Analyzing the competition of gamma rhythms with delayed pulse-coupled oscillators in phase representation

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25th Annual Computational Neuroscience Meeting: CNS-2016

Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Neuronal Network Mechanisms of Gamma Oscillations

Contains fulltext : 175276.pdf (publisher's version ) (Open Access)Neuronal oscillations at various frequency bands play an important role in neuronal information processing. In this thesis, we mathematically and computationally investigated the properties of the gamma band (30-80 Hz) with different networks: a simplified network with two neurons, a large network with thousands of neurons, and interactions between distant brain areas. Our mathematical description of the neurons ranges from a very simple one (one state variable) to a very complex one (more than ten state variables). The gamma-band rhythmogenesis at a microscopic scale involves two major classes of neurons; pyramidal cells and interneurons. Experimental studies suggest that two major mechanisms, namely ING and PING, underlie their generation. For ING, the gamma rhythm is generated by coupled interneurons, while for PING, it stems from interactions between interneurons and pyramidal cells. Chapter 2 illustrates how ING and PING interact using computer simulations of the large network. Chapter 3 addresses the same question analytically for the simplified network. Both approaches demonstrate that ING and PING compete: The mechanism generating the higher oscillation frequency "wins". The winning mechanism determines the frequency of the network oscillation and suppresses the other mechanism. Experimental studies report that neuronal activity between distant brain areas synchronizes with zero phase lag. This is a remarkable result because of relatively significant time delays between the interacting brain areas. Chapter 4 analytically shows under what conditions zero-lag synchrony between distant brain areas is possible supported by computer simulations. In Chapter 5, we extended the model of the oscillators by including additional features of the neurons. From the results in both chapters, we conclude that zero-lag synchrony is possible, but not likely for the actual conditions in the brain. This suggests a cautious re-evaluation of the existence and proposed role of zero-lag synchrony in neuronal communication.Radboud University, 24 augustus 2017Promotor : Gielen, C.C.A.M. Co-promotor : Memmesheimer, R.M.241 p

When Long-Range Zero-Lag Synchronization is Feasible in Cortical Networks

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Cooperation and competition of gamma oscillation mechanisms

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