Biological constraints on neural network models of cognitive function

Abstract

| 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...

Modelling concrete and abstract concepts using brain-constrained deep neural networks

“…We adopted a model architecture constrained by neurobiological information and previously applied to explore neural mechanisms of semantic learning (Tomasello et al, 2017(Tomasello et al, , 2018(Tomasello et al, , 2019. The following brain constraints were applied to the model (Pulvermüller et al, 2021):…”

Nonlinear reconfiguration of network edges, topology and information content during an artificial learning task

“…With all that said, neural networks are clearly nowhere near as complex as the typical nervous systems studied by neuroscientists [ 48 ]. Most artificial neural networks treat all nodes as identical, whereas diversity reigns supreme in the biological networks of the brain [ 49 ].…”

The Spatiotemporal Neural Dynamics of Intersensory Attention Capture of Salient Stimuli: A Large-Scale Auditory-Visual Modeling Study

“…Generally, the hypotheses underlying the model are instantiated in a computational framework and quantitative relationships are generated that can be explicitly compared with experimental data ( Horwitz et al, 1999 ). Because the network paradigm now has become central in cognitive neuroscience (and especially in human studies), neural network modeling has emerged as an essential tool for interpreting neuroimaging data, and as well, integrating neuroimaging data with the other kinds of data employed by cognitive neuroscientists ( Horwitz et al, 2000 ; Bassett et al, 2018 ; Kay, 2018 ; Naselaris et al, 2018 ; Pulvermuller et al, 2021 ).…”

“…We, among others, believe that computational modeling is a powerful tool for helping determine the neural mechanisms mediating cognitive functions ( Horwitz et al, 1999 , 2005 ; Deco et al, 2008 ; Friston, 2010 ; Jirsa et al, 2010 ; Eliasmith et al, 2012 ; Kriegeskorte and Diedrichsen, 2016 ; Bassett et al, 2018 ; Yang et al, 2019 ; Ito et al, 2020 ; Pulvermuller et al, 2021 ). With respect to working memory, Tagamets and Horwitz (1998) and Horwitz and Tagamets (1999) developed a large-scale dynamic neural model of visual object short-term memory.…”