Dynamic representations and generative models of brain function

The main point made in this article is; that the representational capacity and inherent function of any neuron, neuronal population or cortical area i s dynamic and! context-sensitive. This adaptive rand contextual specialisat ion is mediated by functional integration or interactions among brain syste ms with a special emphasis on backwards or top-down connections. The critic al notion is that neuronal responses, in any given cortical area, can repre sent different things at different times, Our argument is developed under t he perspective of generative models of functional brain architectures, wher e higher-level systems provide a prediction of the inputs to lower-level re gions. Conflict between the two is resolved by changes in the higher-level representations, driven by the resulting error in lower regions, until the mismatch is 'cancelled', In this model the specialisation of any region is determined both by bottom-up driving inputs and by top-down predictions. Sp ecialisation is therefore not an intrinsic property of any region but depen ds on both forward and backward connections with other areas. Because these other areas have access to the context in which the inputs are generated t hey are in a position to modulate the selectivity or specialisation of lowe r areas. The implications for 'classical' models (e,g., classical receptive fields in electrophysiology, classical specialisation in neuroimaging and connectionism in cognitive models) are severe and suggest these models prov ide incomplete accounts of real brain architectures, Generative models repr esent a far more plausible framework for understanding selective neurophysi ological responses and how representations are constructed in the brain. (C ) 2001 Elsevier Science Inc.