Skip to main content Accessibility help

The architecture challenge: Future artificial-intelligence systems will require sophisticated architectures, and knowledge of the brain might guide their construction

  • Gianluca Baldassarre (a1), Vieri Giuliano Santucci (a1), Emilio Cartoni (a1) and Daniele Caligiore (a1)


In this commentary, we highlight a crucial challenge posed by the proposal of Lake et al. to introduce key elements of human cognition into deep neural networks and future artificial-intelligence systems: the need to design effective sophisticated architectures. We propose that looking at the brain is an important means of facing this great challenge.



Hide All
Anderson, M. L. (2003) Embodied cognition: A field guide. Artificial Intelligence 149(1):91130.
Baldassarre, G. (2011) What are intrinsic motivations? A biological perspective. In: Proceedings of the International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob-2011), ed. Cangelosi, A., Triesch, J., Fasel, I., Rohlfing, K., Nori, F., Oudeyer, P.-Y., Schlesinger, M. & Nagai, Y., pp. E18. IEEE.
Baldassarre, G., Caligiore, D. & Mannella, F. (2013a) The hierarchical organisation of cortical and basal-ganglia systems: A computationally-informed review and integrated hypothesis. In: Computational and robotic models of the hierarchical organisation of behaviour, ed. Baldassarre, G. & Mirolli, M., pp. 237–70. Springer-Verlag.
Baldassarre, G., Mannella, F., Fiore, V. G., Redgrave, P., Gurney, K. & Mirolli, M. (2013b) Intrinsically motivated action-outcome learning and goal-based action recall: A system-level bio-constrained computational model. Neural Networks 41:168–87.
Baldassarre, G. & Mirolli, M., eds. (2013) Intrinsically motivated learning in natural and artificial systems. Springer.
Baldassarre, G., Stafford, T., Mirolli, M., Redgrave, P., Ryan, R. M. & Barto, A. (2014) Intrinsic motivations and open-ended development in animals, humans, and robots: An overview. Frontiers in Psychology 5:985.
Caligiore, D., Borghi, A., Parisi, D. & Baldassarre, G. (2010) TRoPICALS: A computational embodied neuroscience model of compatibility effects. Psychological Review 117(4):1188–228.
Caligiore, D., Pezzulo, G., Baldassarre, G., Bostan, A. C., Strick, P. L., Doya, K., Helmich, R. C., Dirkx, M., Houk, J., Jörntell, H., Lago-Rodriguez, A., Galea, J. M., Miall, R. C., Popa, T., Kishore, A., Verschure, P. F. M. J., Zucca, R. & Herreros, I. (2016) Consensus paper: Towards a systems-level view of cerebellar function: The interplay between cerebellum, basal ganglia, and cortex. The Cerebellum 16(1):203–29. doi: 10.1007/s12311-016-0763-3.
Churchland, M. M., Cunningham, J. P., Kaufman, M. T., Foster, J. D., Nuyujukian, P., Ryu, S. I. & Shenoy, K. V. (2012) Neural population dynamics during reaching. Nature 487:5156.
Doya, K. (1999) What are the computations of the cerebellum, the basal ganglia and the cerebral cortex? Neural Networks 12(7–8):961–74.
Franklin, S. (2007) A foundational architecture for artificial general intelligence. In: Advances in artificial general intelligence: Concepts, architectures and algorithms: Proceedings of the AGI Workshop 2006, ed. Want, P. & Goertzel, B., pp. 3654. IOS Press.
Graybiel, A. M. (2005) The basal ganglia: learning new tricks and loving it. Current Opinion in Neurobiology 15(6):638–44.
Houk, J. C., Adams, J. L. & Barto, A. G. (1995) A model of how the basal ganglia generate and use neural signals that predict reinforcement. In: Models of information processing in the basal ganglia, ed. Houk, J. C., Davids, J. L. & Beiser, D. G., pp. 249–70. MIT Press.
Kawato, M., Kuroda, S. & Schweighofer, N. (2011) Cerebellar supervised learning revisited: biophysical modeling and degrees-of-freedom control. Current Opinion in Neurobiology 21(5):791800.
Lisman, J. E. & Grace, A. A. (2005) The hippocampal-VTA loop: Controlling the entry of information into long-term memory. Neuron 46:703–13.
Mannella, F. & Baldassarre, G. (2015) Selection of cortical dynamics for motor behaviour by the basal ganglia. Biological Cybernetics 109:575–95.
Mannella, F., Gurney, K. & Baldassarre, G. (2013) The nucleus accumbens as a nexus between values and goals in goal-directed behavior: A review and a new hypothesis. Frontiers in Behavioral Neuroscience 7(135):e129.
Milner, D. & Goodale, M. (2006) The visual brain in action. Oxford University Press.
Mirolli, M., Mannella, F. & Baldassarre, G. (2010) The roles of the amygdala in the affective regulation of body, brain and behaviour. Connection Science 22(3):215–45.
Mogenson, G. J., Jones, D. L. & Yim, C. Y. (1980) From motivation to action: Functional interface between the limbic system and the motor system. Progress in Neurobiology 14(2–3):6997.
Penhune, V. B. & Steele, C. J. (2012) Parallel contributions of cerebellar, striatal and M1 mechanisms to motor sequence learning. Behavioural Brain Research 226(2):579–91.
Pfeifer, R. & Gómez, G. (2009) Morphological computation–connecting brain, body, and environment. In: Creating brain-like intelligence, ed. Sendhoff, B., Körner, E, Ritter, H. & Doya, K., pp. 6683. Springer.
Redgrave, P. & Gurney, K. (2006) The short-latency dopamine signal: a role in discovering novel actions? Nature Reviews Neuroscience 7:967–75.
Santucci, V. G., Baldassarre, G. & Mirolli, M. (2016), GRAIL: A goal-discovering robotic architecture for intrinsically-motivated learning, IEEE Transactions on Cognitive and Developmental Systems 8(3):214–31.
Scott, S. H. (2004) Optimal feedback control and the neural basis of volitional motor control. Nature Reviews Neuroscience 5(7):532–46.
Shadmehr, R. & Krakauer, J. W. (2008) A computational neuroanatomy for motor control. Experimental Brain Research 185(3):359–81.
Weng, J., McClelland, J., Pentland, A., Sporns, O., Stockman, I., Sur, M. & Thelen, E. (2001) Autonomous mental development by robots and animals. Science 291(5504):599600.
Wolpert, D. M., Miall, R. C. & Kawato, M. (1998) Internal models in the cerebellum. Trends in Cognitive Science 2(9):338–47.


Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed