Choice-predicting neurons in the prelimbic cortex
DACH
Disciplines
Medical-Theoretical Sciences, Pharmacy (100%)
Keywords
- Neuronal Circuits,
- Decisions,
- Preforntal Cortex
Success often depends on inspired choices between risk-taking for exciting achievements or perseverance with safe, incremental advancements. In this project we aim to discover neuronal firing patterns triggering such inspired decisions based on internal valuations of probabilistic evidence. We will train rodents to perform a modified-Iowa-gambling-t ask, successfully adjusting choices between certain-small, or possible-big rewards with changing long-term advantages. Recently (Passecker et al., 2019), we have discovered a subset of specialized neurons in the prelimbic subdivision (PL) of medial PFC, whose firing predict the subsequent decision of the animal, even for unlikely choices. During this project, we will perform juxtacellular recording and labelling of these neurons to determine their cellular identity and axonal projections, we will manipulate neuronal activity during the gambling task and we will develop computational models to explain the network dynamics underlying choices during gambling and dynamically adjusting decisions based on internal valuations. This project provides a link between the role of identified individual neurons and large scale cellular assemblies for flexible decision making the FOR 5159 consortium supported by the DFG (Deutsche Forschungsgemeinschaft).
Research Output
- 61 Citations
- 3 Publications
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2023
Title Resolving the prefrontal mechanisms of adaptive cognitive behaviors: A cross-species perspective DOI 10.1016/j.neuron.2023.03.017 Type Journal Article Author Hanganu-Opatz I Journal Neuron Pages 1020-1036 -
2023
Title Differential behavior-related activity of distinct hippocampal interneuron types during odor-associated spatial navigation DOI 10.1016/j.neuron.2023.05.007 Type Journal Article Author Forro T Journal Neuron Link Publication -
2023
Title Hippocampus facilitates reinforcement learning under partial observability DOI 10.1101/2023.11.09.565503 Type Preprint Author Pedamonti D Pages 2023.11.09.565503 Link Publication