Biology subjects

Averbeck, B.

Publications and source records attributed to Averbeck, B..

11 recordsLinked to original sources

bioRxiv2026

Long-term Learning Induces Plastic Changes in Frontostriatal Circuits

Neural activity in frontal-striatal circuits underlies reinforcement learning. Traditional theories suggest that reinforcement signals, which drive learning, strengthen connections within the basal ganglia. This strengthening is believed to shift information processing from cortical regions to subcortical regions as learning becomes established over time. To examine this hypothesis, we trained macaques to associate multiple sets of images with their values. Selecting different images led to either an increase (+2, +1) or a decrease (-1, -2) in the number of tokens, which subsequently determined the amount of juice reward the macaques received. We simultaneously recorded neuronal activity from orbitofrontal cortex, ventral striatum, amygdala, and dorsomedial thalamic nucleus, analyzing the dynamic changes in these brain regions during both the initial learning and overlearned stages. The results indicated that as learning progressed from the initial stage to the overlearned stage, information processing shifted from the ventral striatum to the orbitofrontal cortex, corresponding to the abstraction from stimulus value to state value. This finding challenges traditional theories and provides a new perspective on the neural circuit mechanisms of learning.

neuroscience↗

bioRxiv2025

Differential contribution of HCN1 and HCN4 to the synchronisation of sinoatrial pacemaker cells

The hearts ability to beat with high precision, with a regular and steady rhythm relies on the synchronised activity of pacemaker cells in the sinoatrial node (SAN), which communicate with one another through gap junctions. This process ensures that electrical impulses are organised and reach a critical mass to ignite the electrical activity of the surrounding atrial tissue and trigger the regular heartbeat. HypothesisWe hypothesise that hyperpolarisation-activated, cyclic nucleotide-gated (HCN) channels play a pivotal role in maintaining this synchronisation process. This idea aligns with the well-established role of HCN channels in stabilising the membrane potential in the voltage range of the slow diastolic depolarisation, counteracting voltage fluctuations and effectively filtering out variations in the beating rate from neighbouring cells. AimWe focus on two specific HCN channel subtypes--HCN1 and HCN4--and their contributions to the rapid synchronisation of pacemaker cells, a phenomenon known as phasic entrainment. Using two HCN channel-mutant mouse models, we dissect the distinct roles of HCN1 and HCN4 in this process. MethodsWe employed patch-clamp electrophysiology to determine phase response curves (PRCs) to predict the ability of single cells to interact in the SAN. Using computer simulations, the behaviour of the SAN at the network level was determined. ResultsWe found that HCN1, but not HCN4, is essential for the fast synchronisation of pacemaker cells in the SAN and propose a mechanism by which HCN1 channels regulate this process. ConclusionThese findings highlight HCN1 as a critical component for ensuring the precise and rapid coordination needed for synchronisation of the SAN, for a regular and consistent heartbeat. Translational perspectiveOur work provides essential insights into the cellular and molecular mechanisms governing SAN function and lays the groundwork for several clinically relevant applications. Understanding how If blockers may affect heart rate and rhythm stability is crucial for assessing potential side effects of current and future subtype-specific HCN channel inhibitors. Moreover, our findings could support new diagnostic strategies for detecting patients at risk of SAN dysfunction. Ultimately, these findings pave the way for innovative therapeutic approaches, including targeted channel modulation and future cell or gene therapies to restore pacemaker stability.

physiology↗

bioRxiv2025

Neuropeptidergic transmission shapes emergent properties of prefrontal cortical circuits underlying learning

The prefrontal cortex (PFC) is essential for top-down control of affect and its dysfunction is implicated in many psychiatric disorders. Inhibitory interneurons expressing somatostatin have been implicated in cognition, affect, and disease. However, somatostatins function as a neuropeptide transmitter remains unclear. Here, we investigated the contribution of somatostatin neurotransmission in differentiating between salient (rewarding or aversive) and neutral outcomes. Monitoring somatostatin release and somatostatin receptor antagonism revealed time-dependent regulation of outcome-specific associative learning during acquisition. We found that somatostatin transmission enables configural representations incorporating a salient, aversive outcome in prefrontal cortical neurons, and a threat-driven shift in population-level network activity in-vivo. These findings show a novel role for somatostatin, an interneuron "cellular marker", signaling in shaping learning and emergent network dynamics. Further this framework revealed that reduced somatostatin neuropeptidergic transmission may impair top-down control of affective behaviors observed in mental health disorders, suggesting potential new avenues for therapeutic intervention.

neuroscience↗

bioRxiv2024

Contribution of amygdala to dynamic model arbitration under uncertainty

Intrinsic uncertainty in the reward environment requires the brain to run multiple models simultaneously to predict outcomes based on preceding cues or actions, commonly referred to as stimulus- and action-based learning. Ultimately, the brain also must adopt appropriate choice behavior using reliability of these models. Here, we combined multiple experimental and computational approaches to quantify concurrent learning in monkeys performing tasks with different levels of uncertainty about the model of the environment. By comparing behavior in control monkeys and monkeys with bilateral lesions to the amygdala or ventral striatum, we found evidence for dynamic, competitive interaction between stimulus-based and action-based learning, and for a distinct role of the amygdala. Specifically, we demonstrate that the amygdala adjusts the initial balance between the two learning systems, thereby altering the interaction between arbitration and learning that shapes the time course of both learning and choice behaviors. This novel role of the amygdala can account for existing contradictory observations and provides testable predictions for future studies into circuit-level mechanisms of flexible learning and choice under uncertainty.

neuroscience↗

bioRxiv2024

Neural dynamics of reversal learningin the prefrontal cortex and recurrent neural networks

In probabilistic reversal learning, the choice option yielding reward with higher probability switches at a random trial. To perform optimally in this task, one has to accumulate evidence across trials to infer the probability that a reversal has occurred. We investigated how this reversal probability is represented in cortical neurons by analyzing the neural activity in the prefrontal cortex of monkeys and recurrent neural networks trained on the task. We found that in a neural subspace encoding reversal probability, its activity represented integration of reward outcomes as in a line attractor model. The reversal probability activity at the start of a trial was stationary, stable and consistent with the attractor dynamics. However, during the trial, the activity was associated with task-related behavior and became non-stationary, thus deviating from the line attractor. Fitting a predictive model to neural data showed that the stationary state at the trial start serves as an initial condition for launching the non-stationary activity. This suggested an extension of the line attractor model with behavior-induced non-stationary dynamics. The non-stationary trajectories were separable indicating that they can represent distinct probabilistic values. Perturbing the reversal probability activity in the recurrent neural networks biased choice outcomes demonstrating its functional significance. In sum, our results show that cortical networks encode reversal probability in stable stationary state at the start of a trial and utilize it to initiate non-stationary dynamics that accommodates task-related behavior while maintaining the reversal information.

neuroscience↗

bioRxiv2023

Computational mechanisms underlying motivation to earn symbolic reinforcers

Reinforcement learning (RL) is a theoretical framework that describes how agents learn to select options that maximize rewards and minimize punishments over time. We often make choices, however, to obtain symbolic reinforcers (e.g. money, points) that can later be exchanged for primary reinforcers (e.g. food, drink). Although symbolic reinforcers are motivating, little is understood about the neural or computational mechanisms underlying the motivation to earn them. In the present study, we examined how monkeys learn to make choices that maximize fluid rewards through reinforcement with tokens. The question addressed here is how the value of a state, which is a function of multiple task features (e.g. current number of accumulated tokens, choice options, task epoch, trials since last delivery of primary reinforcer, etc.), drives value and affects motivation. We constructed a Markov decision process model that computes the value of task states given task features to capture the motivational state of the animal. Fixation times, choice reaction times, and abort frequency were all significantly related to values of task states during the tokens task (n=5 monkeys). Furthermore, the model makes predictions for how neural responses could change on a moment-by-moment basis relative to changes in state value. Together, this task and model allow us to capture learning and behavior related to symbolic reinforcement. Significance statementSymbolic reinforcers, like money and points, play a critical role in our lives. Like rewards, symbolic reinforcers can be motivating and can even lead to compulsive behaviors like gambling addiction. However, we lack an understanding of how symbolic reinforcement can drive fluctuations in motivation. Here we investigated the effect of symbolic reinforcers on behaviors related to motivation during a token reinforcement learning task, using a novel reinforcement learning model and data from five monkeys. Our findings suggest that the value of a task state can affect willingness to initiate a trial, speed to choose, and persistence to complete a trial. Our model makes testable predictions for within trial fluctuations of neural activity related to values of task states.

neuroscience↗

bioRxiv2023

Attractor dynamics reflect decision confidence in macaque prefrontal cortex

Decisions are made with different degrees of consistency, and this consistency can be linked to the confidence that the best choice has been made. Theoretical work suggests that attractor dynamics in networks can account for choice consistency, but how this is implemented in the brain remains unclear. Here, we provide evidence that the energy landscape around attractor basins in population neural activity in prefrontal cortex reflects choice consistency. We trained two rhesus monkeys to make accept/reject decisions based on pretrained visual cues that signaled reward offers with different magnitudes and delays-to-reward. Monkeys made consistent decisions for very good and very bad offers, but decisions were less consistent for intermediate offers. Analysis of neural data showed that the attractor basins around patterns of activity reflecting decisions had steeper landscapes for offers that led to consistent decisions. Therefore, we provide neural evidence that energy landscapes predict decision consistency, which reflects decision confidence.

neuroscience↗

bioRxiv2020

Noradrenergic regulation of Win-Stay/Lose-Shift policy and choice determinism in a two-armed bandit task

Reversal learning depends on cognitive flexibility. Many reversal learning studies assess cognitive flexibility based on the number of reversals that occur over a test session. Reversals occur when an option is repeatedly chosen, e.g. eight times in a row. This design feature encourages win-stay behavior and thus makes it difficult to understand how win-stay decisions influence reversal performance. We used an alternative design, reversals over blocks of trials independent of performance, to study how perturbations of the medial orbital cortex and the noradrenergic system influence reversal learning. We found that choice accuracy varies independently of win-stay behavior and the noradrenergic system controls sensitivity to positive feedback during reversal learning.

neuroscience↗

bioRxiv2020

A latent variable approach to decoding neural population activity

Decoding is a powerful approach for measuring the information contained in the activity of neural populations. As a result, decoding analyses are now used across a wide range of model organisms and experimental paradigms. However, typical analyses employ general purpose decoding algorithms that do not explicitly take advantage of the structure of neural variability, which is often low-dimensional and can thus be effectively characterized using latent variables. Here we propose a new decoding framework that exploits the low-dimensional structure of neural population variability by removing correlated variability that is unrelated to the decoded variable, then decoding the resulting denoised activity. We demonstrate the efficacy of this framework using simulated data, where the true upper bounds for decoding performance are known. A linear version of our decoder provides an estimator for the decoded variable that can be more efficient than other commonly used linear estimators such as linear discriminant analysis. In addition, our proposed decoding framework admits a simple extension to nonlinear decoding that compares favorably to standard feed-forward neural networks. By explicitly modeling shared population variability, the success of the resulting linear and nonlinear decoders also offers a new perspective on the relationship between shared variability and information contained in large neural populations.

neuroscience↗

bioRxiv2019

Learning to Select Actions Shapes Recurrent Dynamics in the Corticostriatal System

Learning to select appropriate actions based on their values is fundamental to adaptive behavior. This form of learning is supported by fronto-striatal systems. The dorsal-lateral prefrontal cortex (dlPFC) and the dorsal striatum (dSTR), which are strongly interconnected, are key nodes in this circuitry. Substantial experimental evidence, including neurophysiological recordings, have shown that neurons in these structures represent key aspects of learning. The computational mechanisms that shape the neurophysiological responses, however, are not clear. To examine this, we developed a recurrent neural network (RNN) model of the dlPFC-dSTR circuit and trained it on an oculomotor sequence learning task. We compared the activity generated by the model to activity recorded from monkey dlPFC and dSTR in the same task. This network consisted of a striatal component which encoded action values, and a prefrontal component which selected appropriate actions. After training, this system was able to autonomously represent and update action values and select actions, thus being able to closely approximate the representational structure in corticostriatal recordings. We found that learning to select the correct actions drove action-sequence representations further apart in activity space, both in the model and in the neural data. The model revealed that learning proceeds by increasing the distance between sequence-specific representations. This makes it more likely that the model will select the appropriate action sequence as learning develops. Our model thus supports the hypothesis that learning in networks drives the neural representations of actions further apart, increasing the probability that the network generates correct actions as learning proceeds. Altogether, this study advances our understanding of how neural circuit dynamics are involved in neural computation, showing how dynamics in the corticostriatal system support task learning.

neuroscience↗

bioRxiv2018

A comparison of auditory oddball responses in dorsolateral prefrontal cortex, basolateral amygdala and auditory cortex of macaque

The mismatch negativity (MMN) is an event-related potential component seen in response to unexpected \"novel\" stimuli, such as in an auditory oddball task. The MMN is of wide interest and application, but the neural responses that generate it are poorly understood. This is in part due to differences in design and focus between animal and human oddball paradigms. For example, one of the main explanatory models, the \"predictive error hypothesis\", posits differences in timing and selectivity between signals carried in auditory and prefrontal cortex. However these predictions have not been fully tested because 1) noninvasive techniques used in humans lack the combined spatial and temporal precision necessary for these comparisons, and 2) single-neuron studies in animal models, which combine spatial and temporal precision, have not focused on higher order contributions to novelty signals. In addition, accounts of the MMN traditionally do not address contributions from subcortical areas known to be involved in novelty detection, such as the amygdala. To better constrain hypotheses and to address methodological gaps between human and animal studies, we recorded single neuron activity from the auditory cortex, dorsolateral prefrontal cortex, and basolateral amygdala of two macaque monkeys during an auditory oddball paradigm modeled after that used in humans. Consistent with predictions of the predictive error hypothesis, novelty signals in prefrontal cortex were generally later than in auditory cortex, and were abstracted from stimulus-specific effects seen in auditory cortex. However, we found signals in amygdala that were comparable in magnitude and timing to those in prefrontal cortex, and both prefrontal and amygdala signals were generally much weaker than those in auditory cortex. These observations place useful quantitative constraints on putative generators of the auditory oddball-based MMN, and additionally indicate that there are subcortical areas, such as the amygdala, that may be involved in novelty detection in an auditory oddball paradigm.

neuroscience↗