It had previously been shown that generic cortical microcircuit models can perform complex real-time computations on continuous input streams, provided that these computations can be carried out with a rapidly fading memory. We investigate ... the computational capability of such circuits in the more realistic case where not only readout neurons, but in addition a few neurons within the circuit have been trained for specific tasks. This is essentially equivalent to the case where the output of trained readout neurons is fed back into the circuit. We show that this new model overcomes the limitation of a rapidly fading memory. In fact, we prove that in the idealized case without noise it can carry out any conceiv- able digital or analog computation on time-varying inputs. But even with noise the resulting computational model can perform a large class of biologically relevant real-time computations that require a non-fading memory. ... In particular we show that ... generic cortical microcircuits with feedback provide a new model for working memory that is consistent with a large set of biological constraints. See paper for more and details.
Model Type: Realistic Network
Region(s) or Organism(s): Neocortex
Model Concept(s): Temporal Pattern Generation; Simplified Models
Simulation Environment: CSIM (web link to model)
References:
Maass W, Joshi P, Sontag ED. (2007). Computational aspects of feedback in neural circuits. PLoS computational biology. 3 [PubMed]
Maass W, Sontag ED, Joshi P. (2006). Principles of real-time computing with feedback applied to cortical microcircuit models. Advances in Neural Information Processing Systems. 18