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 is dynamic and! context-sensitive. This adaptive rand contextual specialisation is mediated by functional integration or interactions among brain systems with a special emphasis on backwards or top-down connections. The critical notion is that neuronal responses, in any given cortical area, can represent different things at different times, Our argument is developed under the perspective of generative models of functional brain architectures, where higher-level systems provide a prediction of the inputs to lower-level regions. 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. Specialisation is therefore not an intrinsic property of any region but depends on both forward and backward connections with other areas. Because these other areas have access to the context in which the inputs are generated they are in a position to modulate the selectivity or specialisation of lower 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 provide incomplete accounts of real brain architectures, Generative models represent a far more plausible framework for understanding selective neurophysiological responses and how representations are constructed in the brain. (C) 2001 Elsevier Science Inc.