Biology subjects

Lange, G.

Publications and source records attributed to Lange, G..

3 recordsLinked to original sources

bioRxiv2025

Accumbal Dopamine and Acetylcholine Dynamics during Psychostimulant Sensitization

Behavioral sensitization to repeated psychostimulant exposure is believed to contribute to the development of addiction. Nucleus accumbens (NAcc) dopamine (DA) is known to be a key substrate in sensitization, though recent work suggests that striatal acetylcholine (ACh) may also play a critical role. However, underlying ACh changes and their relationship to DA signaling have not been characterized. Here, we used dual-color fiber photometry to simultaneously measure DA and ACh in the NAcc shell of mice across repeated injections of cocaine or amphetamine. Repeated exposure progressively elevated locomotor activity and increased slow extracellular DA while attenuating transient DA release. Psychostimulants reduced phasic ACh transient amplitude and frequency, an effect that sensitized with repeated injections. However, the temporal coupling of DA and ACh remained unchanged. To determine whether D2 receptors (D2Rs) on cholinergic interneurons (CINs) drive this effect, we generated CIN-selective D2R knockout (KO) mice. Surprisingly, KOs continued to show an acute decrease in ACh and intact DA-ACh correlations after psychostimulant administration. However, they failed to exhibit sensitization of either DA or ACh in response to repeated psychostimulant administration. Despite this lack of sensitization in underlying neuromodulator signaling, the KO mice nevertheless exhibited behavioral sensitization, though at a slower rate than wild-type. These findings suggest that neural sensitization to psychostimulants is dependent on D2R expressed on CINs, but that behavioral sensitization is not dependent on sensitization of these underlying signals.

neuroscience↗

bioRxiv2025

Temporal adaptation aids object recognition in deep convolutional neural networks in suboptimal viewing scenario's

The primate visual system excels in recognizing objects under challenging viewing scenarios. A neural mechanism that is thought to play a key role in this ability is rapid temporal adaptation, or the adjustment of neurons activity based on recent history. To understand how temporal adaptation may support object recognition, previous work has incorporated a variety of temporal feedback mechanisms in deep convolutional neural networks (DCNN) and explored how these mechanisms affect object recognition performance. While multiple adaptation mechanisms have been shown to impact model behavior, it remains unclear how the origin (intrinsic or recurrent) and the way the temporal feedback is integrated (additive or multiplicative) affects object recognition. Here, we compare the impact of four different temporal adaptation mechanisms on object recognition using three different task designs, including object recognition under either noise or occlusion, and in the context of novelty detection. Our results show that the effectiveness of temporal adaptation mechanisms for robust object recognition depends on the task and dataset. For objects embedded in noise, intrinsic adaptation excels with simple, high-contrast inputs, while recurrent mechanisms perform better with complex, low-contrast inputs, highlighting their focus on different visual features. Under dynamic occlusion, recurrent adaptation mechanisms exhibit a more progressive increase in performance over time, suggesting they better maintain object coherence when parts are obscured. For novelty detection, recurrent mechanisms show higher performance compared to intrinsic adaptation mechanisms, suggesting that recurrence aids in detecting global changes caused by the presentation of new objects. All together, these findings suggest that robust object recognition likely requires multiple temporal adaptation strategies in parallel to handle the diverse challenges of naturalistic visual settings.

neuroscience↗

bioRxiv2019

Interfering with a memory without erasing its trace

Previous research has shown that performance of a novice skill can be easily interfered with by subsequent training of another skill. We address the open questions whether extensively trained skills show the same vulnerability to interference as novice skills and which memory mechanism regulates interference between expert skills. We developed a recurrent neural network model of V1 able to learn from feedback experienced over the course of a long-term orientation discrimination experiment. After first exposing the model to one discrimination task for 3480 consecutive trials, we assessed how its performance was affected by subsequent training in a second, similar task. Training the second task strongly interfered with the first (highly trained) discrimination skill. The magnitude of interference depended on the relative amounts of training devoted to the different tasks. We used these and other model outcomes as predictions for a perceptual learning experiment in which human participants underwent the same training protocol as our model. Specifically, over the course of three months participants underwent baseline training in one orientation discrimination task for 15 sessions before being trained for 15 sessions on a similar task and finally undergoing another 15 sessions of training on the first task (to assess interference). Across all conditions, the pattern of interference observed empirically closely matched model predictions. According to our model, behavioral interference can be explained by antagonistic changes in neuronal tuning induced by the two tasks. Remarkably, this did not stem from erasing connections due to earlier learning but rather from a reweighting of lateral inhibition.

neuroscience↗