Gene delivery to cone photoreceptors by subretinal injection of rAAV2/6 in the mouse retina.

Adeno-associated virus (AAV) has been studied as a safe delivery tool for gene therapy of retinal blinding diseases such as Leber's congenital amaurosis (LCA). The tropism of recombinant AAV (rAAV) including its specificity and efficiency in targeting retinal cell types has been studied with native or engineered capsids, along with specific promoters. However, one of the rAAV serotypes, rAAV2/6, has not been well-studied based on a report of low infection efficiency in the retina. We investigated the tropism of several rAAVs by subretinal injection in the adult mouse and found that rAAV2/6 predominantly infected cone photoreceptors including the main spectral type. Our data suggest that subretinal injection with rAAV2/6 may provide both an efficacious and specific means of gene delivery to cone photoreceptors in murine retinas.

Impact of slow K(+) currents on spike generation can be described by an adaptive threshold model.

A neuron that is stimulated by rectangular current injections initially responds with a high firing rate, followed by a decrease in the firing rate. This phenomenon is called spike-frequency adaptation and is usually mediated by slow K(+) currents, such as the M-type K(+) current (I M ) or the Ca(2+)-activated K(+) current (I AHP ). It is not clear how the detailed biophysical mechanisms regulate spike generation in a cortical neuron. In this study, we investigated the impact of slow K(+) currents on spike generation mechanism by reducing a detailed conductance-based neuron model. We showed that the detailed model can be reduced to a multi-timescale adaptive threshold model, and derived the formulae that describe the relationship between slow K(+) current parameters and reduced model parameters. Our analysis of the reduced model suggests that slow K(+) currents have a differential effect on the noise tolerance in neural coding.

Ryanodine-receptor-driven intracellular calcium dynamics underlying spatial association of synaptic plasticity.

Synaptic modifications induced at one synapse are accompanied by hetero-synaptic changes at neighboring sites. In addition, it is suggested that the mechanism of spatial association of synaptic plasticity is based on intracellular calcium signaling that is mainly regulated by two types of receptors of endoplasmic reticulum calcium store: the ryanodine receptor (RyR) and the inositol triphosphate receptor (IP3R). However, it is not clear how these types of receptors regulate intracellular calcium flux and contribute to the outcome of calcium-dependent synaptic change. To understand the relation between the synaptic association and store-regulated calcium dynamics, we focused on the function of RyR calcium regulation and simulated its behavior by using a computational neuron model. As a result, we observed that RyR-regulated calcium release depended on spike timings of pre- and postsynaptic neurons. From the induction site of calcium release, the chain activation of RyRs occurred, and spike-like calcium increase propagated along the dendrite. For calcium signaling, the propagated calcium increase did not tend to attenuate; these characteristics came from an all-or-none behavior of RyR-sensitive calcium store. Considering the role of calcium dependent synaptic plasticity, the results suggest that RyR-regulated calcium propagation induces a similar change at the synapses. However, according to the dependence of RyR calcium regulation on the model parameters, whether the chain activation of RyRs occurred, sensitively depended on spatial expression of RyR and nominal fluctuation of calcium flux. Therefore, calcium regulation of RyR helps initiate rather than relay calcium propagation.

Emergence of input selective recurrent dynamics via information transfer maximization.

Network structures of the brain have wiring patterns specialized for specific functions. These patterns are partially determined genetically or evolutionarily based on the type of task or stimulus. These wiring patterns are important in information processing; however, their organizational principles are not fully understood. This study frames the maximization of information transmission alongside the reduction of maintenance costs as a multi-objective optimization challenge, utilizing information theory and evolutionary computing algorithms with an emphasis on the visual system. The goal is to understand the underlying principles of circuit formation by exploring the patterns of wiring and information processing. The study demonstrates that efficient information transmission necessitates sparse circuits with internal modular structures featuring distinct wiring patterns. Significant trade-offs underscore the necessity of balance in wiring pattern development. The dynamics of effective circuits exhibit moderate flexibility in response to stimuli, in line with observations from prior visual system studies. Maximizing information transfer may allow for the self-organization of information processing functions similar to actual biological circuits, without being limited by modality. This study offers insights into neuroscience and the potential to improve reservoir computing performance.

Impact of network topology on inference of synaptic connectivity from multi-neuronal spike data simulated by a large-scale cortical network model.

Many mechanisms of neural processing rely critically upon the synaptic connectivity between neurons. As our ability to simultaneously record from large populations of neurons expands, the ability to infer network connectivity from this data has become a major goal of computational neuroscience. To address this issue, we employed several different methods to infer synaptic connections from simulated spike data from a realistic local cortical network model. This approach allowed us to directly compare the accuracy of different methods in predicting synaptic connectivity. We compared the performance of model-free (coherence measure and transfer entropy) and model-based (coupled escape rate model) methods of connectivity inference, applying those methods to the simulated spike data from the model networks with different network topologies. Our results indicate that the accuracy of the inferred connectivity was higher for highly clustered, near regular, or small-world networks, while accuracy was lower for random networks, irrespective of which analysis method was employed. Among the employed methods, the model-based method performed best. This model performed with higher accuracy, was less sensitive to threshold changes, and required less data to make an accurate assessment of connectivity. Given that cortical connectivity tends to be highly clustered, our results outline a powerful analytical tool for inferring local synaptic connectivity from observations of spontaneous activity.

The network configuration in Parkinsonian state compensates network activity change caused by loss of dopamine.

Parkinson's disease is a movement disorder caused by dopamine depletion in the basal ganglia. Neural activity of the subthalamic nucleus (STN) and globus pallidus externus (GPe) in the basal ganglia are closely related to motor symptoms of Parkinson's disease. However, the pathogenesis of the disease and the transition from the normal state to the pathological state have yet to be elucidated. The functional organization of the GPe is gaining attention due to the recent finding that it consists of two distinct cell populations, namely prototypic GPe neurons and arkypallidal neurons. Identifying connectivity structures between these cell populations, as well as STN neurons, in relation to the dependence of the network activity on the dopaminergic effects is vital. In the present study, using a computational model of the STN-GPe network, we explored biologically plausible connectivity structures between these cell populations. We evaluated the experimentally reported neural activities of these cell types to elucidate the effects of dopaminergic modulation and changes caused by chronic dopamine depletion, such as strengthened connections in the neural activity of the STN-GPe network. Our results indicate that the arkypallidal neurons receive cortical inputs separately from the source for prototypic and STN neurons, suggesting that arkypallidal neurons might be responsible for an additional pathway with the cortex. Furthermore, changes caused by chronic dopamine depletion compensate for the loss of dopaminergic modulation. Changes caused by dopamine depletion itself likely induce the pathological activity observed in patients with Parkinson's disease. However, such changes counteract those of firing rates caused by loss of dopaminergic modulation. In addition, we observed that the STN-GPe tends to exhibit activity with pathological characteristics as side effects.

Association between different sensory modalities based on concurrent time series data obtained by a collaborative reservoir computing model.

Humans perceive the external world by integrating information from different modalities, obtained through the sensory organs. However, the aforementioned mechanism is still unclear and has been a subject of widespread interest in the fields of psychology and brain science. A model using two reservoir computing systems, i.e., a type of recurrent neural network trained to mimic each other's output, can detect stimulus patterns that repeatedly appear in a time series signal. We applied this model for identifying specific patterns that co-occur between information from different modalities. The model was self-organized by specific fluctuation patterns that co-occurred between different modalities, and could detect each fluctuation pattern. Additionally, similarly to the case where perception is influenced by synchronous/asynchronous presentation of multimodal stimuli, the model failed to work correctly for signals that did not co-occur with corresponding fluctuation patterns. Recent experimental studies have suggested that direct interaction between different sensory systems is important for multisensory integration, in addition to top-down control from higher brain regions such as the association cortex. Because several patterns of interaction between sensory modules can be incorporated into the employed model, we were able to compare the performance between them; the original version of the employed model incorporated such an interaction as the teaching signals for learning. The performance of the original and alternative models was evaluated, and the original model was found to perform the best. Thus, we demonstrated that feedback of the outputs of appropriately learned sensory modules performed the best when compared to the other examined patterns of interaction. The proposed model incorporated information encoded by the dynamic state of the neural population and the interactions between different sensory modules, both of which were based on recent experimental observations; this allowed us to study the influence of the temporal relationship and frequency of occurrence of multisensory signals on sensory integration, as well as the nature of interaction between different sensory signals.

Mechanisms of simultaneous linear and nonlinear computations at the mammalian cone photoreceptor synapse.

Neurons enhance their computational power by combining linear and nonlinear transformations in extended dendritic trees. Rich, spatially distributed processing is rarely associated with individual synapses, but the cone photoreceptor synapse may be an exception. Graded voltages temporally modulate vesicle fusion at a cone's ~20 ribbon active zones. Transmitter then flows into a common, glia-free volume where bipolar cell dendrites are organized by type in successive tiers. Using super-resolution microscopy and tracking vesicle fusion and postsynaptic responses at the quantal level in the thirteen-lined ground squirrel, Ictidomys tridecemlineatus, we show that certain bipolar cell types respond to individual fusion events in the vesicle stream while other types respond to degrees of locally coincident events, creating a gradient across tiers that are increasingly nonlinear. Nonlinearities emerge from a combination of factors specific to each bipolar cell type including diffusion distance, contact number, receptor affinity, and proximity to glutamate transporters. Complex computations related to feature detection begin within the first visual synapse.

Influences of membrane properties on phase response curve and synchronization stability in a model globus pallidus neuron.

The activity patterns of the globus pallidus (GPe) and subthalamic nucleus (STN) are closely associated with motor function and dysfunction in the basal ganglia. In the pathological state caused by dopamine depletion, the STN-GPe network exhibits rhythmic synchronous activity accompanied by rebound bursts in the STN. Therefore, the mechanism of activity transition is a key to understand basal ganglia functions. As synchronization in GPe neurons could induce pathological STN rebound bursts, it is important to study how synchrony is generated in the GPe. To clarify this issue, we applied the phase-reduction technique to a conductance-based GPe neuronal model in order to derive the phase response curve (PRC) and interaction function between coupled GPe neurons. Using the PRC and interaction function, we studied how the steady-state activity of the GPe network depends on intrinsic membrane properties, varying ionic conductances on the membrane. We noted that a change in persistent sodium current, fast delayed rectifier Kv3 potassium current, M-type potassium current and small conductance calcium-dependent potassium current influenced the PRC shape and the steady state. The effect of those currents on the PRC shape could be attributed to extension of the firing period and reduction of the phase response immediately after an action potential. In particular, the slow potassium current arising from the M-type potassium and the SK current was responsible for the reduction of the phase response. These results suggest that the membrane property modulation controls synchronization/asynchronization in the GPe and the pathological pattern of STN-GPe activity.

Selective participation of somatodendritic HCN channels in inhibitory but not excitatory synaptic integration in neurons of the subthalamic nucleus.

The activity patterns of subthalamic nucleus (STN) neurons are intimately linked to motor function and dysfunction and arise through the complex interaction of intrinsic properties and inhibitory and excitatory synaptic inputs. In many neurons, hyperpolarization-activated cyclic nucleotide-gated (HCN) channels play key roles in intrinsic excitability and synaptic integration both under normal conditions and in disease states. However, in STN neurons, which strongly express HCN channels, their roles remain relatively obscure. To address this deficit, complementary molecular and cellular electrophysiological, imaging, and computational approaches were applied to the rat STN. Molecular profiling demonstrated that individual STN neurons express mRNA encoding several HCN subunits, with HCN2 and 3 being the most abundant. Light and electron microscopic analysis showed that HCN2 subunits are strongly expressed and distributed throughout the somatodendritic plasma membrane. Voltage-, current-, and dynamic-clamp analysis, two-photon Ca(2+) imaging, and computational modeling revealed that HCN channels are activated by GABA(A) receptor-mediated inputs and thus limit synaptic hyperpolarization and deinactivation of low-voltage-activated Ca(2+) channels. Although HCN channels also limited the temporal summation of EPSPs, generated through two-photon uncaging of glutamate, this action was largely shunted by GABAergic inhibition that was necessary for HCN channel activation. Together the data demonstrate that HCN channels in STN neurons selectively counteract GABA(A) receptor-mediated inhibition arising from the globus pallidus and thus promote single-spike activity rather than rebound burst firing.

[Analyses of Functional Connectivity between Brain Regions].

Because higher-order cognitive functions are supposed to be executed by the interplay between various brain regions, it is necessary to elucidate the neural communication between brain regions to understand the purpose of understanding the mechanism of such brain functions. Therefor, functional connectivity analysis has been rapidly gaining in importance. This is an analysis that illuminates the spatiotemporal dynamics at the whole-brain level based on the functional or effective connections, defined by a statistical correlation or a causal relation of neural activities between brain regions. The present manuscript primarily provides the basic idea of functional connectivity analysis, and then introduces representative methods. Finally, the approaches to the diagnosis of neurological diseases based on this analysis are introduced.

Information-theoretic approach to detect directional information flow in EEG signals induced by TMS.

Effective connectivity analysis has been widely applied to noninvasive recordings such as functional magnetic resonance imaging and electroencephalograms (EEGs). Previous studies have aimed to extract the causal relations between brain regions, but the validity of the derived connectivity has not yet been fully determined. This is because it is generally difficult to identify causality in the usual experimental framework based on observations alone. Transcranial magnetic stimulation (TMS) provides a framework in which a controllable perturbation is applied to a local brain region and the effect is examined by comparing the neural activity with and without this stimulation. This study evaluates two methods for effective connectivity analysis, symbolic transfer entropy (STE) and vector autoregression (VAR), by applying them to TMS-EEG data. In terms of the consistency of results from different experimental sessions, STE is found to yield robust results irrespective of sessions, whereas VAR produces less correlation between sessions. Furthermore, STE preferentially detects the directional information flow from the TMS target. Taken together, our results suggest that STE is a reliable method for detecting the effect of TMS, implying that it would also be useful for identifying neural activity during cognitive tasks and resting states.

Structure of spontaneous UP and DOWN transitions self-organizing in a cortical network model.

Synaptic plasticity is considered to play a crucial role in the experience-dependent self-organization of local cortical networks. In the absence of sensory stimuli, cerebral cortex exhibits spontaneous membrane potential transitions between an UP and a DOWN state. To reveal how cortical networks develop spontaneous activity, or conversely, how spontaneous activity structures cortical networks, we analyze the self-organization of a recurrent network model of excitatory and inhibitory neurons, which is realistic enough to replicate UP-DOWN states, with spike-timing-dependent plasticity (STDP). The individual neurons in the self-organized network exhibit a variety of temporal patterns in the two-state transitions. In addition, the model develops a feed-forward network-like structure that produces a diverse repertoire of precise sequences of the UP state. Our model shows that the self-organized activity well resembles the spontaneous activity of cortical networks if STDP is accompanied by the pruning of weak synapses. These results suggest that the two-state membrane potential transitions play an active role in structuring local cortical circuits.

Reconstructing neuronal circuitry from parallel spike trains.

State-of-the-art techniques allow researchers to record large numbers of spike trains in parallel for many hours. With enough such data, we should be able to infer the connectivity among neurons. Here we develop a method for reconstructing neuronal circuitry by applying a generalized linear model (GLM) to spike cross-correlations. Our method estimates connections between neurons in units of postsynaptic potentials and the amount of spike recordings needed to verify connections. The performance of inference is optimized by counting the estimation errors using synthetic data. This method is superior to other established methods in correctly estimating connectivity. By applying our method to rat hippocampal data, we show that the types of estimated connections match the results inferred from other physiological cues. Thus our method provides the means to build a circuit diagram from recorded spike trains, thereby providing a basis for elucidating the differences in information processing in different brain regions.

Interplay between a phase response curve and spike-timing-dependent plasticity leading to wireless clustering.

A phase response curve (PRC) characterizes the signal transduction between oscillators such as neurons on a fixed network in a minimal manner, while spike-timing-dependent plasiticity (STDP) characterizes the way of rewiring networks in an activity-dependent manner. This paper demonstrates that these two key properties both related to the interaction times of oscillators work synergetically to carve functionally useful circuits. STDP working on neurons that prefer asynchrony converts the initial asynchronous firing to clustered firing with synchrony within a cluster. They get synchronized within a cluster despite their preference to asynchrony because STDP selectively disrupts intracluster connections, which we call wireless clustering. Our PRC analysis reveals a triad mechanism: the network structure affects how the PRC is read out to determine the synchrony tendency, the synchrony tendency affects how the STDP works, and STDP affects the network structure, closing the loop.

[A Potential Mechanism for Spontaneous Oscillation in the Abnormal Retina].

　Rhythmic neural activities are observed in many brain regions, and these are considered to play an important role in neural information processing. On the other hand, distinct rhythmic neural activities emerge under several pathological conditions, suggesting that rhythmic neural activity has a close relation to brain function and dysfunction. In many pathological cases, the intrinsic property of unusual rhythm generation in a neuron or a neuronal network is prevented under normal conditions, but released by the pathological condition. Therefore, it may be useful to explore which conditions determine rhythm generation in order to understand the mechanisms of brain function/dysfunction. The pathological retina in retinal degeneration exhibits rhythmic neural activity not observed in the healthy retina. In this review, we first provide a brief introduction to the possible mechanisms of rhythm generation in a neural system. Then we introduce experimental evidence of rhythm generation in the pathological retina, as well as two hypotheses regarding this mechanism. Finally, we raise several issues to be solved for the further understanding of pathological rhythm generation.

[Electrophysiological Analysis of Retinal Oscillation in Normal and Degenerated Retina].

　The vertebrate retina is one of the most sophisticated parts of the nervous system. It comprises five classes of neurons and one glial type cell. During development, but prior to a vertebrate's eyes opening, retinal circuits are refined by endogenous neural activity. Characteristic patterns of activity, including oscillatory activity, occur in the normal retina, whereas distinctive alternative patterns occur in abnormal retinas. In this paper, we first describe the electrophysiological and spike sorting methods used to study retinal oscillations. Next, we describe the mechanisms and functions of oscillation in the normal retina. Finally, we characterize the distinctive oscillations and abnormal spontaneous activities in the degenerative retina.

Different Activity Patterns in Retinal Ganglion Cells of TRPM1 and mGluR6 Knockout Mice.

TRPM1, the first member of the melanoma-related transient receptor potential (TRPM) subfamily, is the visual transduction channel downstream of metabotropic glutamate receptor 6 (mGluR6) on retinal ON bipolar cells (BCs). Human TRPM1 mutations are associated with congenital stationary night blindness (CSNB). In both TRPM1 and mGluR6 KO mouse retinas, OFF but not ON BCs respond to light stimulation. Here we report an unexpected difference between TRPM1 knockout (KO) and mGluR6 KO mouse retinas. We used a multielectrode array (MEA) to record spiking in retinal ganglion cells (RGCs). We found spontaneous oscillations in TRPM1 KO retinas, but not in mGluR6 KO retinas. We performed a structural analysis on the synaptic terminals of rod ON BCs. Intriguingly, rod ON BC terminals were significantly smaller in TRPM1 KO retinas than in mGluR6 KO retinas. These data suggest that a deficiency of TRPM1, but not of mGluR6, in rod ON bipolar cells may affect synaptic terminal maturation. We speculate that impaired signaling between rod BCs and AII amacrine cells (ACs) leads to spontaneous oscillations. TRPM1 and mGluR6 are both essential components in the signaling pathway from photoreceptors to ON BC dendrites, yet they differ in their effects on the BC terminal and postsynaptic circuitry.

Bayesian Estimation of Phase Dynamics Based on Partially Sampled Spikes Generated by Realistic Model Neurons.

A dynamic system showing stable rhythmic activity can be represented by the dynamics of phase oscillators. This would provide a useful mathematical framework through which one can understand the system's dynamic properties. A recent study proposed a Bayesian approach capable of extracting the underlying phase dynamics directly from time-series data of a system showing rhythmic activity. Here we extended this method to spike data that otherwise provide only limited phase information. To determine how this method performs with spike data, we applied it to simulated spike data generated by a realistic neuronal network model. We then compared the estimated dynamics obtained based on the spike data with the dynamics theoretically derived from the model. The method successfully extracted the modeled phase dynamics, particularly the interaction function, when the amount of available data was sufficiently large. Furthermore, the method was able to infer synaptic connections based on the estimated interaction function. Thus, the method was found to be applicable to spike data and practical for understanding the dynamic properties of rhythmic neural systems.