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maintain stimulus-specific responses, while indirect pathway (D2) neurons are progressively recruited to suppress outdated associations during policy adaptation.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.

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.

This study explores the computational potential of single striatal projection neurons (SPN), 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 that leverages 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 addition, we incorporated metaplasticity in order to devise a self-adjusting learning rule which ensures stability for individual synaptic weights. We demonstrate that this rule allows single neurons to solve the nonlinear feature binding problem (NFBP), a task traditionally attributed to neuronal networks. We also detail an inhibitory plasticity mechanism, critical for dendritic compartmentalization, further enhancing computational efficiency in dendrites. This in silico study underscores the computational capacity of individual neurons, extending our understanding of neuronal processing and the brain’s ability to perform complex computations.

In recent years, brain research has indisputably entered a new epoch, driven by substantial methodological advances and digitally enabled data integration and modelling at multiple scales—from molecules to the whole brain. Major advances are emerging at the intersection of neuroscience with technology and computing. This new science of the brain combines high-quality research, data integration across multiple scales, a new culture of multidisciplinary large-scale collaboration, and translation into applications. As pioneered in Europe’s Human Brain Project (HBP), a systematic approach will be essential for meeting the coming decade’s pressing medical and technological challenges. The aims of this paper are to: develop a concept for the coming decade of digital brain research, discuss this new concept with the research community at large, identify points of convergence, and derive therefrom scientific common goals; provide a scientific framework for the current and future development of EBRAINS, a research infrastructure resulting from the HBP’s work; inform and engage stakeholders, funding organisations and research institutions regarding future digital brain research; identify and address the transformational potential of comprehensive brain models for artificial intelligence, including machine learning and deep learning; outline a collaborative approach that integrates reflection, dialogues, and societal engagement on ethical and societal opportunities and challenges as part of future neuroscience research.

This in silico study predicts the impact that the single-cell neuronal morphological alterations will have on the striatal microcircuit connectivity. We find that the richness in the topological striatal motifs is significantly reduced in Parkinson's disease (PD), highlighting that just measuring the pairwise connectivity between neurons gives an incomplete description of network connectivity. Moreover, we predict how the resulting electrophysiological changes of striatal projection neuron excitability together with their reduced number of dendritic branches affect their response to the glutamatergic drive from the cortex and thalamus. We find that the effective glutamatergic drive is likely significantly increased in PD, in accordance with the hyperglutamatergic hypothesis.

Biophysically detailed multi-compartment models are powerful tools to explore computational principles of the brain and also serve as a theoretical framework to generate algorithms for artificial intelligence (AI) systems. However, the expensive computational cost severely limits the applications in both the neuroscience and AI fields. The major bottleneck during simulating detailed compartment models is the ability of a simulator to solve large systems of linear equations. Here, we present a novel Dendritic Hierarchical Scheduling (DHS) method to markedly accelerate such a process. We theoretically prove that the DHS implementation is computationally optimal and accurate. This GPU-based method performs with 2-3 orders of magnitude higher speed than that of the classic serial Hines method in the conventional CPU platform. We build a DeepDendrite framework, which integrates the DHS method and the GPU computing engine of the NEURON simulator and demonstrate applications of DeepDendrite in neuroscience tasks. We investigate how spatial patterns of spine inputs affect neuronal excitability in a detailed human pyramidal neuron model with 25,000 spines. Furthermore, we provide a brief discussion on the potential of DeepDendrite for AI, specifically highlighting its ability to enable the efficient training of biophysically detailed models in typical image classification tasks.

Plateau potentials are a critical feature of neuronal excitability, but their all-or-none behavior is not easily captured in modeling. In this study, we investigated models of plateau potentials in multi-compartment neuron models and found that including glutamate spillover provides robust all-or-none behavior. This result arises due to the prolonged duration of extrasynaptic glutamate. When glutamate spillover is not included, the all-or-none behavior is very sensitive to the steepness of the Mg2+ block. These results suggest a potentially significant role of glutamate spillover in plateau potential generation, providing a mechanism for robust all-or-none behavior across a wide range of slopes of the Mg2+ block curve. We also illustrate the importance of the all-or-none plateau potential behavior for nonlinear computation with regard to the nonlinear feature binding problem.