Effects of an extra link and routing on spatio-temporal packet traffic dynamics of network model

2004 ◽  
Author(s):  
K.P. Maxie ◽  
A.T. Lawniczak ◽  
A. Gerisch
Keyword(s):  
Network Model ◽  
Traffic Dynamics ◽  
Spatio Temporal

scholarly journals Unsupervised Learning of Temporal Features for Word Categorization in a Spiking Neural Network Model of the Auditory Brain

2016 ◽  
Author(s):  
Irina Higgins ◽  
Simon Stringer ◽  
Jan Schnupp
Keyword(s):  
Neural Network ◽  
Auditory Cortex ◽  
Network Model ◽  
Neural Network Model ◽  
Spiking Neural Network ◽  
Cortical Areas ◽  
Speaker Independent ◽  
Temporal Encoding ◽  
Spatio Temporal ◽  
Spiking Neural Network Model

AbstractThe nature of the code used in the auditory cortex to represent complex auditory stimuli, such as naturally spoken words, remains a matter of debate. Here we argue that such representations are encoded by stable spatio-temporal patterns of firing within cell assemblies known as polychronous groups, or PGs. We develop a physiologically grounded, unsupervised spiking neural network model of the auditory brain with local, biologically realistic, spike-time dependent plasticity (STDP) learning, and show that the plastic cortical layers of the network develop PGs which convey substantially more information about the speaker independent identity of two naturally spoken word stimuli than does rate encoding that ignores the precise spike timings. We furthermore demonstrate that such informative PGs can only develop if the input spatio-temporal spike patterns to the plastic cortical areas of the model are relatively stable.Author SummaryCurrently we still do not know how the auditory cortex encodes the identity of complex auditory objects, such as words, given the great variability in the raw auditory waves that correspond to the different pronunciations of the same word by different speakers. Here we argue for temporal information encoding within neural cell assemblies for representing auditory objects. Unlike the more traditionally accepted rate encoding, temporal encoding takes into account the precise relative timing of spikes across a population of neurons. We provide support for our hypothesis by building a neurophysiologically grounded spiking neural network model of the auditory brain with a biologically plausible learning mechanism. We show that the model learns to differentiate between naturally spoken digits “one” and “two” pronounced by numerous speakers in a speaker-independent manner through simple unsupervised exposure to the words. Our simulations demonstrate that temporal encoding contains significantly more information about the two words than rate encoding. We also show that such learning depends on the presence of stable patterns of firing in the input to the cortical areas of the model that are performing the learning.

scholarly journals Network dynamics underlying OFF responses in the auditory cortex

2019 ◽  
Author(s):  
Giulio Bondanelli ◽  
Thomas Deneux ◽  
Brice Bathellier ◽  
Srdjan Ostojic
Keyword(s):  
Auditory Cortex ◽  
Single Cell ◽  
Network Model ◽  
Network Dynamics ◽  
Population Responses ◽  
Two Photon ◽  
Cortical Areas ◽  
Population Analyses ◽  
Spatio Temporal ◽  
Low Dimensional

AbstractAcross sensory systems, complex spatio-temporal patterns of neural activity arise following the onset (ON) and offset (OFF) of stimuli. While ON responses have been widely studied, the mechanisms generating OFF responses in cortical areas have so far not been fully elucidated. We examine here the hypothesis that OFF responses are single-cell signatures of network dynamics and propose a network model that generates transient OFF responses through recurrent interactions. To test this model, we performed population analyses of two-photon calcium recordings in the auditory cortex of awake mice listening to auditory stimuli. We found that the network model accounts for the low-dimensional organisation of population responses and their global structure across stimuli, where distinct stimuli activate mostly orthogonal dimensions in the neural state-space. In contrast, a single-cell mechanism explains some prominent features of the data, but does not account for the structure across stimuli and trials captured by the network model.

Traffic dynamics on two-layer networks with community structure

2021 ◽  
pp. 2150272
Author(s):  
Jinlong Ma ◽  
Yi Zhou ◽  
Weiheng Wang ◽  
Yongqiang Zhang ◽  
Ruimei Zhao ◽  
...  
Keyword(s):  
Community Structure ◽  
Network Model ◽  
Real World ◽  
Traffic Congestion ◽  
Network Models ◽  
Community Networks ◽  
Traffic Dynamics ◽  
Community Strength ◽  
Traffic Capacity ◽  
Simulation Results

In terms of reducing traffic congestion, it should be understood that traffic dynamics depend on network structure. Most of complex networks in the real world can be represented by multi-layer and community structures, that is, the connections within the community are relatively close, and the connections between the community are relatively sparse. There are generally strong and weak community networks in community networks. In this work, the strong and weak community networks are used to construct two-layer network models of different scales, and then the influence of community structure on traffic capacity is analyzed. The simulation results show that when the two-layer network model is composed of two strong community networks, the traffic capacity is the largest, followed by the two-layer network model composed of two weak community networks, the traffic capacity is also relatively large. When the two subnetworks are of different community strength, the traffic capacity is relatively small.

A Neural Network Model for a Hierarchical Spatio-temporal Memory

2009 ◽  
pp. 428-435
Author(s):  
Kiruthika Ramanathan ◽  
Luping Shi ◽  
Jianming Li ◽  
Kian Guan Lim ◽  
Ming Hui Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Temporal Memory ◽  
Spatio Temporal
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