Zanjan, Iran | Sunday, October 23, 2016   

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


Web Design and Programming in Computer Center of IASBS

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- 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.
2- 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.
3- 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.
4- 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.
5- 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 Poincaré 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.
Modify personal information