Zanjan, Iran | Saturday, August 29, 2015   

Institute for Advanced Studies in Basic Sciences (IASBS)

No. 444, Yousef Sobouti Blvd.

P. O. Box 45195-1159 Zanjan Iran

F: (+98) 24 3315-5142

T: (+98) 24 33151


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>Department of Physics>
Department of Physics
Alireza Valizadeh  
Assistant Professor
Room: Physics 120
Tel: 33152120
Fax: 33152104
Personal Homepage

Research interests:
Delay induced synchronization in systems of non-identical coupled oscillators, correlation transfer in systems of non-identical coupled oscillators, synaptic plasticity and interacting effects of structure and dynamics in neuronal networks, effect of impurities on the nonlinear response of the regular networks, and the ambition: auditory system and neuroscience of language and music perception!

Research area:
Nonlinear phenomena in Condensed matter, Theoretical Neuroscience

1- Bayati, M., Valizadeh, A., Abbasian, A., Cheng, S., "Self-organization of synchronous activity propagation in neuronal networks driven by local excitation", Front. Comput. Neurosci, 9, 1-15, (2015).

Many experimental and theoretical studies have suggested that the reliable propagation of synchronous neural activity is crucial for neural information processing. The propagation of synchronous firing activity in so-called synfire chains has been studied extensively in feed-forward networks of spiking neurons. However, it remains unclear how such neural activity could emerge in recurrent neuronal networks through synaptic plasticity. In this study, we investigate whether local excitation, i.e., neurons that fire at a higher frequency than the other, spontaneously active neurons in the network, can shape a network to allow for synchronous activity propagation. We use two-dimensional, locally connected and heterogeneous neuronal networks with spike-timing dependent plasticity (STDP). We find that, in our model, local excitation drives profound network changes within seconds. In the emergent network, neural activity propagates synchronously through the network. This activity originates from the site of the local excitation and propagates through the network. The synchronous activity propagation persists, even when the local excitation is removed, since it derives from the synaptic weight matrix. Importantly, once this connectivity is established it remains stable even in the presence of spontaneous activity. Our results suggest that synfire-chain-like activity can emerge in a relatively simple way in realistic neural networks by locally exciting the desired origin of the neuronal sequence.
2- Ghasemi Esfahani, Z., Valizadeh , A., "Zero-Lag Synchronization Despite Inhomogeneities in a Relay System ", PLoS ONE, 9: (12), 1-22, (2014).

A novel proposal for the zero-lag synchronization of the delayed coupled neurons, is to connect them indirectly via a third relay neuron. In this study, we develop a Poincar├ę map to investigate the robustness of the synchrony in such a relay system against inhomogeneity in the neurons and synaptic parameters. We show that when the inhomogeneity does not violate the symmetry of the system, synchrony is maintained and in some cases inhomogeneity enhances synchrony. On the other hand if the inhomogeneity breaks the symmetry of the system, zero lag synchrony can not be preserved. In this case we give analytical results for the phase lag of the spiking of the neurons in the stable state.
3- Sadeghi , S., Valizadeh, A., "Synchronization of delayed coupled neurons in presence of inhomogeneity", Journal of Computational Neuroscience, 36, 55-66, (2014).

In principle, two directly coupled limit cycle oscillators can overcome mismatch in intrinsic rates and match their frequencies, but zero phase lag synchronization is just achievable in the limit of zero mismatch, i.e., with identical oscillators. Delay in communication, on the other hand, can exert phase shift in the activity of the coupled oscillators. In this study, we address the question of how phase locked, and in particular zero phase lag synchronization, can be achieved for a heterogeneous system of two delayed coupled neurons. We have analytically studied the possibility of inphase synchronization and near inphase synchronization when the neurons are not identical or the connections are not exactly symmetric. We have shown that while any single source of inhomogeneity can violate isochronous synchrony, multiple sources of inhomogeneity can compensate for each other and maintain synchrony. Numeric studies on biologically plausible models also support the analytic results.
4- Bolhasani, E., Azizi, Y., Valizadeh, A., "Direct connections assist neurons to detect correlation in small amplitude noises ", Front. Comput. Neurosci, 7, 108-, (2013).

We address a question on the effect of common stochastic inputs on the correlation of the spike trains of two neurons when they are coupled through direct connections. We show that the change in the correlation of small amplitude stochastic inputs can be better detected when the neurons are connected by direct excitatory couplings. Depending on whether intrinsic firing rate of the neurons is identical or slightly different, symmetric or asymmetric connections can increase the sensitivity of the system to the input correlation by changing the mean slope of the correlation transfer function over a given range of input correlation. In either case, there is also an optimum value for synaptic strength which maximizes the sensitivity of the system to the changes in input correlation.
5- Bayati, M., Valizadeh, A., "Effect of synaptic plasticity on the structure and dynamics of disordered networks of coupled neurons", Phys. Rev. E, 86, 011925-1-011925-7, (2012).

In an all-to-all network of integrate-and-fire neurons in which there is a disorder in the intrinsic oscillatory frequencies of the neurons, we show that through spike-timing-dependent plasticity the synapses which have the high-frequency neurons as presynaptic tend to be potentiated while the links originated from the low-frequency neurons are weakened. The emergent effective flowof directed connections introduces the high-frequency neurons as the more influential elements in the network and facilitates synchronization by decreasing the synaptic cost for onset of synchronization.
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