New attractor states for synchronous activity in synfire chains with excitatory and inhibitory coupling

Yazdanbakhsh, Arash; Babadi, Baktash; Rouhani, S.; Arabzadeh, Ehsan; Abbassian, Abdolhosein

doi:10.1007/s00422-001-0293-y

Cited by 16 publications

(15 citation statements)

References 37 publications

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“…In contrast, nontrivial limit cycles or chaotic activity was not observed in the current model. The results of Yazdanbakhsh et al (2002) are consistent with a stochastic steady state, which they failed to discriminate from chaotic activity. Note that the extrapolation of the transfer function in the lower range of input (that is, when 0 < n µ−1 < 1) used in their model is not realistic.…”

Section: Dynamics Of Randomly Connected Network With Inhibitory Couplingsupporting

confidence: 86%

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Information coding and oscillatory activity in synfire neural networks with and without inhibitory coupling

Moradi

2004

Biol. Cybern.

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When a population spike (pulse-packet) propagates through a feedforward network with random excitatory connections, it either evolves to a sustained stable level of synchronous activity or fades away (Diesmann et al. in Nature 402:529-533 1999; Cateau and Fukai Neur Netw 14:675-685 2001). Here I demonstrate that in the presence of noise, the probability of the survival of the pulse-packet (or, equivalently, the firing rate of output neurons) reflects the intensity of the input. Furthermore, inhibitory coupling between layers can result in quasi- periodic alternation between several levels of firing activity. These results are obtained by analyzing the evolution of pulse-packet activity as a Markov chain. For the Markov chain analysis, the output of the chain is a linear mapping of the input into a lower-dimensional space, and the eigenvalues and eigenvectors of the transition matrix determine the dynamics of the evolution. Synchronous propagation of firing activity in successive pools of neurons are simulated in networks of integrate-and-fire and compartmental model neurons, and, consistent with the discrete Markov process, the activation of each pool is observed to be predominantly dependent upon the number of cells that fired in the previous pool. Simulation results agree with the numerical solutions of the Markov model. When inhibitory coupling between layers are included in the Markov model, some eigenvalues become complex numbers, implying oscillatory dynamics. The quasiperiodic dynamics is validated with simulation with leaky integrate-and-fire neurons. The networks demonstrate different modes of quasiperiodic activity as the inhibition or excitation parameters of the network are varied.

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Section: Dynamics Of Randomly Connected Network With Inhibitory Couplingsupporting

confidence: 86%

“…The transition matrix may have a characteristic period of two. Yazdanbakhsh et al (2002) have reported simulation results that seem to indicate chaotic and quasiperiodic activity in this regime. In contrast, nontrivial limit cycles or chaotic activity was not observed in the current model.…”

Section: Dynamics Of Randomly Connected Network With Inhibitory Couplingmentioning

confidence: 91%

Information coding and oscillatory activity in synfire neural networks with and without inhibitory coupling

Moradi

2004

Biol. Cybern.

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“…Macroscopic chaos mentioned above arises from the network's global properties, the 306 propensity of isolated neurons to oscillate, the nature of synaptic dynamics, or a 307 mixture of the them, as shown in earlier works [25][26][27][28][29]. In this paper, the focus is 308 different.…”

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confidence: 88%

“…More recently, a totally different mechanism showed that asynchronous 22 chaos, where neurons exhibit asynchronous chaotic firing-rate fluctuations, emerge 23 generically from balanced networks with multiple time scales synaptic dynamics [20]. 24 Different modeling approaches have been used to uncover important conditions for 25 observing these types of chaotic behavior (in particular, synchronous and asynchronous 26 chaos) in neural networks, such as the synaptic strength [25][26][27] in a network, 27 heterogeneity of the numbers of synapses and their synaptic strengths [28,29], and lately 28 the balance of excitation and inhibition [21] . The results obtained by Sompolinsky et 29 al.…”

mentioning

confidence: 99%

“…[25] showed that, when the synaptic strength is increased, neural networks displays a 30 highly heterogeneous chaotic state via a transition from an inactive state. Some other 31 studies demonstrated that chaotic behavior emerges in the presence of weak and strong 32 heterogeneities, for example a coupled heterogeneous population of neural oscillators 33 and the difference of their synaptic strengths [28][29][30]. Recently, Kadmon et al [21] 34 highlighted the importance of balance of excitation and inhibition on a transition to 35 chaos in random neural networks.…”

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confidence: 99%

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Is chaos making a difference? Synchronization transitions in chaotic and nonchaotic neuronal networks

Maidana

Castro

et al. 2017

Preprint

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Chaotic dynamics of neural oscillations has been shown at the single neuron and network levels, both in experimental data and numerical simulations. Theoretical studies over the last twenty years have demonstrated an underlying role of chaos in neural systems. Nevertheless, whether chaotic neural oscillators make a significant contribution to relevant network behavior and whether the dynamical richness of neural networks are sensitive to the dynamics of isolated neurons, still remain open questions. We investigated transition dynamics of a medium-sized heterogeneous neural network of neurons connected by electrical coupling in a small world topology. We make use of an oscillatory neuron model (HB+I h ) that exhibits either chaotic or non-chaotic behavior at different combinations of conductance parameters. Measuring order parameter as a measure of synchrony, we find that the heterogeneity of firing rate and types of firing patterns make a greater contribution than chaos to the steepness of synchronization transition curve. We also show that chaotic dynamics of the isolated neurons do not always make a visible difference in process of network synchronization transitions. Moreover, the macroscopic chaos is observed regardless of the dynamics nature of the neurons. However, performing a Functional Connectivity Dynamics analysis, we show that chaotic nodes can promote what is known as the multi-stable behavior, where the network dynamically switches between a number of different semi-synchronized, metastable states.

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Stochastic optimization of a biologically plausible spino-neuromuscular system model

Gotshall

Browder

Sampson

et al. 2007

Genet Program Evolvable Mach

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Simulations and modeling techniques are becoming increasingly important in understanding the behavior of biological systems. Detailed models help researchers answer questions in diverse areas such as the behavior of bacteria and viruses and aiding in the diagnosis and treatment of injuries and diseases. However, to yield meaningful biological behavior, biological simulations often include hundreds of parameters that correspond to biological components and characteristics. This paper demonstrates the effectiveness of genetic algorithms (GA) and particle swarm optimizer (PSO) based techniques in training biologically plausible behavior in a neuromuscular simulation of a biceps/triceps pair. The results are compared to human subjects during flexion/extension movements to show that these algorithms are effective in training biologically plausible behaviors on both neural and gross anatomical levels. Specific behaviors of interest that emerge include tonic tensions in both muscles during resting periods, biceps/triceps coactivation patterns, and recruitment-like behaviors. These are all fundamental characteristics of biological motor control and emerge without direct selection for these behaviors. This is the first time that all of these characteristic behaviors emerge in a model of this detail without direct selective pressure.

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New attractor states for synchronous activity in synfire chains with excitatory and inhibitory coupling

Cited by 16 publications

References 37 publications

Information coding and oscillatory activity in synfire neural networks with and without inhibitory coupling

Information coding and oscillatory activity in synfire neural networks with and without inhibitory coupling

Is chaos making a difference? Synchronization transitions in chaotic and nonchaotic neuronal networks

Stochastic optimization of a biologically plausible spino-neuromuscular system model

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