Abstract
Perceptual decision-making in the brain is commonly modeled as a competition among tuned cortical populations receiving stimulation according to their perceptual evidence. However, the contribution of evidence on the decision-making process changes through time. In this regard, the mechanisms controlling the sensitivity to perceptual evidence remain unknown. Here we explore this issue by using a biologically constrained model of the neocortex performing a dual-choice perceptual discrimination task. We combine mutual and global GABAergic inhibition, which are differentially regulated by acetylcholine (ACh), a neuromodulator linked to enhanced stimulus discriminability. We find that, while mutual inhibition determines the phase-space separation between two stable attractors representing each stimulus, global inhibition controls the formation of a chaotic attractor in-between the two, effectively protecting the weakest stimulus. Hence, under low ACh levels, where global inhibition dominates, the decision-making process is chaotic and less determined by the difference between perceptual evidences. On the contrary, under high ACh levels, where mutual inhibition dominates, the network becomes very sensitive to small differences between stimuli. Our results are in line with the putative role of ACh in enhanced stimulus discriminability and suggest that ACh levels control the sensitivity to sensory inputs by regulating the amount of chaos.
The current work has received funding from H2020-EU project VirtualBrainCloud, ID:826421. In addition, Adrián F. Amil is supported by a FI-AGAUR2020 scholarship from the Generalitat de Catalunya.
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References
Keung, W., Hagen, T.A., Wilson, R.C.: A divisive model of evidence accumulation explains uneven weighting of evidence over time. Nat. Commun. 11(1), 1–9 (2020)
Wang, X.-J.: Probabilistic decision making by slow reverberation in cortical circuits. Neuron 36(5), 955–968 (2002)
Teeter, C., et al.: Generalized leaky integrate-and-fire models classify multiple neuron types. Nat. Commun. 9(1), 1–15 (2018)
Kawaguchi, Y.: Selective cholinergic modulation of cortical GABAergic cell subtypes. J. Neurophysiol. 78(3), 1743–1747 (1997)
Rosenstein, M.T., Collins, J.J., De Luca, C.J.: A practical method for calculating largest Lyapunov exponents from small data sets. Physica D 65(1–2), 117–134 (1993)
Skarda, C.A., Freeman, W.J.: How brains make chaos in order to make sense of the world. Behav. Brain Sci. 10(2), 161–195 (1987)
van Vreeswijk, C., Sompolinsky, H.: Chaos in neuronal networks with balanced excitatory and inhibitory activity. Science 274(5293), 1724–1726 (1996)
Verschure, P.F.M.J.: Chaos-based learning. Complex Syst. 5, 359–370 (1991)
Puigbò, J.-Y., Arsiwalla, X.D., González-Ballester, M.A., Verschure, P.F.M .J.: Switching operation modes in the neocortex via cholinergic neuromodulation. Mol. Neurobiol. 57(1), 139–149 (2019). https://doi.org/10.1007/s12035-019-01764-w
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Amil, A.F., Puigbò, JY., Verschure, P.F.M.J. (2020). Cholinergic Control of Chaos and Evidence Sensitivity in a Neocortical Model of Perceptual Decision-Making. In: Vouloutsi, V., Mura, A., Tauber, F., Speck, T., Prescott, T.J., Verschure, P.F.M.J. (eds) Biomimetic and Biohybrid Systems. Living Machines 2020. Lecture Notes in Computer Science(), vol 12413. Springer, Cham. https://doi.org/10.1007/978-3-030-64313-3_10
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DOI: https://doi.org/10.1007/978-3-030-64313-3_10
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