Zanjan, Iran | Friday, July 28, 2017   

Institute for Advanced Studies in Basic Sciences (IASBS)

No. 444, Prof. Yousef Sobouti Blvd.

P. O. Box 45195-1159 Zanjan Iran

F: (+98) 24 3315-5142

T: (+98) 24 33151


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Home>Department of Physics>
Department of Physics
Alireza Valizadeh  
Associate Professor
Room: Physics Building 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- Bolhasani, E., Azizi, Y., Valizadeh, A., Perc, M., "Synchronization of oscillators through time-shifted common inputs", Phys. Rev. E, 95: (3), 032207-1-032207-6, (2017).

Shared upstream dynamical processes are frequently the source of common inputs in various physical and biological systems. However, due to finite signal transmission speeds and differences in the distance to the source, time shifts between otherwise common inputs are unavoidable. Since common inputs can be a source of correlation between the elements of multi-unit dynamical systems, regardless of whether these elements are directly connected with one another or not, it is of importance to understand their impact on synchronization. As a canonical model that is representative for a variety of different dynamical systems, we study limit-cycle oscillators that are driven by stochastic time-shifted common inputs. We show that if the oscillators are coupled, time shifts in stochastic common inputs do not simply shift the distribution of the phase differences, but rather the distribution actually changes as a result. The best synchronization is therefore achieved at a precise intermediate value of the time shift, which is due to a resonance-like effect with the most probable phase difference that is determined by the deterministic dynamics.
2- Madadi Asl, M., Valizadeh, A., Tass, P. A., "Dendritic and Axonal Propagation Delays Determine Emergent Structures of Neuronal Networks with Plastic Synapses", Scientific Reports , 7, 39682-1-39682-12, (2017).

Spike-timing-dependent plasticity (STDP) modifies synaptic strengths based on the relative timing of pre- and postsynaptic spikes. The temporal order of spikes turned out to be crucial. We here take into account how propagation delays, composed of dendritic and axonal delay times, may affect the temporal order of spikes. In a minimal setting, characterized by neglecting dendritic and axonal propagation delays, STDP eliminates bidirectional connections between two coupled neurons and turns them into unidirectional connections. In this paper, however, we show that depending on the dendritic and axonal propagation delays, the temporal order of spikes at the synapses can be different from those in the cell bodies and, consequently, qualitatively different connectivity patterns emerge. In particular, we show that for a system of two coupled oscillatory neurons, bidirectional synapses can be preserved and potentiated. Intriguingly, this finding also translates to large networks of type-II phase oscillators and, hence, crucially impacts on the overall hierarchical connectivity patterns of oscillatory neuronal networks.
3- G. Esfahani, Z., Gollo, L., Valizadeh, A., "Stimulus-dependent synchronization in delayed-coupled neuronal networks", Scientific Reports: (6), 23471-1-23471-10, (2016).

Time delay is a general feature of all interactions. Although the effects of delayed interaction are often neglected when the intrinsic dynamics is much slower than the coupling delay, they can be crucial otherwise. We show that delayed coupled neuronal networks support transitions between synchronous and asynchronous states when the level of input to the network changes. The level of input determines the oscillation period of neurons and hence whether time-delayed connections are synchronizing or desynchronizing. We find that synchronizing connections lead to synchronous dynamics, whereas desynchronizing connections lead to out-of-phase oscillations in network motifs and to frustrated states with asynchronous dynamics in large networks. Since the impact of a neuronal network to downstream neurons increases when spikes are synchronous, networks with delayed connections can serve as gatekeeper layers mediating the firing transfer to other regions. This mechanism can regulate the opening and closing of communicating channels between cortical layers on demand.
4- Bolhasani, E., Valizadeh, A., "Stabilizing synchrony by inhomogeneity", Scientific Reports, 5, 13854-1-13854-7, (2015).

We show that for two weakly coupled identical neuronal oscillators with strictly positive phase resetting curve, isochronous synchrony can only be seen in the absence of noise and an arbitrarily weak noise can destroy entrainment and generate intermittent phase slips. Small inhomogeneity–mismatch in the intrinsic firing rate of the neurons–can stabilize the phase locking and lead to more precise relative spike timing of the two neurons. The results can explain how for a class of neuronal models, including leaky integrate-fire model, inhomogeneity can increase correlation of spike trains when the neurons are synaptically connected.
5- Zarepour, M., D. Niry, M., Valizadeh, A., "Functional scale-free networks in the two-dimensional Abelian sandpile model", Phys. Rew. E., 92: (1), 012822-1-012822-6, (2015).

Recently, the similarity of the functional network of the brain and the Ising model was investigated by Chialvo [Nat. Phys. 6, 744 (2010)]. This similarity supports the idea that the brain is a self-organized critical system. In this study we derive a functional network of the two-dimensional Bak-Tang-Wiesenfeld sandpile model as a self-organized critical model, and compare its characteristics with those of the functional network of the brain, obtained from functional magnetic resonance imaging.
1- Goodarzi Nick, A., Niry, M. D., Valizadeh, A., "Scale-free functional networks of 2D Ising model are highly robust against structural defects: neuroscience implications/ Sharpee, et al., BMC Neuroscience 17 (Suppl 1), 54, P41", 25th Annual Computational Neuroscience Meeting: CNS‑2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 2016, 33-, (2016).

In recent years, several experimental observations have confirmed the emergence of self-organized criticality (SOC) in the brain at different scales [1]. At large scale, functional brain networks obtained from fMRI data have shown that node-degree distributions and probability of finding a link versus distance are indicative of scale-free and small-world networks regardless of the tasks in which the subjects were involved [2]. At small scale, the study of neuronal avalanches in networks of living neurons revealed power-law behavior in both spatial and temporal scales [3]. It is also shown that functional networks of the brain are strikingly similar to those derived from the 2D Ising model at critical temperature [4] and the 2D abelian sandpile model [5]
2- Pariz, A., Sadat Parsi, S., Valizadeh, A., "High frequency neuron can facilitate propagation of signal in neural networks/Sharpee, et al., BMC Neuroscience 17 (Suppl 1), 54, P42", 25th Annual Computational Neuroscience Meeting: CNS‑2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 2016, 33-, (2016).

Signal transmission is of interest from both fundamental and clinical perspective and has been well studied in nonlinear science and complex networks [1, 2]. In particular, in nervous systems, cognitive processing involves signal propagation through multiple brain regions and the activation of large numbers of specific neurons [3–6]. In information propagation through brain regions, each part, known as generator, activated locally as information comes to it from neighboring generators. Although the problem is well studied in the context of complex networks, our focus here is on the effect of the intrinsic dynamical properties of the reciprocal generators on the propagation of signal
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