Biology subjects

Diekmann, N.

Publications and source records attributed to Diekmann, N..

7 recordsLinked to original sources

bioRxiv2026

The first step is not always the hardest: A change-point analysis of predictive learning

The progress of learning is usually quantified by averaging responses across participants and/or multiple trials within a block. However, such approaches obscure the trial-by-trial progress of learning, which has been shown recently to express a rich variety of dynamics. An alternative approach which does not suffer from this problem is the detection and analysis of points of behavioral change, i.e., change-point analysis. Using change-point analysis, we reanalyzed data from human participants in different predictive learning tasks in which learned contingencies underwent reversal. We find that responses of individual participants were more accurately characterized by behavioral change points than the average learning curve. Importantly, change points significantly shifted to later trials during reversal learning indicating that reversal learning is more difficult than the initial learning. In a computational model based on deep reinforcement learning, we show that the change point shift required the replay of previous experiences, which in turn depends on the hippocampus. This finding is consistent with studies showing that lesions of the hippocampus yield faster reversal learning. In summary, we reaffirm the importance of the analysis of single participant responses, show that phenomenological learning rates are slower during reversal learning, and provide a theoretical account for this difference.

neuroscience↗

bioRxiv2025

Dopaminergic drugs modulate fear extinction related processes in humans, but effects are mild

The ability to extinguish learned fear responses is crucial for adaptive behavior. The mesolimbic dopaminergic system originating in the ventral tegmental area has been proposed to contribute to fear extinction learning because of its critical role in reward learning. The unexpected omission of aversive unconditioned stimuli (US) is considered as rewarding (outcome better than expected) and to drive extinction learning. We tested the hypothesis that extinction learning is facilitated by dopaminergic drugs and impeded by anti-dopaminergic drugs. The effects of dopamine agonists [levodopa (100 mg) and bromocriptine (1.25 mg)] and antagonists [tiapride (100 mg) and haloperidol (3 mg)] on fear extinction learning were compared to placebo in 146 young and healthy human participants. A three-day differential fear conditioning paradigm was performed with pupil size and skin conductance responses (SCRs) being recorded. Fear acquisition training was performed on day 1, extinction training on day 2, and recall was tested on day 3. The conditioned stimuli (CS+, CS-) consisted of two geometric figures. A short electrical stimulation was used as the aversive US. One of the four drugs or placebo was administered prior to the extinction phase on day 2. Overall, effects were small and seen only in the bromocriptine group. In line with our hypothesis, we measured reduced pupil dilation during late recall in the bromocriptine group compared to the placebo group, indicating faster re-extinction of spontaneously recovered fear reactions on the third day. Effects of levodopa and haloperidol were unspecific and related to generally increased SCR levels in the levodopa group (already prior to drug intake), and miotic side-effects of haloperidol. Findings provide additional support that the dopaminergic system contributes to extinction learning in humans, possibly by improving consolidation of fear extinction memory.

neuroscience↗

bioRxiv2025

Cerebellar transcranial alternating current stimulation in the theta band facilitates extinction of learned fear responses

Fear extinction is a major component of exposure therapy for anxiety disorders. There is initial evidence that the cerebellum contributes to fear extinction learning, i.e., the ability to learn that certain stimuli are no longer associated with an aversive outcome. So far, however, knowledge of the cerebellums role in extinction is scarce. In the present study, 6 Hz cerebellar transcranial alternating current stimulation (ctACS) was used to modulate cerebellar function during extinction learning in young and healthy human participants in an MRI study. A two- day differential fear conditioning paradigm was used with acquisition and extinction training being performed on day 1, and fear extinction recall being tested on day 2. 6 Hz ctACS reduced spontaneous recovery of the initial fear association during recall, stabilizing extinction effects compared to sham ctACS. fMRI data during recall revealed significantly reduced activation in cortical areas involved in initial fear acquisition, such as the anterior cingulate and insula, in the verum ctACS group compared to the sham group. During extinction training, on the other hand, the verum group exhibited more widespread cerebral activation compared to the sham group. Group differences were significant in occipital cortical areas. Although direct stimulation effects cannot be excluded, increased activation in the visual cortex may reflect enhanced encoding and processing of visual information during fear extinction learning. The findings suggest that theta-range oscillatory interactions between the cerebellum and cortical areas support extinction processes and provide causal evidence of the cerebellar role in the human fear extinction network. Significance StatementWhile the cerebellum is well known for its role in associative learning, its contribution to fear extinction learning remains underexplored. This study used 6 Hz cerebellar transcranial alternating current stimulation (ctACS) to modulate cerebellar function during extinction training in healthy participants. The present data demonstrate that ctACS enhances cerebral cortical activation during extinction training which is followed by enhanced recall of the extinction memory and subsequently reduced activation of cerebral cortical areas associated to spontaneous recovery of fear memory. These findings provide causal evidence for the cerebelluar involvement in the human fear extinction network and suggest that enhancing cerebellar theta oscillations may be useful to support exposure therapy.

neuroscience↗

bioRxiv2024

Human cerebellum and ventral tegmental area interact during extinction of learned fear

The key elements for fear extinction learning are unexpected omissions of expected aversive events, which are considered to be rewarding. Given its reception of reward information, we tested the hypothesis that the cerebellum contributes to reward-like prediction error processing driving extinction learning via its connections with the ventral tegmental area (VTA). Forty-three young and healthy participants performed a three-day fear conditioning paradigm in a 7T MR scanner. The cerebellum and VTA were active during unexpected omissions of aversive unconditioned stimuli in the initial extinction trials and in other learning phases, in line with the proposed role of prediction-error processing. Increased functional connectivity was observed between the cerebellum and VTA, indicating that they are functionally coupled during fear extinction learning. These results suggest that an interaction between the cerebellum and VTA should be incorporated into the existing model of the fear extinction network.

neuroscience↗

bioRxiv2022

CoBeL-RL: A neuroscience-oriented simulation framework for complex behavior and learning

Reinforcement learning (RL) has become a popular paradigm for modeling animal behavior, analyzing neuronal representations, and studying their emergence during learning. This development has been fueled by advances in understanding the role of RL in both the brain and artificial intelligence. However, while in machine learning a set of tools and standardized benchmarks facilitate the development of new methods and their comparison to existing ones, in neuroscience, the software infrastructure is much more fragmented. Even if sharing theoretical principles, computational studies rarely share software frameworks, thereby impeding the integration or comparison of different results. Machine learning tools are also difficult to port to computational neuroscience since the experimental requirements are usually not well aligned. To address these challenges we introduce CoBeL-RL, a closed-loop simulator of complex behavior and learning based on RL and deep neural networks. It provides a neuroscience-oriented framework for efficiently setting up and running simulations. CoBeL-RL offers a set of virtual environments, e.g. T-maze and Morris water maze, which can be simulated at different levels of abstraction, e.g. a simple gridworld or a 3D environment with complex visual stimuli, and set up using intuitive GUI tools. A range of RL algorithms, e.g. Dyna-Q and deep Q-network algorithms, is provided and can be easily extended. CoBeL-RL provides tools for monitoring and analyzing behavior and unit activity, and allows for fine-grained control of the simulation via interfaces to relevant points in its closed-loop. In summary, CoBeL-RL fills an important gap in the software toolbox of computational neuroscience.

bioinformatics↗

bioRxiv2022

A Model of Hippocampal Replay Driven by Experience and Environmental Structure Facilitates Spatial Learning

Replay of neuronal sequences in the hippocampus during resting states and sleep play an important role in learning and memory consolidation. Consistent with these functions, replay sequences have been shown to obey current spatial constraints. Nevertheless, replay does not necessarily reflect previous behavior and can construct never-experienced sequences. Here we propose a stochastic replay mechanism that prioritizes experiences based on three variables: 1. Experience strength, 2. experience similarity, and 3. inhibition of return. Using this prioritized replay mechanism to train reinforcement learning agents leads to far better performance than using random replay. Its performance is close to the state-of-the-art, but computationally intensive, algorithm by Mattar & Daw (2018). Importantly, our model reproduces diverse types of replay because of the stochasticity of the replay mechanism and experience-dependent differences between the three variables. In conclusion, a unified replay mechanism generates diverse replay statistics and is efficient in driving spatial learning.

neuroscience↗

bioRxiv2020

Context-dependent extinction learning emerging from raw sensory inputs: A reinforcement learning approach

The context-dependence of extinction learning has been well studied and requires the hippocampus. However, the underlying neural mechanisms are still poorly understood. Using memory-driven reinforcement learning and deep neural 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 in our model, nor is context change signaled. We find that memory-intact agents learn distinct context representations, and develop ABA renewal, whereas memory-impaired agents do not. These findings reproduce the behavior of control and hippocampal animals, respectively. We therefore propose that the role of the hippocampus in the context-dependence of extinction learning might stem from its function in episodic-like memory and not in context-representation per se. We conclude that context-dependence can emerge from raw visual inputs.

neuroscience↗