As social beings, humans make decisions partly based on social interaction. Observing the behavior of others can lead to learning from and about them, potentially increasing trust and prompting trust-based behavioral changes. Observation-based decision making involves different neural structures. The orbitofrontal cortex (OFC) and lateral prefrontal cortex (LPFC) are known as neural structures mainly involved in processing emotional and cognitive decision values, respectively, while the anterior cingulate cortex (ACC) plays a pivotal role as a social hub, integrating the afferent expectancy signals from the OFC and LPFC. This paper presents a neurocomputational model of the interplay between observational learning and trust, as well as their role in individual decision making. Hence, our model provides a framework for investigating how emotional and rational responses may change when individuals observe the action–outcome associations of an alleged expert. We have modeled the neurodynamics of three cortical structures (OFC, LPFC, and ACC) and their interactions, where the neural oscillatory properties, modeled with Dynamic Bayesian Probability, represent the observer’s attitude towards the expert and the decision options. As an example of an everyday behavioral situation related to climate change, we use the choice of transportation between home and work. The model generates EEG-like signals that show how patterns of neural activity change during observation-based decision making. The simulations suggest that higher levels of trust influence both emotional and rational evaluations when individuals observe the actions and outcomes of an expert. Overall, the proposed framework provides insight into how observational learning and trust work together to shape decision making. It highlights the dynamic interplay between emotional and cognitive processes and offers a mechanistic understanding of how social information can influence behavior.

Background: Beta-band (13–30 Hz) oscillations in the cortico-basal ganglia-thalamic (CBT) network strongly correlate with motor deficits in Parkinson’s disease (PD), yet their synaptic origins remain unclear. Given that dopamine (DA) loss is necessary but not sufficient to produce sustained beta rhythms, we hypothesised that corticostriatal glutamatergic overdrive may function as a significant non-dopaminergic amplifier of pathological synchrony. Methods: Using an integrated experimental-computational approach, we combined 6-hydroxydopamine (6-OHDA) male rat models, ex vivo striatal patch-clamp recordings, chemogenetic modulation of corticostriatal projection, and multiscale computational network modelling to examine beta oscillation dynamics in the CBT network. Findings: Early DA denervation caused akinesia without beta elevation, while advanced degeneration triggered robust high-beta (25–40 Hz) oscillations and increased corticostriatal coherence. Ex vivo, medium spiny neurons (MSNs) exhibited heightened presynaptic glutamate release correlated with beta power. Computational modelling showed that excessive corticostriatal input under DA depletion increased MSN synchrony, disrupted striatal decorrelation, and was associated with the emergence of pathological beta rhythms, effects reversed by reducing glutamatergic input. In vivo chemogenetic silencing of corticostriatal projections suppressed beta synchrony and improved motor performance in 6-OHDA rats, whereas activation in DA-intact rats had no effect. Notably, striatal NMDA, not AMPA, receptor blockade reduced beta oscillations and motor deficits. Network simulations implicated the subthalamic → motor cortex feedback loop in the maintenance of this pathological beta state. Interpretation: Corticostriatal glutamatergic overdrive, through NMDA receptor-dependent signalling, is linked to the amplification and propagation of beta synchronisation across the CBT circuit, highlighting it as a potential biomarker and a promising therapeutic target in PD. Funding: This research was supported by the National Natural Science Foundation of China (32271173, 82371256) and the Natural Science Foundation of Beijing Municipality (7242214, 7252213). This study was also supported by the Swedish Research Council (VR-M-2020-01652), the Swedish e-Science Research Centre (SeRC), Science for Life Laboratory, KTH Digital Future, EU/Horizon 2020 No. 945539 (HBP 935 SGA3) and No. 101147319 (EBRAINS 2.0 Project), the European Union’s Research and Innovation Program Horizon Europe under grant agreement No. 101137289(the Virtual Brain Twin Project).

The striatum plays a central role in action selection and reinforcement learning, integrating cortical inputs with dopaminergic signals encoding reward prediction errors. While dopamine modulates synaptic plasticity underlying value learning, the mechanisms that enable selective reinforcement of behaviorally relevant stimulus-action associations-the structural credit assignment problem-remain poorly understood, especially in environments with multiple competing stimuli and actions. Here, we present a computational model in which acetylcholine (ACh), released by striatal cholinergic interneurons, acts as a channel-specific gating signal that restricts plasticity to brief temporal windows following action execution. The model implements a biologically plausible three-factor learning rule requiring presynaptic activity, postsynaptic depolarization, and phasic dopamine, with plasticity gated by cholinergic pauses that temporally align with behaviorally relevant events. This mechanism ensures that only synapses involved in the selected behavior are eligible for modification. Through systematic evaluation across tasks with distractors and contingency reversals, we show that ACh-gated learning promotes synaptic specificity, suppresses cross-channel interference, and yields increasingly competitive performance relative to Q-learning in complex tasks, reflecting the scalability of the proposed learning mechanism. Moreover, the model reveals distinct roles for striatal pathways: direct pathway (D1) neurons maintain stimulus-specific responses, while indirect pathway (D2) neurons are progressively recruited to suppress outdated associations during policy adaptation. These findings provide a mechanistic account of how coordinated cholinergic and dopaminergic signaling can support scalable and efficient reinforcement learning in the striatum, consistent with experimental observations of pathway-specific plasticity.

This study investigates the computational potential of single striatal projection neurons (SPNs), emphasizing dendritic nonlinearities and their crucial role in solving complex integration problems. Utilizing a biophysically detailed multicompartmental model of an SPN, we introduce a calcium-based, local synaptic learning rule dependent on dendritic plateau potentials. According to what is known about excitatory corticostriatal synapses, the learning rule is governed by local calcium dynamics from NMDA and L-type calcium channels and dopaminergic reward signals. In order to devise a self-adjusting learning rule, which ensures stability for individual synaptic weights, metaplasticity is also used. We demonstrate that this rule allows single neurons with sufficiently nonlinear dendrites to solve the nonlinear feature binding problem, a task traditionally attributed to neuronal networks. We also detail an inhibitory plasticity mechanism that contributes to dendritic compartmentalization, further enhancing computational efficiency in dendrites. This in silico study highlights the computational potential of single neurons, providing deeper insights into neuronal information processing and the mechanisms by which the brain executes complex computations.

GABAB receptors (GABABRs) are an important building block in neural activity. Despite their widely hypothesized role in many basic neuronal functions and mental disorder symptomatology, there is a lack of biophysically and biochemically detailed models of these receptors and the way they mediate neuronal inhibition. Here, we developed a computational model for the activation of GABABRs and its effects on the activation of G protein-coupled inwardly rectifying potassium (GIRK) channels as well as inhibition of voltage-gated Ca2+ channels. To ensure the generality of our modeling framework, we fit our model to electrophysiological data including patch-clamp and intracellular recordings that described both pre-and postsynaptic effects of the receptor activation. We validated our model using data on postsynaptic effects of GABABRs on layer V pyramidal cell firing activity ex vivo and in vivo and confirmed the strong impact of dendritic GIRK channel activation on the neuron output. Finally, we reproduced and dissected the effects of a knockout of RGS7 (a G protein signaling protein) on CA1 pyramidal cell electrophysiological properties, which shows the potential of our model in generating insights on genetic manipulations of the GABABR system and related genetic variants. Our model thus provides a flexible tool for biochemically and biophysically detailed simulations of different aspects of GABABR activation that can reveal both foundational principles of neuronal dynamics and brain disorder-associated traits and treatment options.

The substantia nigra pars reticulata (SNr) is a primary output for basal ganglia signaling. It plays an important role in the control of movement, integrating inputs from upstream structures in the basal ganglia, before sending organized projections to a range of targets in the midbrain, brainstem, and thalamus. Here, we present a detailed in silico model of the mouse SNr, including its major afferent inputs. The electrophysiological and morphological properties of SNr neurons are characterized in acute brain slices via whole cell patch-clamp recordings and morphological reconstruction. Using reconstructed morphologies, multicompartmental models of single neurons are instantiated within the NEURON simulation environment and populated with relevant modeled ion channels. Model parameters are optimized via an evolutionary algorithm, such that simulated neurons faithfully reproduce recorded electrophysiological behavior. Using the simulation infrastructure software Snudda, single neuron models are incorporated into a circuit-level model, where the sparse connectivity within the SNr is recreated. We simulate the mouse SNr at scale, featuring realistic volumes and neuronal density. The unique synaptic properties and activity patterns of different afferent sources are captured in silico. Born out of ex vivo data, our model reproduces in vivo firing patterns. Our simulations suggest that paradoxical activity increases in response to experimental inhibition can be explained by lateral connectivity. In addition, our model predicts the functional implications of characteristic short-term synaptic plasticity in the indirect pathway of the basal ganglia. The model can be extended to include additional inputs and be connected with existing models of upstream basal ganglia nuclei to further explore circuit dynamics.

Volition is conceived as a set of orchestrated executive functions, which can be characterized by features, such as reason-based and goal-directedness, driven by endogenous signals. The lateral prefrontal cortex (LPFC) has long been considered to be responsible for cognitive control and executive function, and its neurodynamics appears to be central to goal-directed cognition. In order to address both associative processes (i.e. reason-action and action-outcome) based on internal stimuli, it seems essential to consider the interconnectivity of LPFC and the anterior cingulate cortex (ACC). The critical placement of ACC as a hub mediates projection of afferent expectancy signals directly from brain structures associated with emotion, as well as internal signals from subcortical areas to the LPFC. Apparently, the two cortical areas LPFC and ACC play a pivotal role in the formation of voluntary behaviors. In this paper, we model the neurodynamics of these two neural structures and their interactions related to intentional control. We predict that the emergence of intention is the result of both feedback-based and competitive mechanisms among neural attractors. These mechanisms alter the dimensionalities of coexisting chaotic attractors to more stable, low dimensional manifolds as limit cycle attractors, which may result in the onset of a readiness potential (RP) in SMA, associated with a decision to act.