scholarly journals Orientation Selectivity in Inhibition-Dominated Networks of Spiking Neurons: Effect of Single Neuron Properties and Network Dynamics

2015 ◽  
Vol 11 (1) ◽  
pp. e1004045 ◽  
Author(s):  
Sadra Sadeh ◽  
Stefan Rotter
Author(s):  
Jiaoyan Wang ◽  
Xiaoshan Zhao ◽  
Chao Lei

AbstractInputs can change timings of spikes in neurons. But it is still not clear how input’s parameters for example injecting time of inputs affect timings of neurons. HR neurons receiving both weak and strong inputs are considered. How pulse inputs affecting neurons is studied by using the phase-resetting curve technique. For a single neuron, weak pulse inputs may advance or delay the next spike, while strong pulse inputs may induce subthreshold oscillations depending on parameters such as injecting timings of inputs. The behavior of synchronization in a network with or without coupling delays can be predicted by analysis in a single neuron. Our results can be used to predict the effects of inputs on other spiking neurons.


2016 ◽  
Vol 115 (1) ◽  
pp. 457-469 ◽  
Author(s):  
Mahmood S. Hoseini ◽  
Ralf Wessel

Local field potential (LFP) recordings from spatially distant cortical circuits reveal episodes of coherent gamma oscillations that are intermittent, and of variable peak frequency and duration. Concurrently, single neuron spiking remains largely irregular and of low rate. The underlying potential mechanisms of this emergent network activity have long been debated. Here we reproduce such intermittent ensemble oscillations in a model network, consisting of excitatory and inhibitory model neurons with the characteristics of regular-spiking (RS) pyramidal neurons, and fast-spiking (FS) and low-threshold spiking (LTS) interneurons. We find that fluctuations in the external inputs trigger reciprocally connected and irregularly spiking RS and FS neurons in episodes of ensemble oscillations, which are terminated by the recruitment of the LTS population with concurrent accumulation of inhibitory conductance in both RS and FS neurons. The model qualitatively reproduces experimentally observed phase drift, oscillation episode duration distributions, variation in the peak frequency, and the concurrent irregular single-neuron spiking at low rate. Furthermore, consistent with previous experimental studies using optogenetic manipulation, periodic activation of FS, but not RS, model neurons causes enhancement of gamma oscillations. In addition, increasing the coupling between two model networks from low to high reveals a transition from independent intermittent oscillations to coherent intermittent oscillations. In conclusion, the model network suggests biologically plausible mechanisms for the generation of episodes of coherent intermittent ensemble oscillations with irregular spiking neurons in cortical circuits.


2002 ◽  
Vol 14 (2) ◽  
pp. 369-404 ◽  
Author(s):  
Carsten Meyer ◽  
Carl van Vreeswijk

The determination of temporal and spatial correlations in neuronal activity is one of the most important neurophysiological tools to gain insight into the mechanisms of information processing in the brain. Its interpretation is complicated by the difficulty of disambiguating the effects of architecture, single-neuron properties, and network dynamics. We present a theory that describes the contribution of the network dynamics in a network of “spiking” neurons. For a simple neuron model including refractory properties, we calculate the temporal cross-correlations in a completely homogeneous, excitatory, fully connected network in a stable, stationary state, for stochastic dynamics in both discrete and continuous time. We show that even for this simple network architecture, the cross-correlations exhibit a large variety of qualitatively different properties, strongly dependent on the level of noise, the decay constant of the refractory function, and the network activity. At the critical point, the cross-correlations oscillate with a frequency that depends on the refractory properties or decay exponentially with a diverging damping constant (for “weak” refractory properties). We also investigate the effect of the synaptic time constants. It is shown that these time constants may, apart from their influence on the asymmetric peak arising from the direct synaptic connection, also affect the long-term properties of the cross-correlations.


2013 ◽  
Vol 14 (S1) ◽  
Author(s):  
Ajith Padmanabhan ◽  
Ioannis Vlachos ◽  
Ad Aertsen ◽  
Arvind Kumar

2000 ◽  
Vol 12 (7) ◽  
pp. 1519-1529 ◽  
Author(s):  
Christian W. Eurich ◽  
Stefan D. Wilke

Neural responses in sensory systems are typically triggered by a multitude of stimulus features. Using information theory, we study the encoding accuracy of a population of stochastically spiking neurons characterized by different tuning widths for the different features. The optimal encoding strategy for representing one feature most accurately consists of narrow tuning in the dimension to be encoded, to increase the single-neuron Fisher information, and broad tuning in all other dimensions, to increase the number of active neurons. Extremely narrow tuning without sufficient receptive field overlap will severely worsen the coding. This implies the existence of an optimal tuning width for the feature to be encoded. Empirically, only a subset of all stimulus features will normally be accessible. In this case, relative encoding errors can be calculated that yield a criterion for the function of a neural population based on the measured tuning curves.


SpringerPlus ◽  
2014 ◽  
Vol 3 (1) ◽  
pp. 148 ◽  
Author(s):  
Sadra Sadeh ◽  
Stefano Cardanobile ◽  
Stefan Rotter

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