Zanjan, Iran | Monday, January 22, 2018   

Institute for Advanced Studies in Basic Sciences (IASBS)

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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- Pariz, A., Esfahani, Z. G., Sadat Parsi, S., Valizadeh, A., Canals, S., Mirasso, C. R., "High frequency neurons determine effective connectivity in neuronal networks", NeuroImage, 166, 349-359, (2018).

The emergence of flexible information channels in brain networks is a fundamental question in neuroscience. Understanding the mechanisms of dynamic routing of information would have far-reaching implications in a number of disciplines ranging from biology and medicine to information technologies and engineering. In this work, we show that the presence of a node firing at a higher frequency in a network with local connections, leads to reliable transmission of signals and establishes a preferential direction of information flow. Thus, by raising the firing rate a low degree node can behave as a functional hub, spreading its activity patterns polysynaptically in the network. Therefore, in an otherwise homogeneous and undirected network, firing rate is a tunable parameter that introduces directionality and enhances the reliability of signal transmission. The intrinsic firing rate across neuronal populations may thus determine preferred routes for signal transmission that can be easily controlled by changing the firing rate in specific nodes. We show that the results are generic and the same mechanism works in the networks with more complex topology.
2- Moosavi, S. A., Montakhab, A., Valizadeh, A., "Refractory period in network models of excitable nodes: self-sustaining stable dynamics, extended scaling region and oscillatory behavior", Scientific Reports, 7, 7107-1-7107-10, (2017).

Networks of excitable nodes have recently attracted much attention particularly in regards to neuronal dynamics, where criticality has been argued to be a fundamental property. Refractory behavior, which limits the excitability of neurons is thought to be an important dynamical property. We therefore consider a simple model of excitable nodes which is known to exhibit a transition to instability at a critical point (λ = 1), and introduce refractory period into its dynamics. We use mean-field analytical calculations as well as numerical simulations to calculate the activity dependent branching ratio that is useful to characterize the behavior of critical systems. We also define avalanches and calculate probability distribution of their size and duration. We find that in the presence of refractory period the dynamics stabilizes while various parameter regimes become accessible. A sub-critical regime with λ < 1.0, a standard critical behavior with exponents close to critical branching process for λ = 1, a regime with 1 < λ < 2 that exhibits an interesting scaling behavior, and an oscillating regime with λ > 2.0. We have therefore shown that refractory behavior leads to a wide range of scaling as well as periodic behavior which are relevant to real neuronal dynamics.
3- 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.
4- 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.
5- 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.
1- Madadi Asl, M., Valizadeh, A., Tass, P. A., "Interplay between propagation delay and frequency of oscillation determines emergent structures of neuronal networks driven by triplet-based STDP", BMC Neuroscience 18(Suppl 1): P264, (2017).

Spike-timing-dependent plasticity (STDP) adjusts synaptic strengths according to the relative timing of pre- and postsynaptic spikes [1]. The classic STDP rule eliminates bidirectional connections between two coupled neurons and turns them into unidirectional connections [2, 3]. As shown recently, by taking into account dendritic and axonal propagation delays, the conventional pair-based additive STDP may lead to both unidirectional and bidirectional connections, or decouple neurons by weakening reciprocal connections in both directions [4]. The triplet-based STDP, however, employs triplets of spikes that modify synaptic strengths [5]. Hence, the latter captures the effect of frequency of oscillations on the pre-post pairing. Here, we provide a general theoretical framework by assuming that the neurons are phase-locked with a phase lag which is determined by the temporary values of the synaptic strengths, propagation delays, frequency of oscillation, and the phase sensitivity of the neurons, and explore how the final configuration of the system can be predicted. In the absence of propagation delays, low-frequency oscillation leads to unidirectional connection for both pair- and triplet-based STDP. However, for higher frequencies, the triplet-based model has a tendency to achieve bidirectional connections, but results for the pair-based are the same as in the low-frequency regime. We show that employing triplet-based STDP leads to diverse connectivity patterns of oscillatory neurons in the presence of propagation delays, which qualitatively differ from the results obtained by pair-based STDP. In particular, large axonal propagation delay in the high-frequency regime is associated with a stable decoupling of both reciprocal synapses when the neurons are in a phase-locked state (see Figure 1F).
2- Goodarzi Nick, A., Madadi Asl, M., Valizadeh, A., "Multi-cluster structure and dynamics in networks of coupled phase oscillators through different classes of STDP profiles", BMC Neuroscience 18(Suppl 1): P278, (2017).

In the brain networks, strength of synaptic connections is adjusted according to relative spike timing of pre- and post- synaptic neurons, known as spike-timing-dependent plasticity (STDP) [1]. The theoretical and simulational studies have shown different emergent collective activity and patterns of connectivity when using different STDP profiles with different set of parameters. For example, depending on the parameter set, STDP can either promote or oppose synchrony [2]. It also might lead to potentiation of bidirectional connections or eliminate two-neuron loops by depressing one of the connections for every pair of reciprocally connected neurons [3]. Here, we have inspected how different biologically observed STDP profiles can result in different emergent dynamics. Based on Kuramoto model for description of the phase dynamics in neuronal ensembles [4, 5], we have designed classes of plasticity functions with symmetric and anti-symmetric profiles, mimicking typical forms of STDP for excitatory and inhibitory connections (see Figure 1). Related sets of differential equations for evolution of the phases and the synapses are analytically solved to justify results of numerical simulations. The main observation is that while anti-symmetric profile promotes one structural cluster with almost synchronized dynamics, symmetric profiles lead to multi-cluster structure with various phase relations within and between the clusters.
3- 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]
4- 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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