations, emerges at the root of the feedforward network. In each oscillatory cycle, a packet of neural activity is propagated from the pacemaker to downstream neurons along layers of the feedforward network. This event occurs above a clear-cut threshold value of the initial synaptic weight. Below the threshold, neurons are self-organized into separate clusters each of which is a feedforward network.

Direct connections assist neurons to detect correlation in small amplitude noises

“…We considered an excitatory forward coupling from the high frequency neuron (as the presynaptic) to low frequency neuron (as the postsynaptic). In the absence of noise, any finite value of the forward coupling strength can lead to a zone of 1:1 synchrony, in which the dissimilar neurons fire in a causal master-slave fashion (Takahashi et al, 2009; Bayati and Valizadeh, 2012). In such causal limit the postsynaptic neuron fires immediately after receiving presynaptic stimulation (Woodman and Canavier, 2011; Wang et al, 2012).…”

“…How does STDP change the lateral connections transverse to the path of data flow? It has been shown that in the recurrent networks, asymmetric connections arise through STDP and in the presence of inhomogeneity, such an asymmetric change is in favor of the connection from the high frequency to the low frequency neuron (Takahashi et al, 2009; Bayati and Valizadeh, 2012). Our results show that asymmetric connections can enhance the performance of inhomogeneous systems in the detection of input correlation, and interestingly such an optimum configuration of connections emerges through STDP (with asymmetric profile) in inhomogeneous neuronal pools (Bayati and Valizadeh, 2012).…”

Hopf bifurcation in the evolution of networks driven by spike-timing-dependent plasticity

“…Adaptive networks arise in diverse areas ranging from neural learning [1,2] to social dynamics [3,4]. (For a review see Ref.…”

“…[13], it was shown that the functional architecture of brain can self-organize into small-world and scale-free network via STDP mediated synaptic reorganization. Takahashi et al [2] have studied the role of STDP in the development of feed-forward architecture in neural networks. Gilson et al [14] have developed a framework to analyze systematically the behaviour of STDP in recurrent networks.…”

Learning-rate-dependent clustering and self-development in a network of coupled phase oscillators

“…Recent theoretical efforts have studied how a network may develop in accordance with neural learning mechanisms in relation to the dynamics of synchronous cluster formation [21,24,25,26,27]. Neurophysiological studies have shown that a synapse is strengthened if the pre-synaptic neuron repeatedly causes the post-synaptic neuron to fire, leading to the Long Term Potentiation (LTP) of the synapse [28,29].…”