Distributed and Mixed Information in Monosynaptic Inputs to Dopamine Neurons

Abstract

SUMMARY Dopamine neurons encode the difference between actual and predicted reward, or reward prediction error (RPE). Although many models have been proposed to account for this computation, it has been difficult to test these models experimentally. Here we established an awake electrophysiological recording system, combined with rabies virus and optogenetic cell-type identification, to characterize the firing patterns of monosynaptic inputs to dopamine neurons while mice performed classical conditioning tasks. We found that each variable required to compute RPE, including actual and predicted reward, was distributed in input neurons in multiple brain areas. Further, many input neurons across brain areas signaled combinations of these variables. These results demonstrate that even simple arithmetic computations such as RPE are not localized in specific brain areas but rather distributed across multiple nodes in a brain-wide network. Our systematic method to examine both activity and connectivity revealed unexpected redundancy for a simple computation in the brain.

Striatal and Tegmental Neurons Code Critical Signals for Temporal-Difference Learning of State Value in Domestic Chicks

“…These types generally showed significantly different firing rates, except that the difference between type-1 and type-3 MSt neurons (excitation subtype) were not statistically different. It is thus suggested the recorded set of neurons formed a continuum spectrum, rather than distinct groups separated by gaps, similarly to those shown in recent comparable study in mice (Tian et al, 2016). …”

“…In a very recent paper in mice Tian et al (2016), that appeared after the submission of our present study), neuronal activities were recorded from neurons with confirmed monosynaptic connection to DA-ergic neurons. These input neurons were distributed widely in various brain regions including dorsal and ventral striatum, as well as lateral hypothalamus and tegmental nuclei.…”

“…Interestingly, they found diverse sets of firing patterns in these regions, similar to those found in our present study (Figures 3–8). Tian et al (2016) also reported those neurons that coded “pure reward,” “pure expectation,” or a mixture of both. In particular, a subset of striatal and tegmental neurons coded partial prediction error signal, similarly to our chick cases.…”

Lateral Hypothalamic GABAergic Neurons Encode Reward Predictions that Are Relayed to the Ventral Tegmental Area to Regulate Learning

“…For example, orbital frontal cortex (OFC) provides VTA with information about complex aspects of the task that must be inferred to predict the future value of rewards [44], and the ventral striatum contributes learning about the duration of task states [45]. Contrary to recent suggestions [46],this shows that VTA neurons do receive qualitatively different types of associative information from multiple sources. Understanding how dopaminergic (and other) neurons in the VTA integrate input from the LH GABA neurons and these other sources, and return it to update these various representations, is an important future goal.…”

VTA dopamine neuron plasticity – the unusual suspects

“…Much of the existing data on plasticity in the VTA is focused on excitatory input onto DA neurons, with good reason. DA neurons can switch from tonic firing activity (1-4 Hz) to a burst firing pattern (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) at the presentation of behaviourally relevant stimuli (Paladini & Roeper, 2014), and this switch appears to be triggered by presynaptic excitatory inputs (Chergui et al, 1993;Floresco et al, 2003) arising from various parts of the brain (Watabe-Uchida et al, 2012;Beier et al, 2015;Tian et al, 2016). Thus, excitatory inputs critically influence DA neuron output, and consequently, DA-dependent behaviours.…”