Neural dynamics and information representation in microcircuits of motor cortex.

The brain has to analyze and respond to external events that can change rapidly from time to time, suggesting that information processing by the brain may be essentially dynamic rather than static. The dynamical features of neural computation are of significant importance in motor cortex that governs the process of movement generation and learning. In this paper, we discuss these features based primarily on our recent findings on neural dynamics and information coding in the microcircuit of rat motor cortex.

Mapping the spatio-temporal structure of motor cortical LFP and spiking activities during reach-to-grasp movements.

Grasping an object involves shaping the hand and fingers in relation to the object's physical properties. Following object contact, it also requires a fine adjustment of grasp forces for secure manipulation. Earlier studies suggest that the control of hand shaping and grasp force involve partially segregated motor cortical networks.

Feature-based attention in early vision for the modulation of figure-ground segregation.

We investigated psychophysically whether feature-based attention modulates the perception of figure-ground (F-G) segregation and, based on the results, we investigated computationally the neural mechanisms underlying attention modulation. In the psychophysical experiments, the attention of participants was drawn to a specific motion direction and they were then asked to judge the side of figure in an ambiguous figure with surfaces consisting of distinct motion directions. The results of these experiments showed that the surface consisting of the attended direction of motion was more frequently observed as figure, with a degree comparable to that of spatial attention (Wagatsuma et al.

Different transporter systems regulate extracellular GABA from vesicular and non-vesicular sources.

Tonic GABA type A (GABAA) conductance is a key factor regulating neuronal excitability and computation in neuronal networks. The magnitude of the tonic GABAA conductance depends on the concentration of ambient GABA originating from vesicular and non-vesicular sources and is tightly regulated by GABA uptake. Here we show that the transport system regulating ambient GABA responsible for tonic GABAA conductances in hippocampal CA1 interneurons depends on its source.

Error detection and representation in the olivo-cerebellar system.

Complex spikes generated in a cerebellar Purkinje cell via a climbing fiber have been assumed to encode errors in the performance of neuronal circuits involving Purkinje cells. To reexamine this notion in this review, I analyzed structures of motor control systems involving the cerebellum. A dichotomy was found between the two types of error: sensory and motor errors play roles in the feedforward and feedback control conditions, respectively.

The hierarchical and functional connectivity of higher-order cognitive mechanisms: neurorobotic model to investigate the stability and flexibility of working memory.

Higher-order cognitive mechanisms (HOCM), such as planning, cognitive branching, switching, etc., are known to be the outcomes of a unique neural organizations and dynamics between various regions of the frontal lobe. Although some recent anatomical and neuroimaging studies have shed light on the architecture underlying the formation of such mechanisms, the neural dynamics and the pathways in and between the frontal lobe to form and/or to tune the stability level of its working memory remain controversial.

Associative memory model with long-tail-distributed Hebbian synaptic connections.

The postsynaptic potentials of pyramidal neurons have a non-Gaussian amplitude distribution with a heavy tail in both hippocampus and neocortex. Such distributions of synaptic weights were recently shown to generate spontaneous internal noise optimal for spike propagation in recurrent cortical circuits. However, whether this internal noise generation by heavy-tailed weight distributions is possible for and beneficial to other computational functions remains unknown.

Supercomputers ready for use as discovery machines for neuroscience.

NEST is a widely used tool to simulate biological spiking neural networks. Here we explain the improvements, guided by a mathematical model of memory consumption, that enable us to exploit for the first time the computational power of the K supercomputer for neuroscience. Multi-threaded components for wiring and simulation combine 8 cores per MPI process to achieve excellent scaling.

Functional Differences between Global Pre- and Postsynaptic Inhibition in the Drosophila Olfactory Circuit.

The Drosophila antennal lobe is subdivided into multiple glomeruli, each of which represents a unique olfactory information processing channel. In each glomerulus, feedforward input from olfactory receptor neurons (ORNs) is transformed into activity of projection neurons (PNs), which represent the output. Recent investigations have indicated that lateral presynaptic inhibitory input from other glomeruli controls the gain of this transformation.

Spike sorting of heterogeneous neuron types by multimodality-weighted PCA and explicit robust variational Bayes.

This study introduces a new spike sorting method that classifies spike waveforms from multiunit recordings into spike trains of individual neurons. In particular, we develop a method to sort a spike mixture generated by a heterogeneous neural population. Such a spike sorting has a significant practical value, but was previously difficult.

Association of type I neurons positive for NADPH-diaphorase with blood vessels in the adult monkey corpus callosum.

Sagittal sections through the corpus callosum of adult macaque monkeys (n = 7) reveal a subpopulation of neurons positive for NADPH-diaphorase (NADPHd). These are sparsely distributed, with 2-12 neurons scored over the anterior two-thirds of the callosum (about 14 mm). Neurons are densely labeled, type 1; but on the basis of soma and dendritic morphology, these neurons exhibit distinct heterogeneity.

Layer-Dependent Attentional Processing by Top-down Signals in a Visual Cortical Microcircuit Model.

A vast amount of information about the external world continuously flows into the brain, whereas its capacity to process such information is limited. Attention enables the brain to allocate its resources of information processing to selected sensory inputs for reducing its computational load, and effects of attention have been extensively studied in visual information processing. However, how the microcircuit of the visual cortex processes attentional information from higher areas remains largely unknown.

Information loss associated with imperfect observation and mismatched decoding.

We consider two types of causes leading to information loss when neural activities are passed and processed in the brain. One is responses of upstream neurons to stimuli being imperfectly observed by downstream neurons. The other is upstream neurons non-optimally decoding stimuli information contained in the activities of the downstream neurons.

Finite post synaptic potentials cause a fast neuronal response.

A generic property of the communication between neurons is the exchange of pulses at discrete time points, the action potentials. However, the prevalent theory of spiking neuronal networks of integrate-and-fire model neurons relies on two assumptions: the superposition of many afferent synaptic impulses is approximated by Gaussian white noise, equivalent to a vanishing magnitude of the synaptic impulses, and the transfer of time varying signals by neurons is assessable by linearization. Going beyond both approximations, we find that in the presence of synaptic impulses the response to transient inputs differs qualitatively from previous predictions.

Surrogate spike train generation through dithering in operational time.

Detecting the excess of spike synchrony and testing its significance can not be done analytically for many types of spike trains and relies on adequate surrogate methods. The main challenge for these methods is to conserve certain features of the spike trains, the two most important being the firing rate and the inter-spike interval statistics. In this study we make use of operational time to introduce generalizations to spike dithering and propose two novel surrogate methods which conserve both features with high accuracy.

Spike timing-dependent plasticity as the origin of the formation of clustered synaptic efficacy engrams.

Synapse location, dendritic active properties and synaptic plasticity are all known to play some role in shaping the different input streams impinging onto a neuron. It remains unclear however, how the magnitude and spatial distribution of synaptic efficacies emerge from this interplay. Here, we investigate this interplay using a biophysically detailed neuron model of a reconstructed layer 2/3 pyramidal cell and spike timing-dependent plasticity (STDP).

Possible dendritic contribution to unimodal numerosity tuning and weber-fechner law-dependent numerical cognition.

Humans and animals are known to share an ability to estimate or compare the numerosity of visual stimuli, and this ability is considered to be supported by the cortical neurons that have unimodal tuning for numerosity, referred to as the numerosity detector neurons. How such unimodal numerosity tuning is shaped through plasticity mechanisms is unknown. Here, I propose a testable hypothetical mechanism based on recently revealed features of the neuronal dendrite, namely, cooperative plasticity induction and nonlinear input integration at nearby dendritic sites, on the basis of the existing proposal that individual visual stimuli are represented as similar localized activities regardless of the size or the shape in a cortical region in the dorsal visual pathway.

Optical imaging as a link between cellular neurophysiology and circuit modeling.

The relatively simple and highly modular circuitry of the cerebellum raised expectations decades ago that a realistic computational model of cerebellar circuit operations would be feasible, and prove insightful for unraveling cerebellar information processing. To this end, the biophysical properties of most cerebellar cell types and their synaptic connections have been well characterized and integrated into realistic single cell models. Furthermore, large scale models of cerebellar circuits that extrapolate from single cell properties to circuit dynamics have been constructed.

Second and third generation voltage-sensitive fluorescent proteins for monitoring membrane potential.

Over the last decade, optical neuroimaging methods have been enriched by engineered biosensors derived from fluorescent protein (FP) reporters fused to protein detectors that convert physiological signals into changes of intrinsic FP fluorescence. These FP-based indicators are genetically encoded, and hence targetable to specific cell populations within networks of heterologous cell types. Among this class of biosensors, the development of optical probes for membrane potential is both highly desirable and challenging.

Size and Receptor Density of Glutamatergic Synapses: A Viewpoint from Left-Right Asymmetry of CA3-CA1 Connections.

Synaptic plasticity is considered to be the main mechanism for learning and memory. Excitatory synapses in the cerebral cortex and hippocampus undergo plastic changes during development and in response to electric stimulation. It is widely accepted that this process is mediated by insertion and elimination of various glutamate receptors.

Neural modeling of an internal clock.

We studied a simple random recurrent inhibitory network. Despite its simplicity, the dynamics was so rich that activity patterns of neurons evolved with time without recurrence due to random recurrent connections among neurons. The sequence of activity patterns was generated by the trigger of an external signal, and the generation was stable against noise.