Distinct temporal difference error signals in dopamine axons in three regions of the striatum in a decision-making task
Abstract
Different regions of the striatum regulate different types of behavior. However, how dopamine signals differ across striatal regions and how dopamine regulates different behaviors remain unclear. Here, we compared dopamine axon activity in the ventral, dorsomedial, and dorsolateral striatum, while mice performed a perceptual and value-based decision task. Surprisingly, dopamine axon activity was similar across all three areas. At a glance, the activity multiplexed different variables such as stimulus-associated values, confidence and reward feedback at different phases of the task. Our modeling demonstrates, however, that these modulations can be inclusively explained by moment-by-moment changes in the expected reward, i.e. the temporal difference error. A major difference between areas was the overall activity level of reward responses: reward responses in dorsolateral striatum were positively shifted, lacking inhibitory responses to negative prediction errors. The differences in dopamine signals put specific constraints on the properties of behaviors controlled by dopamine in these regions.
Data availability
A source code file has been provided for Figure 7. Fluorometry data has been deposited in Dryad available at: doi:10.5061/dryad.pg4f4qrmf.
-
Fluorometry DataDryad Digital Repository, doi:10.5061/dryad.pg4f4qrmf.
Article and author information
Author details
Funding
Japan Society for the Promotion of Science
- Iku Tsutsui-Kimura
Japan Society for the Promotion of Science
- Hideyuki Matsumoto
National Institute of Mental Health (R01MH095953,R01MH101207,R01MH110404,R01NS108740)
- Naoshige Uchida
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: All procedures were performed in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals and approved by the Harvard Animal Care and Use Committee (protocol #26-03). All surgeries were performed under aseptic conditions with animals anesthetized with isoflurane (1-2% at 0.5-1.0 l/min). Analgesia was administered pre (buprenorphine, 0.1 mg/kg, I.P) and postoperatively (ketoprofen, 5 mg/kg, I.P).
Copyright
© 2020, Tsutsui-Kimura et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 5,065
- views
-
- 777
- downloads
-
- 77
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Computational and Systems Biology
- Neuroscience
The basolateral amygdala (BLA) is a key site where fear learning takes place through synaptic plasticity. Rodent research shows prominent low theta (~3–6 Hz), high theta (~6–12 Hz), and gamma (>30 Hz) rhythms in the BLA local field potential recordings. However, it is not understood what role these rhythms play in supporting the plasticity. Here, we create a biophysically detailed model of the BLA circuit to show that several classes of interneurons (PV, SOM, and VIP) in the BLA can be critically involved in producing the rhythms; these rhythms promote the formation of a dedicated fear circuit shaped through spike-timing-dependent plasticity. Each class of interneurons is necessary for the plasticity. We find that the low theta rhythm is a biomarker of successful fear conditioning. The model makes use of interneurons commonly found in the cortex and, hence, may apply to a wide variety of associative learning situations.
-
- Neuroscience
Mice can generate a cognitive map of an environment based on self-motion signals when there is a fixed association between their starting point and the location of their goal.