Memory, Vision, Attention, Decision-Making, and their disorders: 
linking cellular and sub-cellular properties to global behavior.

Cortical attractor networks provide a basis for understanding memory, attention, 
and decision-making. Analysis of systems found in the brain using mean-field 
approaches and integrate-and-fire neuronal network simulations shows how stochastic 
noise generated by the probabilistic firing times of neurons can influence the 
operation of these processes, often to advantage. These approaches allow sub-cellular 
effects such as changes in synaptic receptor-activated ion channel conductances 
and neuron-level events such as spiking on the global operation of the whole 
network and its functions in behaviour to be predicted and analyzed. This is 
leading to the use of dynamically realistic models incorporating stochastic noise 
to understand memory, attention, and decision-making, and some of their disorders. 
This new understanding in turn has implications for treatments for these disorders. 


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