Decision making under uncertainty in a spiking neural network model of the basal ganglia
J. Integr. Neurosci.. 2016-12-01; 15(04): 515-538
DOI: 10.1142/S021963521650028X
Lire sur PubMed
Héricé C(1)(2), Khalil R(2), Moftah M(3), Boraud T(1)(2), Guthrie M(1)(2),
Garenne A(1)(2).
Author information:
(1)* University de Bordeaux, Institut des Maladies Neurodégénératives, UMR 5293,
33000 Bordeaux, France.
(2)† CNRS, Institut des Maladies Neurodégénératives, UMR 5293, 33000 Bordeaux,
France.
(3)‡ University of Alexandria, Alexandria, Egypt.
The mechanisms of decision-making and action selection are generally thought to
be under the control of parallel cortico-subcortical loops connecting back to
distinct areas of cortex through the basal ganglia and processing motor,
cognitive and limbic modalities of decision-making. We have used these
properties to develop and extend a connectionist model at a spiking neuron level
based on a previous rate model approach. This model is demonstrated on
decision-making tasks that have been studied in primates and the
electrophysiology interpreted to show that the decision is made in two steps. To
model this, we have used two parallel loops, each of which performs
decision-making based on interactions between positive and negative feedback
pathways. This model is able to perform two-level decision-making as in
primates. We show here that, before learning, synaptic noise is sufficient to
drive the decision-making process and that, after learning, the decision is
based on the choice that has proven most likely to be rewarded. The model is
then submitted to lesion tests, reversal learning and extinction protocols. We
show that, under these conditions, it behaves in a consistent manner and
provides predictions in accordance with observed experimental data.