Neurodynamics of biased competition and cooperation for attention: a model with spiking neurons.
Recent neurophysiological experiments have led to a promising "biased competition hypothesis" of the neural basis of attention. According to this hypothesis, attention appears as a sometimes nonlinear property that results from a top-down biasing effect that influences the competitive and cooperative interactions that work both within cortical areas and between cortical areas. In this paper we describe a detailed dynamical analysis of the synaptic and neuronal spiking mechanisms underlying biased competition. We perform a detailed analysis of the dynamical capabilities of the system by exploring the stationary attractors in the parameter space by a mean-field reduction consistent with the underlying synaptic and spiking dynamics. The nonstationary dynamical behavior, as measured in neuronal recording experiments, is studied by an integrate-and-fire model with realistic dynamics. This elucidates the role of cooperation and competition in the dynamics of biased competition and shows why feedback connections between cortical areas need optimally to be weaker by a factor of about 2.5 than the feedforward connections in an attentional network. We modeled the interaction between top-down attention and bottom-up stimulus contrast effects found neurophysiologically and showed that top-down attentional effects can be explained by external attention inputs biasing neurons to move to different parts of their nonlinear activation functions. Further, it is shown that, although NMDA nonlinear effects may be useful in attention, they are not necessary, with nonlinear effects (which may appear multiplicative) being produced in the way just described.
Deco
Gustavo
G
Department of Technology, Computational Neuroscience, Institució Catalana de Recerca i Estudis Avançats, Universitat Pompeu Fabra, Barcelona, Spain.
Rolls
Edmund T
ET
D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D001288:Attention / Q000502:physiology*; D015982:Bias; D005246:Feedback; D006801:Humans; D008959:Models, Neurological; D009433:Neural Inhibition / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000145:classification / Q000502:physiology*; D017711:Nonlinear Dynamics; D009435:Synaptic Transmission / Q000502:physiology