Project Details
Projekt
Fear loops: Adaptive sensory coding in cortico-thalamic circuits upon associative learning.
Applicant
Professor Jan Gründemann, Ph.D.
Subject Area
Cognitive, Systems and Behavioural Neurobiology
Experimental and Theoretical Network Neuroscience
Experimental and Theoretical Network Neuroscience
Term
since 2023
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 520291101
Associative learning is a fundamental mechanism of behavioral adaptation to changes in the environment. Sensory stimuli from the environment that predict salient outcomes like threats or rewards are associated with those, resulting in behavioral changes in response to the stimuli when they are presented alone. For example, fear conditioning, a model of associative learning based on classical Pavlovian conditioning, leads to enhanced threat responses of an animal (e.g., freezing in mice) when it experiences a sensory stimulus (conditioned stimulus, CS, e.g., a tone) that was previously paired with an aversive outcome (unconditioned stimulus, US, e.g., a mild foot shock). Associative learning models like fear conditioning have been used extensively to study neuronal correlates of threat-related learning. For example, it is well described that individual neurons in the amygdala change their response patterns upon fear learning. Nevertheless, it has recently been shown that fear learning is most likely encoded in a distributed neuronal circuit including upstream areas like the auditory thalamus. Here we proposed to use modern circuit neuroscience tools to measure and manipulate neuronal activity in corticothalamic loops of auditory cortex, medial geniculate body and thalamic reticular nucleus during associative auditory fear learning and adaptive fear behaviors in freely moving animals. This research will help us to understand how activity and plasticity in cortico-fugal structures contribute to sensory plasticity as well as learning and memory formation. We will furthermore use computational neuroscience and machine learning tools to describe and decode how sensory processing evolves across the corticothalamic-loops upon learning. Finally, we will test if individual elements of the corticothalamic-loop encode behavioral states of the animals, e.g., freezing vs. exploration, and how manipulations of each of these elements affects the neuronal coding in the respective area as well as an animal’s behavioral state.
DFG Programme
Priority Programmes
