ISBN : 978-2-9532965-0-1Revisiting CNFT calculation maps in both the discrete and continuous temporal cases, we propose a set of results allowing to choose the right set of parameters in order to both (i) guaranty the stability of the calculation and (ii) tune the shape of the output's map. With such parameters it appears that large sampling steps can be used, speeding up overall calculation. Furthermore, we report experimenting the fact that rectification is the only required non-linearity and formalize the use of this simplified but efficient mechanism. The outcome is shared as an open-source plug-in module to be used in existing simulation software
We present a novel dynamic neural field model consisting of two coupled fields of Amari-type which s...
Neural field models first appeared in the 50's, but the theory really took off in the 70's with the ...
Dynamic neural fields (DNFs) are dynamical systems models that approximate the activity of large, ho...
International audienceDynamic neural fields have been proposed as a continuous model of a neural tis...
International audiencePredictive capabilities are added to the competition mechanism known as the Co...
International audienceThe Continuous Neural Field Theory introduces biologically-inspired competitio...
International audienceNeural Field models (NFM) play an important role in the understanding of neura...
Many neural-field models in neuroscience mimic the all or nothing behavior of a neuron firing an act...
Conventional neural field models describe well some experimental data, such as Local Field Potential...
The mathematical modelling of neural activity is a hugely complex and prominent area of exploration ...
International audienceThis work presents a simulator that facilitates dynamic neural field (DNF) cal...
International audienceA simulator calculating two-dimensional dynamic neural fields with multiple or...
Many neural-field models in neuroscience mimic the all or nothing behavior of a neuron firing an act...
ISBN : 978-2-9532965-0-1In this paper, the behavior of dynamic neural fields is studied through the ...
International audienceThis paper introduces a sparse implementation of the Continuum Neural Field Th...
We present a novel dynamic neural field model consisting of two coupled fields of Amari-type which s...
Neural field models first appeared in the 50's, but the theory really took off in the 70's with the ...
Dynamic neural fields (DNFs) are dynamical systems models that approximate the activity of large, ho...
International audienceDynamic neural fields have been proposed as a continuous model of a neural tis...
International audiencePredictive capabilities are added to the competition mechanism known as the Co...
International audienceThe Continuous Neural Field Theory introduces biologically-inspired competitio...
International audienceNeural Field models (NFM) play an important role in the understanding of neura...
Many neural-field models in neuroscience mimic the all or nothing behavior of a neuron firing an act...
Conventional neural field models describe well some experimental data, such as Local Field Potential...
The mathematical modelling of neural activity is a hugely complex and prominent area of exploration ...
International audienceThis work presents a simulator that facilitates dynamic neural field (DNF) cal...
International audienceA simulator calculating two-dimensional dynamic neural fields with multiple or...
Many neural-field models in neuroscience mimic the all or nothing behavior of a neuron firing an act...
ISBN : 978-2-9532965-0-1In this paper, the behavior of dynamic neural fields is studied through the ...
International audienceThis paper introduces a sparse implementation of the Continuum Neural Field Th...
We present a novel dynamic neural field model consisting of two coupled fields of Amari-type which s...
Neural field models first appeared in the 50's, but the theory really took off in the 70's with the ...
Dynamic neural fields (DNFs) are dynamical systems models that approximate the activity of large, ho...