scholarly journals Gamma oscillations decompose the visual scene into object-based perceptual cycles: a computational model

2010 ◽  
Vol 10 (7) ◽  
pp. 1270-1270
Author(s):  
M. Thomas ◽  
V. Rufin
Keyword(s):  
Computational Model ◽  
Gamma Oscillations ◽  
Visual Scene ◽  
Object Based

scholarly journals A proto-object-based computational model for visual saliency

2013 ◽  
Vol 13 (13) ◽  
pp. 27 ◽  
Author(s):  
Victoria Yanulevskaya ◽  
Jasper Uijlings ◽  
Jan-Mark Geusebroek ◽  
Nicu Sebe ◽  
Arnold Smeulders
Keyword(s):  
Computational Model ◽  
Visual Saliency ◽  
Object Based

scholarly journals Computational model of carbachol-induced delta, theta, and gamma oscillations in the hippocampus

Hippocampus ◽  
2001 ◽  
Vol 11 (3) ◽  
pp. 251-274 ◽  
Author(s):  
Paul H.E. Tiesinga ◽  
Jean-Marc Fellous ◽  
Jorge V. José ◽  
Terrence J. Sejnowski
Keyword(s):  
Computational Model ◽  
Gamma Oscillations

Effects of rodent prefrontal lesions on object-based, visual scene memory

2009 ◽  
Vol 92 (4) ◽  
pp. 552-558 ◽  
Author(s):  
William E. DeCoteau ◽  
Daniel McElvaine ◽  
Linnea Smolentzov ◽  
Raymond P. Kesner
Keyword(s):  
Visual Scene ◽  
Scene Memory ◽  
Object Based

scholarly journals Influence of NMDA and GABA synaptic dysfunction on the evoked gamma oscillations in a computational model of schizophrenia

Biologija ◽  
2017 ◽  
Vol 63 (2) ◽  
Author(s):  
Rokas Jackevičius ◽  
Bruce P. Graham ◽  
Aušra Saudargienė
Keyword(s):  
Neural Network ◽  
Computational Modeling ◽  
Computational Model ◽  
Time Constant ◽  
Gamma Oscillations ◽  
Synaptic Dysfunction ◽  
Spiking Neural Network ◽  
Gamma Frequency ◽  
Gated Channel ◽  
Fast Spiking

Background. Schizophrenia is a psychiatric disorder which is characterized by delusions and hallucinations, and affects thoughts, behaviour and emotions. Major neuronal degeneration is not observed in schizophrenic patients, but abnormalities in cortical circuits are present. These abnormalities are reflected in impaired EEG gamma frequency (30–80 Hz), being crucial for many processes including sensation, perception, working memory, and attention. NMDA and GABA synaptic dysfunction is proposed as one of the possible mechanisms underlying the gamma oscillatory deficits in schizophrenia. Materials and Methods. We used a computational modeling approach to investigate the joint influence of NMDA and GABA synaptic dysfunction on gamma oscillations in cortex. We employed a computational model of a spiking neural network composed of 800 pyramidal neurons, 150 regular-spiking interneurons, and 50 fast-spiking interneurons. All cells were randomly interconnected. Network neurons received independent Poisson noise input at 4 Hz and 40 Hz drive excitatory stimulation. Fast-spiking interneuron GABA receptor-gated channel time constant was increased and NMDA receptor-gated channel synaptic conductance was decreased to represent synaptic dysfunction in schizophrenia. Results. Reducing NMDA conductance enhanced gamma power, and increasing decay time constant of GABA receptorgated channel attenuated gamma generation in a network. The effect of synaptic GABA alteration was more profound. Conclusions. NMDA and GABA synaptic dysfunction leads to the impaired gamma frequency oscillations in a spiking neural network of cortex. Computational modeling approach is a powerful tool to understand complex non-linear dynamical systems and intrinsic mechanisms of neuronal network activity in healthy and diseased brain.

A Computational Model for Object-Based Visual Saliency: Spreading Attention Along Gestalt Cues

2016 ◽  
Vol 18 (2) ◽  
pp. 273-286 ◽  
Author(s):  
Jin-Gang Yu ◽  
Gui-Song Xia ◽  
Changxin Gao ◽  
Ashok Samal
Keyword(s):  
Computational Model ◽  
Visual Saliency ◽  
Object Based

scholarly journals A Neurally Inspired Model of Figure Ground Organization with Local and Global Cues

AI ◽  
2020 ◽  
Vol 1 (4) ◽  
pp. 436-464
Author(s):  
Sudarshan Ramenahalli
Keyword(s):  
Computational Model ◽  
Classification Accuracy ◽  
Model Performance ◽  
Visual Scene ◽  
Color Intensity ◽  
Feed Forward ◽  
Spatial Depth ◽  
Feed Forward Model ◽  
Ground Organization ◽  
Local Cues

Figure Ground Organization (FGO)-inferring spatial depth ordering of objects in a visual scene-involves determining which side of an occlusion boundary is figure (closer to the observer) and which is ground (further away from the observer). A combination of global cues, like convexity, and local cues, like T-junctions are involved in this process. A biologically motivated, feed forward computational model of FGO incorporating convexity, surroundedness, parallelism as global cues and spectral anisotropy (SA), T-junctions as local cues is presented. While SA is computed in a biologically plausible manner, the inclusion of T-Junctions is biologically motivated. The model consists of three independent feature channels, Color, Intensity and Orientation, but SA and T-Junctions are introduced only in the Orientation channel as these properties are specific to that feature of objects. The effect of adding each local cue independently and both of them simultaneously to the model with no local cues is studied. Model performance is evaluated based on figure-ground classification accuracy (FGCA) at every border location using the BSDS 300 figure-ground dataset. Each local cue, when added alone, gives statistically significant improvement in the FGCA of the model suggesting its usefulness as an independent FGO cue. The model with both local cues achieves higher FGCA than the models with individual cues, indicating SA and T-Junctions are not mutually contradictory. Compared to the model with no local cues, the feed-forward model with both local cues achieves ≥8.78% improvement in terms of FGCA.

scholarly journals Kcns3 Deficiency Disrupts Parvalbumin Neuron Physiology in Mouse Prefrontal Cortex: Implications for the pathophysiology of schizophrenia

2020 ◽  
Author(s):  
Takeaki Miyamae ◽  
Takanori Hashimoto ◽  
Monica Abraham ◽  
Rika Kawabata ◽  
Sho Koshikizawa ◽  
...  
Keyword(s):  
Computational Model ◽  
Cell Model ◽  
Gamma Oscillations ◽  
Spike Trains ◽  
Repetitive Firing ◽  
Mrna Levels ◽  
Gene Encoding ◽  
Gamma Frequency ◽  
Channel Inactivation ◽  
Deficient Mice

AbstractThe unique fast spiking (FS) phenotype of cortical parvalbumin-positive (PV) neurons depends on multiple subtypes of voltage-gated potassium channels (Kv). PV neurons selectively express Kcns3, the gene encoding Kv9.3 subunits, suggesting that Kcns3 expression is critical for the FS phenotype. KCNS3 expression is lower in PV neurons in schizophrenia, but the effects of this alteration are unclear, because Kv9.3 subunit function is poorly understood. We therefore assessed the role of Kv9.3 subunits in PV neuron function by combining gene expression analyses, computational modeling, and electrophysiology in acute slices from the cortex of Kcns3-deficient miceKcns3 mRNA levels were ~50% lower in cortical PV neurons from Kcns3-deficient relative to wildtype mice. While silent per se, Kv9.3 subunits are believed to amplify the Kv2.1 current in Kv2.1-Kv9.3 channel complexes. Hence, to assess the consequences of reducing Kv9.3 levels, we simulated the effects of decreasing the Kv2.1-mediated current in a computational model. The FS cell model with reduced Kv2.1 produced spike trains with irregular inter-spike intervals, or stuttering, and greater Na+ channel inactivation, possibly due to a smaller afterhyperpolarization. As in the computational model, PV basket cells (PVBCs) from Kcns3-deficient mice displayed spike trains with strong stuttering, which depressed PVBC firing, and smaller afterhyperpolarization. Moreover, Kcns3 deficiency impaired the recruitment of PVBCs by stimuli mimicking synaptic input during cortical UP states, which elicited bursts of spikes at gamma frequency. Our data suggest that Kv9.3 subunits are critical for PVBC physiology, and that KCNS3 deficiency in schizophrenia may impair the substrate of gamma oscillations.Significance statementIn the neocortex, Kcns3, the gene encoding voltage-dependent potassium (Kv) channel subunits Kv9.3, is selectively expressed by parvalbumin-positive (PV) neurons. Moreover, KCNS3 expression is decreased in PV neurons in schizophrenia. Kv 9.3 subunits are believed to amplify the current mediated by Kv2.1 subunits, however Kv9.3 function has not been investigated in PV cells.Here, simulations in a computational model and electrophysiological experiments with Kcns3-deficient mice revealed that Kcns3 deficiency disrupts repetitive firing in cortical PV neurons, possibly enhancing Na+ channel inactivation, and particularly with stimuli eliciting firing at gamma frequency band (30-80Hz). Our results suggest that Kv9.3 subunits are essential for PV neuron electrophysiology and that KCNS3 deficiency likely contributes to PV neuron dysfunction and gamma oscillation impairments in schizophrenia.

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