A neural mass model of place cell activity: theta phase precession, replay and imagination of never experienced paths

2014 ◽  
Vol 38 (1) ◽  
pp. 105-127 ◽  
Author(s):  
Filippo Cona ◽  
Mauro Ursino
Keyword(s):  
Cell Activity ◽  
Place Cell ◽  
Neural Mass Model ◽  
Mass Model ◽  
Neural Mass ◽  
Phase Precession ◽  
Theta Phase ◽  
Theta Phase Precession

A MULTI-LAYER NEURAL-MASS MODEL FOR LEARNING SEQUENCES USING THETA/GAMMA OSCILLATIONS

2013 ◽  
Vol 23 (03) ◽  
pp. 1250036 ◽  
Author(s):  
FILIPPO CONA ◽  
MAURO URSINO
Keyword(s):  
Associative Memory ◽  
Gamma Oscillations ◽  
Neural Mass Model ◽  
Mass Model ◽  
The Third ◽  
Gamma Range ◽  
Gamma Rhythm ◽  
Neural Mass ◽  
Phase Precession ◽  
Theta Range

A neural mass model for the memorization of sequences is presented. It exploits three layers of cortical columns that generate a theta/gamma rhythm. The first layer implements an auto-associative memory working in the theta range; the second segments objects in the gamma range; finally, the feedback interactions between the third and the second layers realize a hetero-associative memory for learning a sequence. After training with Hebbian and anti-Hebbian rules, the network recovers sequences and accounts for the phase-precession phenomenon.

scholarly journals Spatial cell firing during virtual navigation of open arenas by head-restrained mice

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Guifen Chen ◽  
John Andrew King ◽  
Yi Lu ◽  
Francesca Cacucci ◽  
Neil Burgess
Keyword(s):  
Place Cell ◽  
Head Movements ◽  
Running Speed ◽  
Firing Patterns ◽  
Firing Rates ◽  
Theta Frequency ◽  
Phase Precession ◽  
Cell Firing ◽  
Theta Phase ◽  
Theta Phase Precession

We present a mouse virtual reality (VR) system which restrains head-movements to horizontal rotations, compatible with multi-photon imaging. This system allows expression of the spatial navigation and neuronal firing patterns characteristic of real open arenas (R). Comparing VR to R: place and grid, but not head-direction, cell firing had broader spatial tuning; place, but not grid, cell firing was more directional; theta frequency increased less with running speed, whereas increases in firing rates with running speed and place and grid cells' theta phase precession were similar. These results suggest that the omni-directional place cell firing in R may require local-cues unavailable in VR, and that the scale of grid and place cell firing patterns, and theta frequency, reflect translational motion inferred from both virtual (visual and proprioceptive) and real (vestibular translation and extra-maze) cues. By contrast, firing rates and theta phase precession appear to reflect visual and proprioceptive cues alone.

scholarly journals Mean-Field Models for EEG/MEG: From Oscillations to Waves

2021 ◽  
Author(s):  
Áine Byrne ◽  
James Ross ◽  
Rachel Nicks ◽  
Stephen Coombes
Keyword(s):  
Gap Junction ◽  
Large Scale ◽  
Mean Field ◽  
Coarse Grained ◽  
Neural Mass Model ◽  
Neuron Network ◽  
Mass Model ◽  
Mean Field Model ◽  
Neural Mass ◽  
The Rich

AbstractNeural mass models have been used since the 1970s to model the coarse-grained activity of large populations of neurons. They have proven especially fruitful for understanding brain rhythms. However, although motivated by neurobiological considerations they are phenomenological in nature, and cannot hope to recreate some of the rich repertoire of responses seen in real neuronal tissue. Here we consider a simple spiking neuron network model that has recently been shown to admit an exact mean-field description for both synaptic and gap-junction interactions. The mean-field model takes a similar form to a standard neural mass model, with an additional dynamical equation to describe the evolution of within-population synchrony. As well as reviewing the origins of this next generation mass model we discuss its extension to describe an idealised spatially extended planar cortex. To emphasise the usefulness of this model for EEG/MEG modelling we show how it can be used to uncover the role of local gap-junction coupling in shaping large scale synaptic waves.

Principal dynamic mode analysis of neural mass model for the identification of epileptic states

2016 ◽  
Vol 26 (11) ◽  
pp. 113118 ◽  
Author(s):  
Yuzhen Cao ◽  
Liu Jin ◽  
Fei Su ◽  
Jiang Wang ◽  
Bin Deng
Keyword(s):  
Mode Analysis ◽  
Neural Mass Model ◽  
Dynamic Mode ◽  
Mass Model ◽  
Neural Mass
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