There are two very distinctive approaches to developing mathematical models for brain function and behavior. One approach, exemplified by Traub and colleagues [1] and by the Blue Brain project [2] is to start with a detailed, biophysically accurate description of a single neuron, and then to scale up by assembling hundreds or thousands of interacting neurons to create neural populations, examining the assembly for interesting collective behaviors that emerge spontaneously from these interactions.

There are two very distinctive approaches to developing mathematical models for brain function and behavior. One approach, exemplified by Traub and colleagues [1] and by the Blue Brain project [2] is to start with a detailed, biophysically accurate description of a single neuron, and then to scale up by assembling hundreds or thousands of interacting neurons to create neural populations, examining the assembly for interesting collective behaviors that emerge spontaneously from these interactions.

There are two very distinctive approaches to developing mathematical models for brain function and behavior. One approach, exemplified by Traub and colleagues [1] and by the Blue Brain project [2] is to start with a detailed, biophysically accurate description of a single neuron, and then to scale up by assembling hundreds or thousands of interacting neurons to create neural populations, examining the assembly for interesting collective behaviors that emerge spontaneously from these interactions.