General theoretical benefits:

  • captures neural representation of arbitrarily complex objects (i.e. scalars, vectors, functions, vector fields, etc.)
  • captures linear and nonlinear neural computations
  • captures arbitrary neural dynamics (linear, nonlinear, time-varying, adaptive, etc.)
  • unifies population and temporal coding
  • quantitatively specified

Neural modelling benefits:

  • analytically determines connection weights for models (this can be reversed; i.e. given weights, high-level transformations can be extracted)
  • incorporates any available neural data (e.g., tuning curves, response functions, trasmitter/receptor types, anatomical constraints, etc.)
  • appropriate regardless of single cell model (i.e. rate, spiking, full conductance based)
  • arbitrary network topologies
  • complexity of models is smoothly variable (i.e. single cells, population level, systems level)
  • learning is not need to construct informative models, but can be included as desired
  • means to test abstract hypotheses about neural function
  • relates directly to recorded neural data (e.g. spikes, tuning curves, pooled spikes, fMRI signals (i.e. filtered spiking activity), LFP, EEG, etc.)

Simulation benefits:

  • easily scales complexity to allow match to available hardware
  • no learning is necessary (though allowed), permitting rapid model development
  • GUI simulator freely available, with full scripting (Nengo)