Excess phosphoserine-129 α-synuclein induces synaptic vesicle trafficking and declustering defects at a vertebrate synapse.

α-Synuclein is a presynaptic protein that regulates synaptic vesicle (SV) trafficking. In Parkinson's disease (PD) and dementia with Lewy bodies (DLB), α-synuclein aberrantly accumulates throughout neurons, including at synapses. During neuronal activity, α-synuclein is reversibly phosphorylated at serine 129 (pS129). While pS129 comprises ∼4% of total α-synuclein under physiological conditions, it dramatically increases in PD and DLB brains. The impacts of excess pS129 on synaptic function are currently unknown. We show here that compared with wild-type (WT) α-synuclein, pS129 exhibits increased binding and oligomerization on synaptic membranes and enhanced vesicle "microclustering" in vitro. Moreover, when acutely injected into lamprey reticulospinal axons, excess pS129 α-synuclein robustly localized to synapses and disrupted SV trafficking in an activity-dependent manner, as assessed by ultrastructural analysis. Specifically, pS129 caused a declustering and dispersion of SVs away from the synaptic vicinity, leading to a significant loss of total synaptic membrane. Live imaging further revealed altered SV cycling, as well as microclusters of recently endocytosed SVs moving away from synapses. Thus, excess pS129 caused an activity-dependent inhibition of SV trafficking via altered vesicle clustering/reclustering. This work suggests that accumulation of pS129 at synapses in diseases like PD and DLB could have profound effects on SV dynamics.

Synaptic Function and Dysfunction in Lysosomal Storage Diseases.

Lysosomal storage diseases (LSDs) with neurological involvement are inherited genetic diseases of the metabolism characterized by lysosomal dysfunction and the accumulation of undegraded substrates altering glial and neuronal function. Often, patients with neurological manifestations present with damage to the gray and white matter and irreversible neuronal decline. The use of animal models of LSDs has greatly facilitated studying and identifying potential mechanisms of neuronal dysfunction, including alterations in availability and function of synaptic proteins, modifications of membrane structure, deficits in docking, exocytosis, recycling of synaptic vesicles, and inflammation-mediated remodeling of synapses. Although some extrapolations from findings in adult-onset conditions such as Alzheimer's disease or Parkinson's disease have been reported, the pathogenetic mechanisms underpinning cognitive deficits in LSDs are still largely unclear. Without being fully inclusive, the goal of this mini-review is to present a discussion on possible mechanisms leading to synaptic dysfunction in LSDs.

Sensory Activation of Command Cells for Locomotion and Modulatory Mechanisms: Lessons from Lampreys.

Sensorimotor transformation is one of the most fundamental and ubiquitous functions of the central nervous system (CNS). Although the general organization of the locomotor neural circuitry is relatively well understood, less is known about its activation by sensory inputs and its modulation. Utilizing the lamprey model, a detailed understanding of sensorimotor integration in vertebrates is emerging. In this article, we explore how the vertebrate CNS integrates sensory signals to generate motor behavior by examining the pathways and neural mechanisms involved in the transformation of cutaneous and olfactory inputs into motor output in the lamprey. We then review how 5-hydroxytryptamine (5-HT) acts on these systems by modulating both sensory inputs and motor output. A comprehensive review of this fundamental topic should provide a useful framework in the fields of motor control, sensorimotor integration and neuromodulation.

Computational analysis in vitro: dynamics and plasticity of a neuro-robotic system.

When the brain interacts with the environment it constantly adapts by representing the environment in a form that is called an internal model. The neurobiological basis for internal models is provided by the connectivity and the dynamical properties of neurons. Thus, the interactions between neural tissues and external devices provide a fundamental means for investigating the connectivity and dynamical properties of neural populations. We developed this idea, suggested in the 1980s by Valentino Braitenberg, for investigating and representing the dynamical behavior of neuronal populations in the brainstem of the lamprey. The brainstem was maintained in vitro and connected in a closed loop with two types of artificial device: (a) a simulated dynamical system and (b) a small mobile robot. In both cases, the device was controlled by recorded extracellular signals and its output was translated into electrical stimuli delivered to the neural system. The goal of the first study was to estimate the dynamical dimension of neural preparation in a single-input/single-output configuration. The dynamical dimension is the number of state variables that together with the applied input determine the output of a system. The results indicate that while this neural system has significant dynamical properties, its effective complexity, as established by the dynamical dimension, is rather moderate. In the second study, we considered a more specific situation, in which the same portion of the nervous system controls a robotic device in a two-input/two-output configuration. We fitted the input-output data from the neuro-robotic preparation to neural network models having different internal dynamics and we observed the generalization error of each model. Consistent with the first study, this second experiment showed that a simple recurrent dynamical model was able to capture the behavior of the hybrid system. This experimental and computational framework provides the means for investigating neural plasticity and internal representations in the context of brain-machine interfaces.

A synaptic mechanism for network synchrony.

Within neural networks, synchronization of activity is dependent upon the synaptic connectivity of embedded microcircuits and the intrinsic membrane properties of their constituent neurons. Synaptic integration, dendritic Ca(2+) signaling, and non-linear interactions are crucial cellular attributes that dictate single neuron computation, but their roles promoting synchrony and the generation of network oscillations are not well understood, especially within the context of a defined behavior. In this regard, the lamprey spinal central pattern generator (CPG) stands out as a well-characterized, conserved vertebrate model of a neural network (Smith et al., 2013a), which produces synchronized oscillations in which neural elements from the systems to cellular level that control rhythmic locomotion have been determined. We review the current evidence for the synaptic basis of oscillation generation with a particular emphasis on the linkage between synaptic communication and its cellular coupling to membrane processes that control oscillatory behavior of neurons within the locomotor network. We seek to relate dendritic function found in many vertebrate systems to the accessible lamprey central nervous system in which the relationship between neural network activity and behavior is well understood. This enables us to address how Ca(2+) signaling in spinal neuron dendrites orchestrate oscillations that drive network behavior.

New Perspectives on the Dialogue between Brains and Machines.

Brain-machine interfaces (BMIs) are mostly investigated as a means to provide paralyzed people with new communication channels with the external world. However, the communication between brain and artificial devices also offers a unique opportunity to study the dynamical properties of neural systems. This review focuses on bidirectional interfaces, which operate in two ways by translating neural signals into input commands for the device and the output of the device into neural stimuli. We discuss how bidirectional BMIs help investigating neural information processing and how neural dynamics may participate in the control of external devices. In this respect, a bidirectional BMI can be regarded as a fancy combination of neural recording and stimulation apparatus, connected via an artificial body. The artificial body can be designed in virtually infinite ways in order to observe different aspects of neural dynamics and to approximate desired control policies.

Brain-machine interactions for assessing the dynamics of neural systems.

A critical advance for brain-machine interfaces is the establishment of bi-directional communications between the nervous system and external devices. However, the signals generated by a population of neurons are expected to depend in a complex way upon poorly understood neural dynamics. We report a new technique for the identification of the dynamics of a neural population engaged in a bi-directional interaction with an external device. We placed in vitro preparations from the lamprey brainstem in a closed-loop interaction with simulated dynamical devices having different numbers of degrees of freedom. We used the observed behaviors of this composite system to assess how many independent parameters - or state variables - determine at each instant the output of the neural system. This information, known as the dynamical dimension of a system, allows predicting future behaviors based on the present state and the future inputs. A relevant novelty in this approach is the possibility to assess a computational property - the dynamical dimension of a neuronal population - through a simple experimental technique based on the bi-directional interaction with simulated dynamical devices. We present a set of results that demonstrate the possibility of obtaining stable and reliable measures of the dynamical dimension of a neural preparation.