Javier Baladron

Learn More
We derive the mean-field equations arising as the limit of a network of interacting spiking neurons, as the number of neurons goes to infinity. The neurons belong to a fixed number of populations and are represented either by the Hodgkin-Huxley model or by one of its simplified version, the FitzHugh-Nagumo model. The synapses between neurons are either(More)
We introduce a spiking neural network of the basal ganglia capable of learning stimulus-action associations. We model learning in the three major basal ganglia pathways, direct, indirect and hyperdirect, by spike time dependent learning and considering the amount of dopamine available (reward). Moreover, we allow to learn a cortico-thalamic pathway that(More)
We derive the mean-field equations arising as the limit of a network of interacting spiking neurons, as the number of neurons goes to infinity. The neurons belong to a fixed number of populations and are represented either by the HodgkinHuxley model or by one of its simplified version, the Fitzhugh-Nagumo model. The synapses between neurons are either(More)
We study localised activity patterns in neural field equations posed on the Euclidean plane; such models are commonly used to describe the coarse-grained activity of large ensembles of cortical neurons in a spatially continuous way. We employ matrix-free Newton-Krylov solvers and perform numerical continuation of localised patterns directly on the integral(More)
Theories and models of the basal ganglia have mainly focused on the role of three different corticothalamic pathways: direct, indirect and hyperdirect. Although the indirect and the hyperdirect pathways are linked through the bidirectional connections between the subthalamic nucleus (STN) and the external globus pallidus (GPe), the role of their(More)
  • 1