Explain the Future challenges for Neuroscience?

When the analysis of single cells and even networks has progressed very nicely, there still remain many significant questions. At least two of these challenges are related to the transient nature of the dynamics in neurons and brains. Most of the mathematical analysis that has been done to date assumes stationarity both in the inputs to the neurons as in their intrinsic properties. There will be a need to develop few mathematical techniques for dealing with the transient nature of inputs to the nervous system that goes further than simple periodic forcing. More essentially, most modelers assume that the parameters such as the intrinsic membrane conductances and the synaptic conductance are fixed. Though, it has now been established that there is plasticity at many time scales both in the intrinsic dynamics (Turrigiano and Nelson) and in the connections (Bi and Poo, 1998).

So a major challenge in modelling is how this plasticity influences the behaviour of single neurons and networks of neurons in the existence of stimuli. At some level, it is possible to take benefit of the multiple time scales and generate a hierarchy of models in which the averaged results of one step are used in the next. Another significant challenge is in the feedback between perception and action. That is, most theories and modelers treat the sensory system and the motor systems as separate. Though, it is now clear (see for example, Kleinfeld et al, 2002) that the motor output affects the sensory input forming a massive sensorimotor loop. The computational benefits and consequences of these loops stay to be explored.