![]() ![]() ![]() We propose that such explanation can only be achieved if neural networks for modelling cognition incorporate a broad range of features that make them similar at different levels to real neurobiological networks. Despite substantial progress in understanding brain function in general, explaining how structural and functional features of neural tissue bring about cognition, language and thought has remained a challenge. If so, these neurobiological features must play a decisive role in explanations of cognitive capacities. The superb and specific cognitive capacities of humans and higher mammals may depend on specific structural and functional features of their brains. In closing, we point to possible future clinical applications of brain-constrained modelling.Ĭognition calls for mechanistic explanation. ![]() We highlight recent advances in developing biologically grounded cognitive theories and in mechanistically explaining, based on these brain-constrained neural models, hitherto unaddressed issues regarding the nature, localization and ontogenetic and phylogenetic development of higher brain functions. These aspects range from the choice of model neurons and of mechanisms of synaptic plasticity and learning, to implementation of inhibition and control, along with neuroanatomical properties including area structure and local and long-range connectivity. Here, we discuss different types of neural models, including localist, auto-associative and hetero-associative, deep and whole-brain networks, and identify aspects under which their biological plausibility can be improved. However, although neural networks have advanced dramatically in recent years and even achieve human-like performance on complex perceptual and cognitive tasks, their similarity to aspects of brain anatomy and physiology is imperfect. To address this goal, these models need to be neurobiologically realistic. Neural network models are potential tools for improving our understanding of complex brain functions. ![]()
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