2012
DOI: 10.1142/s0129065712500037
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Binding and Segmentation via a Neural Mass Model Trained With Hebbian and Anti-Hebbian Mechanisms

Abstract: Synchronization of neural activity in the gamma band, modulated by a slower theta rhythm, is assumed to play a significant role in binding and segmentation of multiple objects. In the present work, a recent neural mass model of a single cortical column is used to analyze the synaptic mechanisms which can warrant synchronization and desynchronization of cortical columns, during an autoassociation memory task. The model considers two distinct layers communicating via feedforward connections. The first la… Show more

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Cited by 8 publications
(5 citation statements)
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“…These are pyramidal neurons, excitatory interneurons, GABA-ergic inhibitory interneurons with slow synaptic dynamics, and GABA-ergic inhibitory interneurons with fast synaptic dynamics. A more detailed description of the column is provided in previous papers of the authors (Ursino et al 2010 ; Cona et al 2011 , 2012 ). It is worth noting that we adopted the same parameters for each cortical column in each layer, i.e., differences in rhythms between one layer and another originate from the presence of feedback synapses among the columns, produced by the training procedure.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…These are pyramidal neurons, excitatory interneurons, GABA-ergic inhibitory interneurons with slow synaptic dynamics, and GABA-ergic inhibitory interneurons with fast synaptic dynamics. A more detailed description of the column is provided in previous papers of the authors (Ursino et al 2010 ; Cona et al 2011 , 2012 ). It is worth noting that we adopted the same parameters for each cortical column in each layer, i.e., differences in rhythms between one layer and another originate from the presence of feedback synapses among the columns, produced by the training procedure.…”
Section: Methodsmentioning
confidence: 99%
“…Recently, we developed a model for the study of theta-gamma coupling, exploiting the dynamics of neural masses (Cona et al 2012 ; Cona and Ursino 2013 ). The model was able to store and reproduce sequence of events using Hebbian and anti-Hebbian learning paradigms.…”
Section: Introductionmentioning
confidence: 99%
“…The active segments are filtered into the respective bands with pass-band spectral range of mu (8)(9)(10)(11)(12) and beta (14-28 Hz) rhythms using a Butterworth band-pass filter. The powers of specific spectral bands are calculated using the variance of filtered data.…”
Section: Spectral Power and Asymmetry Ratiomentioning
confidence: 99%
“…These can simulate ultra-fast AMPA synapses. This mechanism induces a very fast inhibition between columns in different patterns of L 2 , so that different features in different patterns tend to desynchronise (Cona et al, 2012). Connections between 6 regions, 3 in L 1 and 3 in L 2 .…”
Section: Recall Of Non-sequential Patternsmentioning
confidence: 99%
“…However, these models used very simple oscillating neural units, such as Wilson Cowan or relaxations oscillators (Wang et al, 1990); (von der Malsburg and Buhmann, 1992); (Wang and Terman, 1997); (Ursino et al, 2003); (Ursino et al, 2009), which are unable to simulate realistic brain rhythms. A recent work addresses the same issue using a more sophisticate model, able to mimic the electrical activity in cortical regions (Cona et al, 2012). Using a mathematical model for the simulation of brain activity, known as neural mass model (NMM), the authors proposed a neural architecture that can learn different NAs and evoke them separated in time.…”
Section: Introductionmentioning
confidence: 99%