A5084661783- Memory and Neural Mechanisms
- Neuroscience and Neuropharmacology Research
- Neural dynamics and brain function
- Sleep and Wakefulness Research
- Zebrafish Biomedical Research Applications
- Vestibular and auditory disorders
- Neuroinflammation and Neurodegeneration Mechanisms
- Heart Rate Variability and Autonomic Control
- Neonatal and fetal brain pathology
- Evolutionary Algorithms and Applications
- Neural and Behavioral Psychology Studies
- Reinforcement Learning in Robotics
Ruhr University Bochum
2019-2025
The cerebellum is involved in the acquisition and consolidation of learned fear responses. Knowledge about its contribution to extinction learning, however, sparse. Extinction processes likely involve erasure memories, but there ample evidence that at least part original memory remains. We asked question whether persists within following training. renewal effect, reoccurrence extinguished during recall a context different from context, constitutes one phenomena indicating responses not fully...
The key elements for fear extinction learning are unexpected omissions of expected aversive events, which considered to be rewarding. Given its reception reward information, we tested the hypothesis that cerebellum contributes prediction error processing driving via connections with ventral tegmental area (VTA). Forty-three young and healthy participants performed a three-day conditioning paradigm in 7T MR scanner. VTA were active during unconditioned stimuli, particularly initial trials....
The key elements for fear extinction learning are unexpected omissions of expected aversive events, which considered to be rewarding. Given its reception reward information, we tested the hypothesis that cerebellum contributes prediction error processing driving via connections with ventral tegmental area (VTA). Forty-three young and healthy participants performed a three-day conditioning paradigm in 7T MR scanner. VTA were active during unconditioned stimuli, particularly initial trials....
The ability to extinguish learned fear responses is crucial for adaptive behavior. mesolimbic dopaminergic system originating in the ventral tegmental area has been proposed contribute extinction learning because of its critical role reward learning. unexpected omission aversive unconditioned stimuli (US) considered as rewarding (outcome better than expected) and drive We tested hypothesis that facilitated by drugs impeded anti-dopaminergic drugs. effects dopamine agonists [levodopa (100 mg)...
Reinforcement learning (RL) has become a popular paradigm for modeling animal behavior, analyzing neuronal representations, and studying their emergence during learning. This development been fueled by advances in understanding the role of RL both brain artificial intelligence. However, while machine set tools standardized benchmarks facilitate new methods comparison to existing ones, neuroscience, software infrastructure is much more fragmented. Even if sharing theoretical principles,...
Replay of neuronal sequences in the hippocampus during resting states and sleep play an important role learning memory consolidation. Consistent with these functions, replay have been shown to obey current spatial constraints. Nevertheless, does not necessarily reflect previous behavior can construct never-experienced sequences. Here, we propose a stochastic mechanism that prioritizes experiences based on three variables: 1. Experience strength, 2. experience similarity, 3. inhibition...
Fear extinction is a major component of exposure therapy for anxiety disorders. There initial evidence that the cerebellum contributes to fear learning, i.e., ability learn certain stimuli are no longer associated with an aversive outcome. So far, however, knowledge cerebellum's role in scarce. In present study, 6 Hz cerebellar transcranial alternating current stimulation (ctACS) was used modulate function during learning young and healthy human participants MRI study. A two-day differential...
The context-dependence of extinction learning has been well studied and requires the hippocampus. However, underlying neural mechanisms are still poorly understood. Using memory-driven reinforcement deep networks, we developed a model that learns to navigate autonomously in biologically realistic virtual reality environments based on raw camera inputs alone. Neither is context represented explicitly our model, nor change signaled. We find memory-intact agents learn distinct representations,...
Episodic memory has been studied extensively in the past few decades, but so far little is understood about how it drives future behavior. Here we propose that episodic can facilitate learning two fundamentally different modes: retrieval and replay, which reinstatement of hippocampal activity patterns during later sleep or awake quiescence. We study their properties by comparing three paradigms using computational modeling based on visually-driven reinforcement learning. Firstly, memories...
Abstract Replay of neuronal sequences in the hippocampus during resting states and sleep play an important role learning memory consolidation. Consistent with these functions, replay have been shown to obey current spatial constraints. Nevertheless, does not necessarily reflect previous behavior can construct never-experienced sequences. Here we propose a stochastic mechanism that prioritizes experiences based on three variables: 1. Experience strength, 2. experience similarity, 3....
Abstract The key elements for fear extinction learning are unexpected omissions of expected aversive events, which considered to be rewarding. Given its reception reward information, we tested the hypothesis that cerebellum contributes prediction error processing driving via connections with ventral tegmental area (VTA). Forty-three young and healthy participants performed a three-day conditioning paradigm in 7T MR scanner. VTA were active during unconditioned stimuli, particularly initial...
Abstract Reinforcement learning (RL) has become a popular paradigm for modeling animal behavior, analyzing neuronal representations, and studying their emergence during learning. This development been fueled by advances in understanding the role of RL both brain artificial intelligence. However, while machine set tools standardized benchmarks facilitate new methods comparison to existing ones, neuroscience, software infrastructure is much more fragmented. Even if sharing theoretical...
Abstract The context-dependence of extinction learning has been well studied and requires the hippocampus. However, underlying neural mechanisms are still poorly understood. Using memory-driven reinforcement deep networks, we developed a model that learns to navigate autonomously in biologically realistic VR environments based on raw camera inputs alone. Neither is context represented explicitly our model, nor change signaled. We find memory-intact agents learn distinct representations,...