Biomedical subjects

Shigeru Shinomoto

Publications and source records attributed to Shigeru Shinomoto.

7 recordsLinked to original sources

PubMed2006

Inhibitory neurons can facilitate rhythmic activity in a neural network.

Synchrony emerges in a population of oscillators interacting through in-phase couplings. We addressed a question of whether inhibitory neurons simply hinder the emergence of the synchronous activity among excitatory neurons, or facilitate it. An analysis of a simple phase model revealed that both cases may take place. Numerical simulations of the more realistic models revealed that inhibitory neurons rather facilitate rhythmic activity.

Journal Article↗

PubMed2005

Regional and laminar differences in in vivo firing patterns of primate cortical neurons.

The firing rates of cortical neurons change in time; yet, some aspects of their in vivo firing characteristics remain unchanged and are specific to individual neurons. A recent study has shown that neurons in the monkey medial motor areas can be grouped into 2 firing types, "likely random" and "quasi-regular," according to a measure of local variation of interspike intervals. In the present study, we extended this analysis to area TE of the inferior temporal cortex and addressed whether this classification applies generally to different cortical areas and whether different types of neurons show different laminar distribution. We found that area TE did consist of 2 groups of neurons with different firing characteristics, one similar to the "likely random" type in the medial motor cortical areas, and the other exhibiting a "clumpy-bursty" firing pattern unique to TE. The quasi-regular type was rarely observed in area TE. The likely random firing type of neuron was more frequently found in layers V-VI than in layers II-III, whereas the opposite was true for the clumpy-bursty firing type. These results show that neocortical areas consist of heterogeneous neurons that differ from one area to another in their basic firing characteristics. Moreover, we show that spike trains obtained from a single cortical neuron can provide a clue that helps to identify its layer localization.

Action Potentials↗

PubMed2004

Predicting spike timings of current-injected neurons.

Typical neurospiking models were examined on the ability to reproduce and predict the spike sequences of a biological neuron for a variety of fluctuating currents, using a fixed set of parameter values. The predicting accuracy was found to be particularly good for the Hodgkin-Huxley models augmented with the Ca(2+)-dependent potassium current generating slow afterhyper-polarization and/or the muscarine-sensitive potassium current. In the successful parameter determination method, the effective membrane time constant is estimated very short, typically about 5 ms. The biological neurons we examined were distinctly classified into two types according to the estimated percent contents of those ionic channels.

Action Potentials↗

PubMed2003

Differences in spiking patterns among cortical neurons.

Spike sequences recorded from four cortical areas of an awake behaving monkey were examined to explore characteristics that vary among neurons. We found that a measure of the local variation of interspike intervals, L(V), is nearly the same for every spike sequence for any given neuron, while it varies significantly among neurons. The distributions of L(V) values for neuron ensembles in three of the four areas were found to be distinctly bimodal. Two groups of neurons classified according to the spiking irregularity exhibit different responses to the same stimulus. This suggests that neurons in each area can be classified into different groups possessing unique spiking statistics and corresponding functional properties.

Action Potentials↗

PubMed2002

New classification scheme of cortical sites with the neuronal spiking characteristics.

Multiple cortical areas are mutually compared on the bases of neuronal spiking characteristics measured through three dimensionless interspike interval statistical coefficients. The spike sequences were recorded from the prefrontal cortical area (PF), the pre-supplementary motor area (Pre-SMA), the supplementary motor area (SMA) and the rostral cingulate motor area (CMAr) of a behaving monkey performing a waiting period task. The distribution of three statistical coefficients is found to be largely dependent on the recording site. By measuring the Hellinger distances among those distributions, Pre-SMA, SMA and CMAr are found to be mutually similar in comparison with PF.

Action Potentials↗

PubMedDate not supplied

A measure of local variation of inter-spike intervals.

It has been revealed in our recent study that cortical neurons are categorized into distinct types, according to a new measure of the local variation of inter-spike intervals, L(V). In this paper, we obtain values of the local variation L(V) and a conventional coefficient of variation C(V) for a variety of model point processes. While the value of C(V) undergoes large changes by rate fluctuation of the point processes, the value of L(V) does not undergo large changes by rate fluctuation, and is principally determined by the form of intrinsic interval distribution of the original model point process.

Action Potentials↗

PubMedDate not supplied

Recording site dependence of the neuronal spiking statistics.

Spiking characteristics of neurons in the middle temporal (MT) area and the medial superior temporal (MST) area in the visual cortex of a monkey are compared with the ones in the principal sulcus (PS) area in the prefrontal cortex. The comparison is based on the basis of three inter-spike interval statistical measures: the coefficient of variation (CV), the skewness coefficient (SK) and the correlation coefficient of consecutive intervals (COR). Even for the spike sequences recorded from the same neuron, three coefficients computed from 100 intervals do not always exhibit similar values, but distribute rather widely. The distribution of three coefficients obtained from a single neuron in the MST area does not largely deviate from the distribution obtained from multiple neurons in MT and MST areas. Those distributions, however, largely deviate from the distribution obtained from neurons in the PS area. In this way, the distribution of those statistical coefficients reflects the nature of the recording site.

Action Potentials↗