ISBN : 978-2-9532965-0-1In this paper, the behavior of dynamic neural fields is studied through the lens of performance. As an alternative to the currently available analytical instruments, an empirical evaluation methodology is proposed in order to examine the dynamic quality of a field. This consists of simulating the field through various key scenarios and compare the observed behavior to an optimal expected one. Some desired effects concerning the evolution of an ideal field are inspected, and a performance criterion is defined accordingly. Practically, this approach implements a generic benchmark framework for qualifying neural fields, allowing to inspect the evolution of the model in different key situations. The presented methodology...
ISBN : 978-2-9532965-0-1Revisiting CNFT calculation maps in both the discrete and continuous tempora...
International audienceThis paper focuses on the possibility of enabling vector quantization learning...
The goal of dynamic causal modelling (DCM) of neuroimaging data is to study experimentally induced c...
ISBN : 978-2-9532965-0-1In this paper, the behavior of dynamic neural fields is studied through the ...
In this paper, the behavior of dynamic neural fields is stud-ied through the lens of performance. As...
International audienceThe Continuous Neural Field Theory introduces biologically-inspired competitio...
Dynamic neural fields (DNFs) are dynamical systems models that approximate the activity of large, ho...
International audienceIn this paper, a method is introduced in order to qualify the performance of d...
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...
This paper presents a framework for creating neural field models from electrophysiological data. The...
DoctoralWe review methods from statistical physics and dynamical systems theory for the analysis of ...
Review article about Dynamic Field theory and applications in cognitive science and roboticseuCognit...
Conventional neural field models describe well some experimental data, such as Local Field Potential...
ISBN : 978-2-9532965-0-1Revisiting CNFT calculation maps in both the discrete and continuous tempora...
International audienceThis paper focuses on the possibility of enabling vector quantization learning...
The goal of dynamic causal modelling (DCM) of neuroimaging data is to study experimentally induced c...
ISBN : 978-2-9532965-0-1In this paper, the behavior of dynamic neural fields is studied through the ...
In this paper, the behavior of dynamic neural fields is stud-ied through the lens of performance. As...
International audienceThe Continuous Neural Field Theory introduces biologically-inspired competitio...
Dynamic neural fields (DNFs) are dynamical systems models that approximate the activity of large, ho...
International audienceIn this paper, a method is introduced in order to qualify the performance of d...
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...
This paper presents a framework for creating neural field models from electrophysiological data. The...
DoctoralWe review methods from statistical physics and dynamical systems theory for the analysis of ...
Review article about Dynamic Field theory and applications in cognitive science and roboticseuCognit...
Conventional neural field models describe well some experimental data, such as Local Field Potential...
ISBN : 978-2-9532965-0-1Revisiting CNFT calculation maps in both the discrete and continuous tempora...
International audienceThis paper focuses on the possibility of enabling vector quantization learning...
The goal of dynamic causal modelling (DCM) of neuroimaging data is to study experimentally induced c...