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, 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 areal structure and local and long-range connectivity. We highlight recent advances in developing biologically grounded cognitive theories and in mechanistically explaining, on the basis of 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.
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Liberman, A. M., Cooper, F. S., Shankweiler, D. P. & Studdert-Kennedy, M. Perception of the speech code. Psychol. Rev. 74, 431–461 (1967).
Fodor, J. A. The Modularity of Mind (MIT Press, 1983).
Shallice, T. From Neuropsychology to Mental Structure (Cambridge Univ. Press, 1988).
Ellis, A. W. & Young, A. W. Human Cognitive Neuropsychology (Lawrence Erlbaum Associates, 1988).
Hebb, D. O. The Organization of Behavior. A Neuropsychological Theory (Wiley, 1949).
Braitenberg, V. in Theoretical Approaches to Complex Systems Vol. 21 (eds Heim, R. & Palm, G.) 171–188 (Springer, 1978).
O’Reilly, R. C. Six principles for biologically based computational models of cortical cognition. Trends Cogn. Sci. 2, 455–562 (1998).
Dell, G. S. A spreading-activation theory of retrieval in sentence production. Psychol. Rev. 93, 283–321 (1986).
MacKay, D. G. The Organization of Perception and Action. A Theory of Language and Other Cognitive Skills (Springer, 1987).
Grainger, J. & Jacobs, A. M. Orthographic processing in visual word recognition: a multiple read-out model. Psychol. Rev. 103, 518–565 (1996).
Dell, G. S., Schwartz, M. F., Martin, N., Saffran, E. M. & Gagnon, D. A. Lexical access in aphasic and nonaphasic speakers. Psychol.Rev. 104, 801–838 (1997).
Dijkstra, T. et al. Multilink: a computational model for bilingual word recognition and word translation. Bilingualism Lang. Cognition 22, 657–679 (2019).
Barlow, H. Single units and cognition: a neurone doctrine for perceptual psychology. Perception 1, 371–394 (1972).
Abeles, M. Corticonics — Neural Circuits of the Cerebral Cortex (Cambridge Univ. Press, 1991).
Quiroga, R. Q., Kreiman, G., Koch, C. & Fried, I. Sparse but not ‘grandmother-cell’ coding in the medial temporal lobe. Trends Cognit. Sci. 12, 87–91 (2008).
Perrett, D. J., Mistlin, A. J. & Chitty, A. J. Visual neurons responsive to faces. Trends Neurosci. 10, 358–364 (1987).
Quiroga, R. Q. Concept cells: the building blocks of declarative memory functions. Nat. Rev. Neurosci. 13, 587–597 (2012).
Quiroga, R. Q. Plugging in to human memory: advantages, challenges, and insights from human single-neuron recordings. Cell 179, 1015–1032 (2019).
Braitenberg, V. in Architectonics of the Cerebral Cortex (eds Brazier, M. A. B. & Petsche, H.) 443–465 (Raven, 1978).
Braitenberg, V. & Schüz, A. Cortex: Statistics and Geometry of Neuronal Connectivity 2nd edn (Springer, 1998).
Willshaw, D. J., Buneman, O. P. & Longuet-Higgins, H. C. Non-holographic associative memory. Nature 222, 960–962 (1969).
Palm, G. Neural Assemblies (Springer, 1982).
Palm, G. Cell assemblies as a guideline for brain research. Concepts Neurosci. 1, 133–147 (1990).
Palm, G., Knoblauch, A., Hauser, F. & Schüz, A. Cell assemblies in the cerebral cortex. Biol. Cybern. 108, 559–572 (2014).
Lundqvist, M., Rehn, M., Djurfeldt, M. & Lansner, A. Attractor dynamics in a modular network model of neocortex. Network 17, 253–276 (2006).
Lansner, A. Associative memory models: from the cell-assembly theory to biophysically detailed cortex simulations. Trends Neurosci. 32, 178–186 (2009).
Hopfield, J. J. & Tank, D. W. Computing with neural circuits: a model. Science 233, 625–633 (1986).
Hinton, G. E. & Shallice, T. Lesioning an attractor network: investigation of acquired dyslexia. Psychol.Rev. 98, 74–95 (1991).
Sommer, F. T. & Wennekers, T. Models of distributed associative memory networks in the brain. Theory Biosci. 122, 55–69 (2003).
Rigotti, M., Ben Dayan Rubin, D., Wang, X. J. & Fusi, S. Internal representation of task rules by recurrent dynamics: the importance of the diversity of neural responses. Front. Comput. Neurosci. 4, 24 (2010).
Huyck, C. R. & Passmore, P. J. A review of cell assemblies. Biol. Cybern. 107, 263–288 (2013).
Lindsay, G. W., Rigotti, M., Warden, M. R., Miller, E. K. & Fusi, S. Hebbian learning in a random network captures selectivity properties of the prefrontal cortex. J. Neurosci. 37, 11021–11036 (2017).
Ballintyn, B., Shlaer, B. & Miller, P. Spatiotemporal discrimination in attractor networks with short-term synaptic plasticity. J. Comput.Neurosci. 46, 279–297 (2019).
Seeholzer, A., Deger, M. & Gerstner, W. Stability of working memory in continuous attractor networks under the control of short-term plasticity. PLoS Comput. Biol. 15, e1006928 (2019).
Olshausen, B. A. & Field, D. J. Sparse coding of sensory inputs. Curr. Opin. Neurobiol. 14, 481–487 (2004).
Papadimitriou, C. H., Vempala, S. S., Mitropolsky, D., Collins, M. & Maass, W. Brain computation by assemblies of neurons. Proc. Natl Acad. Sci. USA 117, 14464–14472 (2020).
Hubel, D. Eye, Brain, and Vision 2nd edn (Scientific American Library, 1995).
Wennekers, T., Garagnani, M. & Pulvermüller, F. Language models based on Hebbian cell assemblies. J. Physiol. Paris. 100, 16–30 (2006).
Zipser, D., Kehoe, B., Littlewort, G. & Fuster, J. M. A spiking network model of short-term active memory. J. Neurosci. 13, 3406–3420 (1993).
Pulvermüller, F., Garagnani, M. & Wennekers, T. Thinking in circuits: towards neurobiological explanation in cognitive neuroscience. Biol. Cybern. 108, 573–593 (2014).
Dominey, P. F. Complex sensory-motor sequence learning based on recurrent state representation and reinforcement learning. Biol. Cybern. 73, 265–274 (1995).
Bibbig, A., Wennekers, T. & Palm, G. A neural network model of the cortico-hippocampal interplay and the representation of contexts. Behav. Brain Res. 66, 169–175 (1995).
Knoblauch, A. & Palm, G. Scene segmentation by spike synchronization in reciprocally connected visual areas. I. Local effects of cortical feedback. Biol. Cybern. 87, 151–167 (2002).
Knoblauch, A. & Palm, G. Scene segmentation by spike synchronization in reciprocally connected visual areas. II. Global assemblies and synchronization on larger space and time scales. Biol. Cybern. 87, 168–184 (2002).
Dominey, P. F. & Inui, T. Cortico-striatal function in sentence comprehension: insights from neurophysiology and modeling. Cortex 45, 1012–1018 (2009).
Verduzco-Flores, S., Bodner, M., Ermentrout, B., Fuster, J. M. & Zhou, Y. Working memory cells’ behavior may be explained by cross-regional networks with synaptic facilitation. PLoS ONE 4, e6399 (2009).
Eliasmith, C. et al. A large-scale model of the functioning brain. Science 338, 1202–1205 (2012).
Cazin, N. et al. Reservoir computing model of prefrontal cortex creates novel combinations of previous navigation sequences from hippocampal place-cell replay with spatial reward propagation. PLoS Comput. Biol. 15, e1006624 (2019).
Drude, L., von Neumann, T. & Haeb-Umbach, R. in Proc. IEEE Int. Conf. Acoust. Speech Signal Process. 11–15 (IEEE, 2018).
Tomasello, R., Wennekers, T., Garagnani, M. & Pulvermüller, F. Visual cortex recruitment during language processing in blind individuals is explained by Hebbian learning. Sci. Rep. 9, 3579 (2019).
Minsky, M. & Papert, S. Perceptrons (MIT Press, 1969).
McClelland, J. L. & Rumelhart, D. E. Parallel Distributed Processing: Explorations in the Microstructure of Cognition (MIT Press, 1986).
Hubel, D. Eye, Brain, and Vision (Freeman, 1988).
McClelland, J. L. & Rumelhart, D. E. Distributed memory and the representation of general and specific information. J. Exp. Psychol. 114, 159–188 (1985).
Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Nature 323, 533–536 (1986).
Richards, B. A. et al. A deep learning framework for neuroscience. Nat. Neurosci. 22, 1761–1770 (2019).
Elman, J. L. et al. Rethinking Innateness. A Connectionist Perspective on Development (MIT Press, 1996).
Rumelhart, D. E. & McClelland, J. L. Parallel Distributed Processing: Explorations in the Microstructure of Cognition (eds McClelland, J. L. & Rumelhart, D. E.) (MIT Press, 1986).
Elman, J. L. Finding structure in time. Cognit. Sci. 14, 179–211 (1990).
Rogers, T. T. & McClelland, J. L. Semantic Cognition: A Parallel Distributed Processing Approach (MIT Press, 2004).
Hinton, G. E. Learning multiple layers of representation. Trends Cogn. Sci. 11, 428–434 (2007).
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
Kriegeskorte, N. & Golan, T. Neural network models and deep learning. Curr. Biol. 29, R231–R236 (2019).
Yu, Y., Si, X., Hu, C. & Zhang, J. A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput. 31, 1235–1270 (2019).
Felleman, D. J. & Van Essen, D. C. Distributed hierarchical processing in the primate cerebral cortex. Cereb. Cortex 1, 1–47 (1991).
Krizhevsky, A., Sutskever, I. & Hinton, G. E. in Adv. Neural Inf. Process. Syst. (eds Bartlett, P. et al) 1106–1114 (2012).
Zhou, H.-Y., Liu, A.-A., Nie, W.-Z. & Nie, J. Multi-view saliency guided deep neural network for 3-D object retrieval and classification. IEEE Trans. Multimed. 22, 1496–1506 (2019).
Dahl, G. E., Yu, D., Deng, L. & Acero, A. Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Trans. Audio Speech Language Process. 20, 30–42 (2012).
Graves, A., Mohamed, A.-R. & Hinton, G. in Proc. IEEE Int. Conf. Acoust. Speech Signal Process. 6645–6649 (IEEE, 2013).
Smit, P., Virpioja, S. & Kurimo, M. Advances in subword-based HMM-DNN speech recognition across languages. Computer Speech Lang. 66, 101–158 (2021).
Hasson, U., Nastase, S. A. & Goldstein, A. Direct fit to nature: an evolutionary perspective on biological and artificial neural networks. Neuron 105, 416–434 (2020).
Szegedy, C. et al. Intriguing properties of neural networks. Preprint at arXiv https://arxiv.org/abs/1312.6199 (2014).
Nguyen, A., Yosinski, J. & Clune, J. in Comput. Vis. Pattern Recognit. 427–436 (IEEE, 2015).
Zhang, C., Bengio, S., Hardt, M., Recht, B. & Vinyals, O. Understanding deep learning requires re-thinking generalization. Commun. ACM 64, 107–115 (2021).
Alcorn, M. A. et al. in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 4845–4854 (IEEE, 2019).
Carlini, N. & Wagner, D. Towards evaluating the robustness of neural networks. Preprint at arXiv https://arxiv.org/abs/1608.04644 (2017).
Dapello, J. et al. Simulating a primary visual cortex at the front of CNNs improves robustness to image perturbations. Preprint at bioRxiv https://doi.org/10.1101/2020.06.16.154542 (2020).
Devereux, B. J., Clarke, A. & Tyler, L. K. Integrated deep visual and semantic attractor neural networks predict fMRI pattern-information along the ventral object processing pathway. Sci. Rep. 8, 1–12 (2018).
Tanaka, G. et al. Recent advances in physical reservoir computing: a review. Neural Netw. 115, 100–123 (2019).
Brodmann, K. Vergleichende Lokalisationslehre der Grosshirnrinde (Springer, 1909).
Fan, L. et al. The human Brainnetome Atlas: a new brain atlas based on connectional architecture. Cereb. Cortex 26, 3508–3526 (2016).
Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016).
Pandya, D. N. & Yeterian, E. H. in Cerebral Cortex. Association and Auditory Cortices Vol. 4 (eds Peters, A. & Jones, E. G.) 3–61 (Plenum, 1985).
Yeterian, E. H., Pandya, D. N., Tomaiuolo, F. & Petrides, M. The cortical connectivity of the prefrontal cortex in the monkey brain. Cortex 48, 58–81 (2012).
Waugh, J. L. et al. A registration method for improving quantitative assessment in probabilistic diffusion tractography. Neuroimage 189, 288–306 (2019).
Sarwar, T., Ramamohanarao, K. & Zalesky, A. Mapping connectomes with diffusion mri: deterministic or probabilistic tractography? Magnetic Reson. Med. 81, 1368–1384 (2019).
Descoteaux, M., Deriche, R., Knosche, T. R. & Anwander, A. Deterministic and probabilistic tractography based on complex fibre orientation distributions. IEEE Trans. Med. Imaging 28, 269–286 (2008).
Behrens, T. E. J., Berg, H. J., Jbabdi, S., Rushworth, M. F. S. & Woolrich, M. W. Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? Neuroimage 34, 144–155 (2007).
Kötter, R. Neuroscience databases: tools for exploring brain structure-function relationships. Phil. Trans. R. Soc. Lond. B 356, 1111–1120 (2001).
Bressler, S. L. & Menon, V. Large-scale brain networks in cognition: emerging methods and principles. Trends Cogn. Sci. 14, 277–290 (2010).
Hagmann, P. et al. Mapping the structural core of human cerebral cortex. PLoS Biol. 6, e159 (2008).
Honey, C. J., Kotter, R., Breakspear, M. & Sporns, O. Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc. Natl Acad. Sci. USA 104, 10240–10245 (2007).
Honey, C. J. et al. Predicting human resting-state functional connectivity from structural connectivity. Proc. Natl Acad. Sci. USA 106, 2035–2040 (2009).
Deco, G. & Jirsa, V. K. Ongoing cortical activity at rest: criticality, multistability, and ghost attractors. J. Neurosci. 32, 3366–3375 (2012).
Deco, G., Jirsa, V. K. & McIntosh, A. R. Resting brains never rest: computational insights into potential cognitive architectures. Trends Neurosci. 36, 268–274 (2013).
Deco, G., Tononi, G., Boly, M. & Kringelbach, M. L. Rethinking segregation and integration: contributions of whole-brain modelling. Nat. Rev. Neurosci. 16, 430–439 (2015).
Petersen, S. E. & Sporns, O. Brain networks and cognitive architectures. Neuron 88, 207–219 (2015).
Nakagawa, T. T., Adhikari, M. H. & Deco, G. Large-scale computational models of ongoing brain activity. Comput. Models Brain Behav. https://doi.org/10.1002/9781119159193.ch31 (2017).
Avena-Koenigsberger, A., Misic, B. & Sporns, O. Communication dynamics in complex brain networks. Nat. Rev. Neurosci. 19, 17–33 (2017).
Palm, G. Neural information processing in cognition: we start to understand the orchestra, but where is the conductor? Front. Comput. Neurosci. 10, 3 (2016).
van Albada, S. J. et al. Bringing anatomical information into neuronal network models. Preprint at arXiv https://arxiv.org/abs/2007.00031 (2020).
Arbib, M. A., Billard, A., Iacoboni, M. & Oztop, E. Synthetic brain imaging: grasping, mirror neurons and imitation. Neural Netw. 13, 975–997 (2000).
Kell, A. J. & McDermott, J. H. Deep neural network models of sensory systems: windows onto the role of task constraints. Curr. Opin. Neurobiol. 55, 121–132 (2019).
Gerstner, W. & Naud, R. How good are neuron models? Science 326, 379–380 (2009).
Teeter, C. et al. Generalized leaky integrate-and-fire models classify multiple neuron types. Nat. Commun. 9, 1–15 (2018).
Schwalger, T., Deger, M. & Gerstner, W. Towards a theory of cortical columns: from spiking neurons to interacting neural populations of finite size. PLoS Comput. Biol. 13, e1005507 (2017).
Malagarriga, D., Pons, A. J. & Villa, A. E. Complex temporal patterns processing by a neural mass model of a cortical column. Cognit. Neurodyn. 13, 379–392 (2019).
Jansen, B. H. & Rit, V. G. Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns. Biol. Cybern. 73, 357–366 (1995).
Potjans, T. C. & Diesmann, M. The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model. Cereb. Cortex 24, 785–806 (2014).
Einevoll, G. T. et al. The scientific case for brain simulations. Neuron 102, 735–744 (2019).
O’Connell, R. G., Shadlen, M. N., Wong-Lin, K. & Kelly, S. P. Bridging neural and computational viewpoints on perceptual decision-making. Trends Neurosci. 41, 838–852 (2018).
Hahn, G., Ponce-Alvarez, A., Deco, G., Aertsen, A. & Kumar, A. Portraits of communication in neuronal networks. Nat. Rev. Neurosci. 20, 117–127 (2019).
Kriegeskorte, N., Mur, M. & Bandettini, P. Representational similarity analysis - connecting the branches of systems neuroscience. Front. Syst. Neurosci. 2, 4 (2008).
Carota, F., Nili, H., Pulvermüller, F. & Kriegeskorte, N. Distinct fronto-temporal substrates of distributional and taxonomic similarity among words: evidence from RSA of BOLD signals. Neuroimage 224, 117408 (2021).
Papadopoulou, M., Friston, K. & Marinazzo, D. Estimating directed connectivity from cortical recordings and reconstructed sources. Brain Topogr. 32, 741–752 (2019).
Shen, K. et al. Exploring the limits of network topology estimation using diffusion-based tractography and tracer studies in the macaque cortex. Neuroimage 191, 81–92 (2019).
Kandel, E. R., Schwartz, J. H. & Jessell, T. M. Principles of Neural Sciences 4th edn (McGraw-Hill, 2000).
Matthews, G. G. Cellular Physiology of Nerve and Muscle (Wiley, 2009).
O’Reilly, R. C. & Munakata, Y. Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain (MIT Press, 2000).
Deco, G., Jirsa, V. K., Robinson, P. A., Breakspear, M. & Friston, K. The dynamic brain: from spiking neurons to neural masses and cortical fields. PLoS Comput. Biol. 4, e1000092 (2008).
Breakspear, M. Dynamic models of large-scale brain activity. Nat. Neurosci. 20, 340–352 (2017).
Burkitt, A. N. A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input. Biol. Cybern. 95, 1–19 (2006).
Brette, R. et al. Simulation of networks of spiking neurons: a review of tools and strategies. J. Comput. Neurosci. 23, 349–398 (2007).
Gerstner, W. & Kistler, W. M. Spiking Neuron Models: Single Neurons, Populations, Plasticity (Cambridge Univ. Press, 2002).
Li, S. et al. Dendritic computations captured by an effective point neuron model. Proc. Natl Acad. Sci. USA 116, 15244–15252 (2019).
London, M. & Häusser, M. Dendritic computation. Annu. Rev. Neurosci. 28, 503–532 (2005).
Bono, J. & Clopath, C. Modeling somatic and dendritic spike mediated plasticity at the single neuron and network level. Nat. Commun. 8, 706 (2017).
Venkadesh, S., Komendantov, A. O., Wheeler, D. W., Hamilton, D. J. & Ascoli, G. A. Simple models of quantitative firing phenotypes in hippocampal neurons: comprehensive coverage of intrinsic diversity. PLoS Comput. Biol. 15, e1007462 (2019).
Gidon, A. et al. Dendritic action potentials and computation in human layer 2/3 cortical neurons. Science 367, 83–87 (2020).
Faisal, A. A., Selen, L. P. & Wolpert, D. M. Noise in the nervous system. Nat. Rev. Neurosci. 9, 292–303 (2008).
Gerstner, W. & Kistler, W. M. Mathematical formulations of Hebbian learning. Biol. Cybern. 87, 404–415 (2002).
Tsumoto, T. Long-term potentiation and long-term depression in the neocortex. Prog. Neurobiol. 39, 209–228 (1992).
Artola, A. & Singer, W. Long-term depression of excitatory synaptic transmission and its relationship to long-term potentiation. Trends Neurosci. 16, 480–487 (1993).
Gerstner, W., Kempter, R., van Hemmen, J. L. & Wagner, H. A neuronal learning rule for sub-millisecond temporal coding. Nature 383, 76–81 (1996).
Kempter, R., Gerstner, W. & Van Hemmen, J. L. Hebbian learning and spiking neurons. Phys. Rev. E 59, 4498 (1999).
Caporale, N. & Dan, Y. Spike timing-dependent plasticity: a Hebbian learning rule. Annu. Rev. Neurosci. 31, 25–46 (2008).
Rumbell, T., Denham, S. L. & Wennekers, T. A spiking self-organizing map combining STDP, oscillations, and continuous learning. IEEE Trans. Neural Netw. Learn. Syst. 25, 894–907 (2014).
Mollick, J. A. et al. A systems-neuroscience model of phasic dopamine. Psychol. Rev. 127, 972–1021 (2020).
Thorpe, S. J. & Imbert, M. in Connectionism in Perspective (eds Pfeifer, R., Schreter, Z., Fogelman-Soulie, F. & Steels, L.) 63–92 (North Holland, 1989).
Marblestone, A. H., Wayne, G. & Kording, K. P. Toward an integration of deep learning and neuroscience. Front. Comput. Neurosci. 10, 94 (2016).
Pozzi, I., Bohté, S. & Roelfsema, P. A biologically plausible learning rule for deep learning in the brain. Preprint at arXiv https://arxiv.org/abs/1811.01768 (2018).
Lillicrap, T. P., Santoro, A., Marris, L., Akerman, C. J. & Hinton, G. Backpropagation and the brain. Nat. Rev. Neurosci. 21, 335–346 (2020).
Marcus, G. F. Negative evidence in language acquisition. Cognition 46, 53–85 (1993).
Goldberg, A. E. Constructions at Work: The Nature of Generalisation in Language (Oxford Univ. Press, 2006).
Goldberg, A. E. Explain Me This: Creativity, Competition and the Partial Productivity of Constructions (Princeton Univ. Press, 2019).
Pulvermüller, F. Neural reuse of action perception circuits for language, concepts and communication. Prog. Neurobiol. 160, 1–44 (2018).
Yuille, A. L. & Geiger, D. in The Handbook of Brain Theory and Neural Networks (ed. Arbib, M. A.) 1228–1231 (MIT Press, 2003).
Gurney, K., Prescott, T. J., Wickens, J. R. & Redgrave, P. Computational models of the basal ganglia: from robots to membranes. Trends Neurosci. 27, 453–459 (2004).
Knoblauch, A., Markert, H. & Palm, G. in Int. Work-Conf. Interplay Between Nat. Artif. Computat. Vol. 3562 (eds Mira, J. & Alvarez, J. R.) 405–414 (Springer, 2005).
Sommer, F. T. & Wennekers, T. Associative memory in networks of spiking neurons. Neural Netw. 14, 825–834 (2001).
Garagnani, M., Wennekers, T. & Pulvermüller, F. A neuroanatomically-grounded Hebbian learning model of attention-language interactions in the human brain. Eur. J. Neurosci. 27, 492–513 (2008).
Binzegger, T., Douglas, R. J. & Martin, K. A. A quantitative map of the circuit of cat primary visual cortex. J. Neurosci. 24, 8441–8453 (2004).
Thomson, A. M. & Lamy, C. Functional maps of neocortical local circuitry. Front. Neurosci. 1, 19–42 (2007).
Schmidt, M. et al. A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas. PLoS Comput. Biol. 14, e1006359 (2018).
Van Essen, D. C., Glasser, M. F., Dierker, D. L., Harwell, J. & Coalson, T. Parcellations and hemispheric asymmetries of human cerebral cortex analyzed on surface-based atlases. Cereb. Cortex 22, 2241–2262 (2012).
Elston, G. N., Benavides-Piccione, R. & DeFelipe, J. The pyramidal cell in cognition: a comparative study in human and monkey. J. Neurosci. 21, RC163 (2001).
Haug, H. Brain sizes, surfaces, and neuronal sizes of the cortex cerebri: a stereological investigation of man and his variability and a comparison with some mammals (primates, whales, marsupials, insectivores, and one elephant). Am. J. Anat. 180, 126–142 (1987).
Hellwig, B. A quantitative analysis of the local connectivity between pyramidal neurons in layers 2/3 of the rat visual cortex. Biol. Cybern. 82, 111–121 (2000).
Perin, R., Berger, T. K. & Markram, H. A synaptic organizing principle for cortical neuronal groups. Proc. Natl Acad. Sci.USA 108, 5419–5424 (2011).
Kaas, J. H. Topographic maps are fundamental to sensory processing. Brain Res. Bull. 44, 107–112 (1997).
Hopfield, J. J. & Tank, D. W. “Neural” computation of decisions in optimization problems. Biol. Cybern. 52, 141–152 (1985).
Garagnani, M., Lucchese, G., Tomasello, R., Wennekers, T. & Pulvermüller, F. A spiking neurocomputational model of high-frequency oscillatory brain responses to words and pseudowords. Front. Comput. Neurosci. 10, 145 (2017).
Douglas, R. J., Martin, K. A. & Whitteridge, D. A canonical microcircuit for neocortex. Neural Comput. 1, 480–488 (1989).
Young, M. P., Scannell, J. W. & Burns, G. The Analysis of Cortical Connectivity (Springer, 1995).
Eichert, N. et al. What is special about the human arcuate fasciculus? Lateralization, projections, and expansion. Cortex 118, 107–115 (2019).
Rojkova, K. et al. Atlasing the frontal lobe connections and their variability due to age and education: a spherical deconvolution tractography study. Brain Struct. Funct. 221, 1751–1766 (2016).
Fernandez-Miranda, J. C. et al. Asymmetry, connectivity, and segmentation of the arcuate fascicle in the human brain. Brain Struct. Funct. 220, 1665–1680 (2015).
Rilling, J. K. Comparative primate neuroimaging: insights into human brain evolution. Trends Cognit. Sci. 18, 46–55 (2014).
Petrides, M., Tomaiuolo, F., Yeterian, E. H. & Pandya, D. N. The prefrontal cortex: comparative architectonic organization in the human and the macaque monkey brains. Cortex 48, 46–57 (2012).
Thiebaut de Schotten, M., Dell’Acqua, F., Valabregue, R. & Catani, M. Monkey to human comparative anatomy of the frontal lobe association tracts. Cortex 48, 82–96 (2012).
Ardesch, D. J. et al. Evolutionary expansion of connectivity between multimodal association areas in the human brain compared with chimpanzees. Proc. Natl Acad. Sci. USA 116, 7101–7106 (2019).
Barbeau, E. B., Descoteaux, M. & Petrides, M. Dissociating the white matter tracts connecting the temporo-parietal cortical region with frontal cortex using diffusion tractography. Sci. Rep. 10, 8186 (2020).
Kietzmann, T., McClure, P. & Kriegeskorte, N. in Oxford Research Encyclopedia, Neuroscience (Oxford Univ. Press, 2019).
Schuecker, J., Schmidt, M., van Albada, S. J., Diesmann, M. & Helias, M. Fundamental activity constraints lead to specific interpretations of the connectome. PLoS Comput. Biol. 13, e1005179 (2017).
Friston, K. J. Functional and effective connectivity: a review. Brain Connect. 1, 13–36 (2011).
Friston, K., Moran, R. & Seth, A. K. Analysing connectivity with Granger causality and dynamic causal modelling. Curr. Opin. Neurobiol. 23, 172–178 (2013).
Sokolov, A. A. et al. Asymmetric high-order anatomical brain connectivity sculpts effective connectivity. Netw. Neurosci. 4, 871–890 (2020).
Zarghami, T. S. & Friston, K. J. Dynamic effective connectivity. Neuroimage 207, 116453 (2020).
Markov, N. T. et al. A weighted and directed interareal connectivity matrix for macaque cerebral cortex. Cereb. Cortex 24, 17–36 (2014).
Schmidt, M., Bakker, R., Hilgetag, C. C., Diesmann, M. & van Albada, S. J. Multi-scale account of the network structure of macaque visual cortex. Brain Struct. Funct. 223, 1409–1435 (2018).
Schmidt, M., Bakker, R., Hilgetag, C. C., Diesmann, M. & van Albada, S. J. Correction to: Multi-scale account of the network structure of macaque visual cortex. Brain Struct. Funct. 225, 1159–1162 (2020).
Deco, G. & Rolls, E. T. Neurodynamics of biased competition and cooperation for attention: a model with spiking neurons. J. Neurophysiol. 94, 295–313 (2005).
Bojak, I., Oostendorp, T. F., Reid, A. T. & Kötter, R. Towards a model-based integration of co-registered electroencephalography/functional magnetic resonance imaging data with realistic neural population meshes. Phil. Trans. R. Soc. A 369, 3785–3801 (2011).
Khaligh-Razavi, S. M. & Kriegeskorte, N. Deep supervised, but not unsupervised, models may explain IT cortical representation. PLoS Comput. Biol. 10, e1003915 (2014).
Tomasello, R., Garagnani, M., Wennekers, T. & Pulvermüller, F. A neurobiologically constrained cortex model of semantic grounding with spiking neurons and brain-like connectivity. Front. Comput. Neurosci. 12, 88 (2018).
Carlson, T. A., Simmons, R. A., Kriegeskorte, N. & Slevc, L. R. The emergence of semantic meaning in the ventral temporal pathway. J. Cogn. Neurosci. 26, 120–131 (2014).
Cichy, R. M., Khosla, A., Pantazis, D., Torralba, A. & Oliva, A. Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence. Sci. Rep. 6, 27755 (2016).
Kietzmann, T. C. et al. Recurrence is required to capture the representational dynamics of the human visual system. Proc. Natl Acad. Sci. USA 116, 21854–21863 (2019).
Desimone, R. & Duncan, J. Neural mechanisms of selective visual attention. Annu. Rev. Neurosci. 18, 193–222 (1995).
Buehlmann, A. & Deco, G. The neuronal basis of attention: rate versus synchronization modulation. J. Neurosci. 28, 7679–7686 (2008).
Lindsay, G. W. & Miller, K. D. How biological attention mechanisms improve task performance in a large-scale visual system model. eLife 7, e38105 (2018).
Lindsay, G. W. Attention in psychology, neuroscience, and machine learning. Front. Comput. Neurosci. 14, 29 (2020).
Duncan, J., Assem, M. & Shashidhara, S. Integrated intelligence from distributed brain activity. Trends Cogn. Sci. 24, 838–852 (2020).
Markert, H., Kaufmann, U., Kara Kayikci, Z. & Palm, G. Neural associative memories for the integration of language, vision and action in an autonomous agent. Neural Netw. 22, 134–143 (2009).
Ueno, T., Saito, S., Rogers, T. T. & Lambon Ralph, M. A. Lichtheim 2: synthesizing aphasia and the neural basis of language in a neurocomputational model of the dual dorsal-ventral language pathways. Neuron 72, 385–396 (2011).
Zhong, J., Cangelosi, A. & Wermter, S. Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives. Front. Behav. Neurosci. 8, 22 (2014).
Cangelosi, A., Schlesinger, M. & Smith, L. B. Developmental Robotics: From Babies to Robots (MIT Press, 2015).
Heinrich, S. & Wermter, S. Interactive natural language acquisition in a multi-modal recurrent neural architecture. Connect. Sci. 30, 99–133 (2018).
Raven, J. & Court, J. Manual for Raven’s Progressive Matrices and Vocabulary Scales (Harcourt Assessment, 2004).
Rast, A. D. et al. Behavioral learning in a cognitive neuromorphic robot: an integrative approach. IEEE Trans. Neural Netw. Learn. Syst. 29, 6132–6144 (2018).
Rolls, E. T. & Deco, G. Networks for memory, perception, and decision-making, and beyond to how the syntax for language might be implemented in the brain. Brain Res. 1621, 316–334 (2014).
Fuster, J. M. & Bressler, S. L. Cognit activation: a mechanism enabling temporal integration in working memory. Trends Cogn. Sci. 16, 207–218 (2012).
Fiebig, F. & Lansner, A. A spiking working memory model based on Hebbian short-term potentiation. J. Neurosci. 37, 83–96 (2017).
Pulvermüller, F. & Garagnani, M. From sensorimotor learning to memory cells in prefrontal and temporal association cortex: a neurocomputational study of disembodiment. Cortex 57, 1–21 (2014).
Schomers, M. R., Garagnani, M. & Pulvermüller, F. Neurocomputational consequences of evolutionary connectivity changes in perisylvian language cortex. J. Neurosci. 37, 3045–3055 (2017).
Binder, J. R. & Desai, R. H. The neurobiology of semantic memory. Trends Cogn. Sci. 15, 527–536 (2011).
Kiefer, M. & Pulvermüller, F. Conceptual representations in mind and brain: theoretical developments, current evidence and future directions. Cortex 48, 805–825 (2012).
Ralph, M. A., Jefferies, E., Patterson, K. & Rogers, T. T. The neural and computational bases of semantic cognition. Nat. Rev. Neurosci. 18, 42–55 (2017).
Harpaintner, M., Sim, E. J., Trumpp, N. M., Ulrich, M. & Kiefer, M. The grounding of abstract concepts in the motor and visual system: an fMRI study. Cortex 124, 1–22 (2020).
Damasio, A. R. The brain binds entities and events by multiregional activation from convergence zones. Neural Comput. 1, 123–132 (1989).
Garagnani, M. & Pulvermüller, F. Conceptual grounding of language in action and perception: a neurocomputational model of the emergence of category specificity and semantic hubs. Eur. J. Neurosci. 43, 721–737 (2016).
Chen, L., Ralph, M. A. L. & Rogers, T. T. A unified model of human semantic knowledge and its disorders. Nat. Hum. Behav. 1, 0039 (2017).
Tomasello, R., Garagnani, M., Wennekers, T. & Pulvermüller, F. Brain connections of words, perceptions and actions: a neurobiological model of spatio-temporal semantic activation in the human cortex. Neuropsychologia 98, 111–129 (2017).
Chang, Y.-N. & Lambon Ralph, M. A. A unified neurocomputational bilateral model of spoken language production in healthy participants and recovery in poststroke aphasia. Proc. Natl Acad. Sci. USA 117, 32779–32790 (2020).
Seghier, M. L. & Price, C. J. Interpreting and utilising intersubject variability in brain function. Trends Cognit. Sci. 22, 517–530 (2018).
Picht, T., Frey, D., Thieme, S., Kliesch, S. & Vajkoczy, P. Presurgical navigated TMS motor cortex mapping improves outcome in glioblastoma surgery: a controlled observational study. J. Neurooncol. 126, 535–543 (2016).
Cha, Y. J. et al. Prediction of response to stereotactic radiosurgery for brain metastases using convolutional neural networks. Anticancer Res. 38, 5437–5445 (2018).
Tuncer, M. S. et al. Towards a tractography-based risk stratification model for language area associated gliomas. Neuroimage Clin. 29, 102541 (2021).
Schaefer, A. et al. Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cereb. Cortex 28, 3095–3114 (2018).
Sotiropoulos, S. N. & Zalesky, A. Building connectomes using diffusion MRI: why, how and but. NMR Biomed. 32, e3752 (2017).
Rilling, J. K. et al. The evolution of the arcuate fasciculus revealed with comparative DTI. Nat. Neurosci. 11, 426–428 (2008).
Rilling, J. K., Glasser, M. F., Jbabdi, S., Andersson, J. & Preuss, T. M. Continuity, divergence, and the evolution of brain language pathways. Front. Evol. Neurosci. 3, 11 (2011).
Martin, A. The representation of object concepts in the brain. Annu. Rev. Psychol. 58, 25–45 (2007).
Barsalou, L. W. Grounded cognition. Annu. Rev. Psychol. 59, 617–645 (2008).
Borghi, A. M. et al. Words as social tools: language, sociality and inner grounding in abstract concepts. Phys. Life Rev. 29, 120–153 (2019).
Grisoni, L., Tomasello, R. & Pulvermüller, F. Correlated brain indexes of semantic prediction and prediction error: brain localization and category specificity. Cereb. Cortex 31, 1553–1568 (2021).
The authors thank A. Aertsen, A. Cangelosi, L. Fekonja, M. Garagnani, A. Glenberg, L. Grisoni, S. Harnad, A. Knoblauch, G. Palm, T. Picht, S. Rotter and W. Schäffner for comments and suggestions on earlier versions of the manuscript and related discussions. Research funding was provided by the European Research Council, Advanced Grant “Material Constraints Enabling Human Cognition” (ERC-2019-ADG 883811), and by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Cluster of Excellence “Matters of Activity. Image Space Material” (DFG EXC 2025/1 – 390648296).
The authors declare no competing interests.
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Pulvermüller, F., Tomasello, R., Henningsen-Schomers, M.R. et al. Biological constraints on neural network models of cognitive function. Nat Rev Neurosci 22, 488–502 (2021). https://doi.org/10.1038/s41583-021-00473-5
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