A pavlovian model of the amygdala and its influence within the medial temporal lobe - Carrere, Maxime; Alexandre, Frédéric (Frontiers in Systems Neuroscience, 2015)
Recent advances in neuroscience give us a better view of the inner structure of the amygdala, of its relations with other regions in the Medial Temporal Lobe (MTL) and of the prominent role of neuromodulation. They have particularly shed light on two kinds of neurons in the basal nucleus of the amygdala, the so-called fear neurons and extinction neurons. Fear neurons mediate context-dependent fear by receiving contextual information from the hippocampus, whereas extinction neurons are linked with the medial prefrontal cortex (mPFC) and involved in fear extinction. The computational model of the amygdala that we describe in this paper is primarily a model of pavlovian conditioning, but its architecture also emphasizes the central role of the amygdala in the MTL memory processes through three main information flows. (i) Thalamic and higher order sensory cortical inputs including from the perirhinal cortex are received in the lateral amygdalar nucleus, where CS-US associations can be acquired. (ii) These associations are subsequently modulated, in the basal nucleus of the amygdala, by contextual inputs coming from the hippocampus and the mPFC. Basal fear and extinction neurons indicate the currently valid association to their main targets including in the MTL and the mPFC. (iii) The competition for the choice of the pavlovian response is ultimately performed by projection of these amygdalar neurons in the central nucleus of the amygdala where, beyond motor responding, a hormonal response, including cholinergic modulation, is also triggered via the basal forebrain. In turn, acetylcholine modulates activation in the basal nucleus and facilitates learning in the hippocampus. Based on biologically founded arguments, our model replicates a number of biological experiments, proposes some predictions about the role of amygdalar regions and describes pavlovian conditioning as a distributed systemic learning, binding memory processes in the MTL.