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Adaptive Inhibition for Optimal Energy Consumption by Animals, Robots and Neurocomputers

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From Animals to Animats 16 (SAB 2022)

Abstract

In contrast to artificial systems, animals must forage for food. In biology, the availability of energy is typically both precarious and highly variable. Most importantly, the very structure of organisms is dependent on the continuous metabolism of nutrients into ATP, and its use in maintaining homeostasis. This means that energy is at the centre of all biological processes, including cognition. So far, in computational neuroscience and artificial intelligence, this issue has been overlooked. In simulations of cognitive processes, whether at the neural level, or the level of larger brain systems, the constant and ample supply of energy is implicitly assumed. However, studies from the biological sciences indicate that much of the brain’s processes are in place to maintain allostasis, both of the brain itself and of the organism as a whole. This also relates to the fact that different neural populations have different energy needs. Many artificial systems, including robots and laptop computers, have circuitry in place to measure energy consumption. However, this information is rarely used in controlling the details of cognitive processing to minimize energy consumption. In this work, we make use of some of this circuitry and explicitly connect it to the processing requirements of different cognitive subsystems and show first how a cognitive model can learn the relation between cognitive ‘effort’, the quality of the computations and energy consumption, and second how an adaptive inhibitory mechanism can learn to only use the amount of energy minimally needed for a particular task. We argue that energy conservation is an important goal of central inhibitory mechanisms, in addition to its role in attentional and behavioral selection.

This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program - Humanities and Society (WASP-HS) funded by the Marianne and Marcus Wallenberg Foundation and the Marcus and Amalia Wallenberg Foundation.

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References

  1. Arora, A., Gouda, M.G.: Closure and convergence: a foundation of fault-tolerant computing. IEEE Trans. Softw. Eng. 19, 1015–1027 (1993)

    Article  Google Scholar 

  2. Aston-Jones, G., Cohen, J.D.: Adaptive gain and the role of the locus coeruleus-norepinephrine system in optimal performance. J. Comp. Neurol. 493, 99–110 (2005)

    Article  Google Scholar 

  3. Baca, S.M., Marin-Burgin, A., Wagenaar, D.A., Kristan, W.B.: Widespread inhibition proportional to excitation controls the gain of a leech behavioral circuit. Neuron 57, 276–289 (2008)

    Article  Google Scholar 

  4. Balkenius, C., Johansson, B., Tjøstheim, T.A.: Ikaros: a framework for controlling robots with system-level brain models. Int. J. Adv. Robot. Syst. 17 (2020)

    Google Scholar 

  5. Balkenius, C., Morén, J.: A computational model of context processing. In: 6th International Conference on the Simulation of Adaptive Behaviour. Citeseer (2000)

    Google Scholar 

  6. Berridge, K.C.: The debate over dopamine’s role in reward: the case for incentive salience. Psychopharmacology 191, 391–431 (2006). https://doi.org/10.1007/s00213-006-0578-x

    Article  Google Scholar 

  7. Berridge, K.C., Robinson, T.E.: What is the role of dopamine in reward: hedonic impact, reward learning, or incentive salience? Brain Res. Rev. 28, 309–369 (1998)

    Article  Google Scholar 

  8. Blakemore, C., Carpenter, R.H.S., Georgeson, M.A.: Lateral inhibition between orientation detectors in the human visual system. Nature 228, 37–39 (1970)

    Article  Google Scholar 

  9. Charnov, E.L.: Optimal foraging, the marginal value theorem. Theor. Popul. Biol. 9(2), 129–36 (1976)

    Article  Google Scholar 

  10. Choi, D.W.: Glutamate neurotoxicity in cortical cell culture is calcium dependent. Neurosci. Lett. 58, 293–297 (1985)

    Article  Google Scholar 

  11. Cole, B.J., Robbins, T.W.: Forebrain norepinephrine: role in controlled information processing in the rat. Neuropsychopharmacol. Off. Publ. Ame. Coll. Neuropsychopharmacol. 7(2), 129–42 (1992)

    Google Scholar 

  12. Cox, D.D., Dean, T.L.: Neural networks and neuroscience-inspired computer vision. Curr. Biol. 24, R921–R929 (2014)

    Article  Google Scholar 

  13. Frank, M.J.: Hold your horses: a dynamic computational role for the subthalamic nucleus in decision making. Neural Netw. Off. J. Int. Neural Netw. Soc. 19(8), 1120–1136 (2006)

    Article  Google Scholar 

  14. Glimcher, P.W.: Understanding dopamine and reinforcement learning: the dopamine reward prediction error hypothesis. Proc. Natl. Acad. Sci. 108(Supplement 3), 15647–15654 (2011)

    Article  Google Scholar 

  15. Gold, J.I., Shadlen, M.N.: The neural basis of decision making. Ann. Rev. Neurosci. 30, 535–574 (2007)

    Article  Google Scholar 

  16. Gray, J., McNaughton, N.: The Neuropsychology of Anxiety. Oxford University Press, New York (2000)

    Google Scholar 

  17. Holroyd, C.B., Coles, M.G.H.: The neural basis of human error processing: reinforcement learning, dopamine, and the error-related negativity. Psychol. Rev. 109(4), 679–709 (2002)

    Article  Google Scholar 

  18. Horne, J.A.: Sleep function, with particular reference to sleep deprivation. Ann. Clin. Res. 17(5), 199–208 (1985)

    Google Scholar 

  19. Matsumoto, M., Hikosaka, O.: Two types of dopamine neuron distinctly convey positive and negative motivational signals. Nature 459, 837–841 (2009)

    Article  Google Scholar 

  20. Merchant, H., Naselaris, T., Georgopoulos, A.P.: Dynamic sculpting of directional tuning in the primate motor cortex during three-dimensional reaching. J. Neurosci. 28, 9164–9172 (2008)

    Article  Google Scholar 

  21. Merrer, J.L., Becker, J.A.J., Befort, K., Kieffer, B.L.: Reward processing by the opioid system in the brain. Physiol. Rev. 89(4), 1379–1412 (2009)

    Article  Google Scholar 

  22. Pertermann, M., Mückschel, M., Adelhöfer, N., Ziemssen, T., Beste, C.: On the interrelation of 1/f neural noise and norepinephrine system activity during motor response inhibition. J. Neurophysiol. 121(5), 1633–1643 (2019)

    Article  Google Scholar 

  23. Posner, M.I., Rafal, R.D., Choate, L.S., Vaughan, J.: Inhibition of return: neural basis and function. Cogn. Neuropsychol. 2, 211–228 (1985)

    Article  Google Scholar 

  24. Satoh, M., Minami, M.: Molecular pharmacology of the opioid receptors. Pharmacol. Ther. 68(3), 343–364 (1995)

    Article  Google Scholar 

  25. van Steenbergen, H., Eikemo, M., Leknes, S.: The role of the opioid system in decision making and cognitive control: a review. Cogn. Affect. Behav. Neurosci. 19(3), 435–458 (2019). https://doi.org/10.3758/s13415-019-00710-6

    Article  Google Scholar 

  26. Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: DeepFace: closing the gap to human-level performance in face verification. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701–1708 (2014)

    Google Scholar 

  27. Tononi, G., Cirelli, C.: Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration. Neuron 81, 12–34 (2014)

    Article  Google Scholar 

  28. Towal, R.B., Mormann, M.M., Koch, C.: Simultaneous modeling of visual saliency and value computation improves predictions of economic choice. Proc. Natl. Acad. Sci. 110, E3858–E3867 (2013)

    Article  Google Scholar 

  29. Tsividis, Y.: Operation and Modeling of the MOS Transistor. McGraw-Hill, Inc. (1987)

    Google Scholar 

  30. Watkins, C.J., Dayan, P.: Q-learning. Mach. Learn. 8(3), 279–292 (1992). https://doi.org/10.1007/BF00992698

    Article  MATH  Google Scholar 

  31. Wise, R.A.: Dopamine, learning and motivation. Nat. Rev. Neurosci. 5, 483–494 (2004)

    Article  Google Scholar 

  32. Xiong, W., Chen, W.R.: Dynamic gating of spike propagation in the mitral cell lateral dendrites. Neuron 34, 115–126 (2002)

    Article  Google Scholar 

  33. Xue, M., Atallah, B.V., Scanziani, M.: Equalizing excitation-inhibition ratios across visual cortical neurons. Nature 511, 596–600 (2014)

    Article  Google Scholar 

  34. Yang, G.R., Murray, J.D., Wang, X.J.: A dendritic disinhibitory circuit mechanism for pathway-specific gating. Nat. Commun. 7, 1–14 (2016)

    Google Scholar 

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Correspondence to Christian Balkenius .

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Tjøstheim, T.A., Johansson, B., Balkenius, C. (2022). Adaptive Inhibition for Optimal Energy Consumption by Animals, Robots and Neurocomputers. In: Cañamero, L., Gaussier, P., Wilson, M., Boucenna, S., Cuperlier, N. (eds) From Animals to Animats 16. SAB 2022. Lecture Notes in Computer Science(), vol 13499. Springer, Cham. https://doi.org/10.1007/978-3-031-16770-6_9

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  • DOI: https://doi.org/10.1007/978-3-031-16770-6_9

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