Large‐scale Computational Models of Ongoing Brain Activity
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| Neural network models are potential tools for improving our understanding of complex brain functions. To address this goal, these models need to be neurobiologically realistic. 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. 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. 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. 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. In closing, we point to possible future clinical applications of brain-constrained modelling. PULVERMÜLLER ET AL., BIOLOGICAL CONSTRAINTS ON NEURAL NETWORK MODELS OF COGNITIVE FUNCTIONSAn important step towards addressing the neural substrate was taken by so-called localist models of cognition and language [8][9][10][11][12] , which filled the boxes of modular models with single artificial 'neurons' thought to locally represent cognitive elements 13 such as perceptual features and percepts, phonemes, word forms, meaning features, concepts and so on (Fig. 1a). The 1:1 relationship between the artificial neuron-like computational-algorithmic implementations and the entities postulated by cognitive theories made it easy to connect the two types of models. However, the notion that individual neurons each carry major cognitive functions is controversial today and difficult to reconcile with evidence from neuroscience research 14,15 . This is not to dispute the great specificity of some neurons' responses 16 , but rather to highlight the now dominant view that even these very specific cells "do not act in isolation but are part of cell assemblies representing familiar concepts", objects or other entities 17,18 . A further limitation of the localist models was that they did not systematically address the mechanisms underlying the formation of new representations and their connections.Auto-associative networks. Neuroanatomical observations suggest that the cortex is characterized by ample intrinsic and recurrent connectivity between its neurons and, therefore, it can be seen as an associative memory 19,20 . This position inspired a family of artificial neural networks, called 'auto-associative networks' or 'attractor networks' [21][22][23][24][25][26][27][28][29][30][31][32] .Auto-associative network models implement neurons with connections betwe...
| Neural network models are potential tools for improving our understanding of complex brain functions. To address this goal, these models need to be neurobiologically realistic. 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. 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. 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. 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. In closing, we point to possible future clinical applications of brain-constrained modelling. PULVERMÜLLER ET AL., BIOLOGICAL CONSTRAINTS ON NEURAL NETWORK MODELS OF COGNITIVE FUNCTIONSAn important step towards addressing the neural substrate was taken by so-called localist models of cognition and language [8][9][10][11][12] , which filled the boxes of modular models with single artificial 'neurons' thought to locally represent cognitive elements 13 such as perceptual features and percepts, phonemes, word forms, meaning features, concepts and so on (Fig. 1a). The 1:1 relationship between the artificial neuron-like computational-algorithmic implementations and the entities postulated by cognitive theories made it easy to connect the two types of models. However, the notion that individual neurons each carry major cognitive functions is controversial today and difficult to reconcile with evidence from neuroscience research 14,15 . This is not to dispute the great specificity of some neurons' responses 16 , but rather to highlight the now dominant view that even these very specific cells "do not act in isolation but are part of cell assemblies representing familiar concepts", objects or other entities 17,18 . A further limitation of the localist models was that they did not systematically address the mechanisms underlying the formation of new representations and their connections.Auto-associative networks. Neuroanatomical observations suggest that the cortex is characterized by ample intrinsic and recurrent connectivity between its neurons and, therefore, it can be seen as an associative memory 19,20 . This position inspired a family of artificial neural networks, called 'auto-associative networks' or 'attractor networks' [21][22][23][24][25][26][27][28][29][30][31][32] .Auto-associative network models implement neurons with connections betwe...
A challenging problem in cognitive neuroscience is to relate the structural connectivity (SC) to the functional connectivity (FC) to better understand how large-scale network dynamics underlying human cognition emerges from the relatively fixed SC architecture. Recent modeling attempts point to the possibility of a single diffusion kernel giving a good estimate of the FC. We highlight the shortcomings of the single-diffusion-kernel model (SDK) and propose a multi-scale diffusion scheme. Our multi-scale model is formulated as a reaction-diffusion system giving rise to spatio-temporal patterns on a fixed topology. We hypothesize the presence of inter-regional co-activations (latent parameters) that combine diffusion kernels at multiple scales to characterize how FC could arise from SC. We formulated a multiple kernel learning (MKL) scheme to estimate the latent parameters from training data. Our model is analytically tractable and complex enough to capture the details of the underlying biological phenomena. The parameters learned by the MKL model lead to highly accurate predictions of subject-specific FCs from test datasets at a rate of 71%, surpassing the performance of the existing linear and non-linear models. We provide an example of how these latent parameters could be used to characterize age-specific reorganization in the brain structure and function.
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