Current Opinion in Neurobiology最新文献

Learning and execution of complex motor skills are often modulated by sensory feedback and contextual cues arriving across multiple sensory modalities. Vocal motor behaviors, in particular, are primarily influenced by auditory inputs, both during learning and mature vocal production. The importance of auditory input in shaping vocal output has been investigated in several songbird species that acquire their adult song based on auditory exposure to a tutor during development. Recent studies have highlighted the influences of stimuli arriving through other sensory channels in juvenile song learning and in adult song production. Here, we review changes induced by diverse sensory stimuli during the song learning process and the production of adult song, considering the neuroethological significance of sensory channels in different species of songbirds. Additionally, we highlight advances, open questions, and possible future approaches for understanding the neural circuits that enable the multimodal shaping of singing behavior.

Relapse to drug use during abstinence is a defining feature of addiction. To date, however, results from studies using rat relapse/reinstatement models have yet to result in FDA-approved medications for relapse prevention. To address this translational gap, we and others have developed rat models of relapse after voluntary abstinence from drug self-administration. One of these models is the electric barrier conflict model. Here, we introduce the model, and then review studies on behavioral and neuropharmacological mechanisms of cue-induced relapse and incubation of drug seeking (time-dependent increase in drug seeking during abstinence) after electric barrier-induced abstinence. We also briefly discuss future directions and potential clinical implications. One major conclusion of our review is that the brain mechanisms controlling drug relapse after electrical barrier-induced voluntary abstinence are likely distinct from those controlling relapse after homecage forced abstinence.

The concept of ‘prion-like’ behavior has emerged in the study of diseases involving protein misfolding where fibrillar structures, called amyloids, self-propagate and induce disease in a fashion similar to prions. From a biological standpoint, in order to be considered ‘prion-like,’ a protein must traverse cells and tissues and further propagate via a templated conformational change. Since 2017, cryo-electron microscopy structures from patient-derived ‘prion-like’ amyloids, in particular tau, have been presented and revealed structural similarities shared across amyloids. Since 2021, cryo-EM structures from prions of known infectivity have been added to the ex vivo amyloid structure family. In this review, we discuss current proposals for the ‘prion-like’ mechanisms of spread for tau and prion protein as well as discuss different influencers on structures of aggregates from tauopathies and prion diseases. Lastly, we discuss some of the current hypotheses for what may distinguish structures that are ‘prion-like’ from transmissible prion structures.

The entorhinal cortex and hippocampus form a recurrent network that informs many cognitive processes, including memory, planning, navigation, and imagination. Neural recordings from these regions reveal spatially organized population codes corresponding to external environments and abstract spaces. Aligning the former cognitive functionalities with the latter neural phenomena is a central challenge in understanding the entorhinal-hippocampal circuit (EHC). Disparate experiments demonstrate a surprising level of complexity and apparent disorder in the intricate spatiotemporal dynamics of sequential non-local hippocampal reactivations, which occur particularly, though not exclusively, during immobile pauses and rest. We review these phenomena with a particular focus on their apparent lack of physical simulative realism. These observations are then integrated within a theoretical framework and proposed neural circuit mechanisms that normatively characterize this neural complexity by conceiving different regimes of hippocampal microdynamics as neuromarkers of diverse cognitive computations.

The risk of depression is influenced by both genetic and environmental factors. It has been suggested that epigenetic mechanisms may mediate the risk of depression following exposure to adverse life events. Epigenetics encompasses stable alterations in gene expression that are controlled through transcriptional, post-transcriptional, translational, or post-translational processes, including DNA modifications, chromatin remodeling, histone modifications, RNA modifications, and non-coding RNA (ncRNA) regulation, without any changes in the DNA sequence. In this review, we explore recent research advancements in the realm of epigenetics concerning depression. Furthermore, we evaluate the potential of epigenetic changes as diagnostic and therapeutic biomarkers for depression.

The brain is a remarkably capable and efficient system. It can process and store huge amounts of noisy and unstructured information, using minimal energy. In contrast, current artificial intelligence (AI) systems require vast resources for training while still struggling to compete in tasks that are trivial for biological agents. Thus, brain-inspired engineering has emerged as a promising new avenue for designing sustainable, next-generation AI systems. Here, we describe how dendritic mechanisms of biological neurons have inspired innovative solutions for significant AI problems, including credit assignment in multi-layer networks, catastrophic forgetting, and high-power consumption. These findings provide exciting alternatives to existing architectures, showing how dendritic research can pave the way for building more powerful and energy efficient artificial learning systems.

The nervous system evolved to enable navigation throughout the environment in the pursuit of resources. Evolutionarily newer structures allowed increasingly complex adaptations but necessarily added redundancy. A dominant view of movement neuroscientists is that there is a one-to-one mapping between brain region and function. However, recent experimental data is hard to reconcile with the most conservative interpretation of this framework, suggesting a degree of functional redundancy during the performance of well-learned, constrained behaviors. This apparent redundancy likely stems from the bidirectional interactions between the various cortical and subcortical structures involved in motor control. We posit that these bidirectional connections enable flexible interactions across structures that change depending upon behavioral demands, such as during acquisition, execution or adaptation of a skill. Observing the system across both multiple actions and behavioral timescales can help isolate the functional contributions of individual structures, leading to an integrated understanding of the neural control of movement.

Data-driven computational models of neurons, synapses, microcircuits, and mesocircuits have become essential tools in modern brain research. The goal of these multiscale models is to integrate and synthesize information from different levels of brain organization, from cellular properties, dendritic excitability, and synaptic dynamics to microcircuits, mesocircuits, and ultimately behavior. This article surveys recent advances in the genesis of data-driven computational models of mammalian neural networks in cortical and subcortical areas. I discuss the challenges and opportunities in developing data-driven multiscale models, including the need for interdisciplinary collaborations, the importance of model validation and comparison, and the potential impact on basic and translational neuroscience research. Finally, I highlight future directions and emerging technologies that will enable more comprehensive and predictive data-driven models of brain function and dysfunction.

Glial cells have been shown to be vital for various brain functions, including homeostasis, information processing, and cognition. Over the past 30 years, various signaling interactions between neuronal and glial cells have been shown to underlie these functions. This review summarizes the interactions, particularly between neurons and astrocytes, which are types of glial cells. Some of the interactions remain controversial in part due to the nature of experimental methods and preparations used. Based on the accumulated data, computational models of the neuron–astrocyte interactions have been developed to explain the complex functions of astrocytes in neural circuits and to test conflicting hypotheses. This review presents the most significant recent models, modeling methods and simulation tools for neuron–astrocyte interactions. In the future, we will especially need more experimental research on awake animals in vivo and new computational models of neuron–glia interactions to advance our understanding of cellular dynamics and the functioning of neural circuits in different brain regions.

Striatal dopamine governs a wide range of behavioral functions, yet local dopamine concentrations can be dissociated from somatic activity. Here, we discuss how dopamine's diverse roles in behavior may be driven by local circuit mechanisms shaping dopamine release. We first look at historical and recent work demonstrating that striatal circuits interact with dopaminergic terminals to either initiate the release of dopamine or modulate the release of dopamine initiated by spiking in midbrain dopamine neurons, with particular attention to GABAergic and cholinergic local circuit mechanisms. Then we discuss some of the first in vivo studies of acetylcholine-dopamine interactions in striatum and broadly discuss necessary future work in understanding the roles of midbrain versus striatal dopamine regulation.