It has been empirically established that the cerebral cortical areas defined by Brodmann one hundred years ago solely on the basis of cellular organization are closely correlated to their function, such as sensation, association, and motion. Cytoarchitectonically distinct cortical areas have different densities and types of neurons. Thus, signaling patterns may also vary among cytoarchitectonically unique cortical areas. To examine how neuronal signaling patterns are related to innate cortical functions, we detected intrinsic features of cortical firing by devising a metric that efficiently isolates non-Poisson irregular characteristics, independent of spike rate fluctuations that are caused extrinsically by ever-changing behavioral conditions. Using the new metric, we analyzed spike trains from over 1,000 neurons in 15 cortical areas sampled by eight independent neurophysiological laboratories. Analysis of firing-pattern dissimilarities across cortical areas revealed a gradient of firing regularity that corresponded closely to the functional category of the cortical area; neuronal spiking patterns are regular in motor areas, random in the visual areas, and bursty in the prefrontal area. Thus, signaling patterns may play an important role in function-specific cerebral cortical computation.

In simulating realistic neuronal circuitry composed of diverse types of neurons, we need an elemental spiking neuron model that is capable of not only quantitatively reproducing spike times of biological neurons given in vivo-like fluctuating inputs, but also qualitatively representing a variety of firing responses to transient current inputs. Simplistic models based on leaky integrate-and-fire mechanisms have demonstrated the ability to adapt to biological neurons. In particular, the multi-timescale adaptive threshold (MAT) model reproduces and predicts precise spike times of regular-spiking, intrinsic-bursting, and fast-spiking neurons, under any fluctuating current; however, this model is incapable of reproducing such specific firing responses as inhibitory rebound spiking and resonate spiking. In this paper, we augment the MAT model by adding a voltage dependency term to the adaptive threshold so that the model can exhibit the full variety of firing responses to various transient current pulses while maintaining the high adaptability inherent in the original MAT model. Furthermore, with this addition, our model is actually able to better predict spike times. Despite the augmentation, the model has only four free parameters and is implementable in an efficient algorithm for large-scale simulation due to its linearity, serving as an element neuron model in the simulation of realistic neuronal circuitry.

Neurons temporally integrate input signals, translating them into timed output spikes. Because neurons nonperiodically emit spikes, examining spike timing can reveal information about input signals, which are determined by activities in the populations of excitatory and inhibitory presynaptic neurons. Although a number of mathematical methods have been developed to estimate such input parameters as the mean and fluctuation of the input current, these techniques are based on the unrealistic assumption that presynaptic activity is constant over time. Here, we propose tracking temporal variations in input parameters with a two-step analysis method. First, nonstationary firing characteristics comprising the firing rate and non-Poisson irregularity are estimated from a spike train using a computationally feasible state-space algorithm. Then, information about the firing characteristics is converted into likely input parameters over time using a transformation formula, which was constructed by inverting the neuronal forward transformation of the input current to output spikes. By analyzing spike trains recorded in vivo, we found that neuronal input parameters are similar in the primary visual cortex V1 and middle temporal area, whereas parameters in the lateral geniculate nucleus of the thalamus were markedly different.

Every computational unit in the brain monitors incoming signals, instant by instant, for meaningful changes in the face of stochastic fluctuation. Recent studies have suggested that even a single neuron can detect changes in noisy signals. In this paper, we demonstrate that a single leaky integrate-and-fire neuron can achieve change-point detection close to that of theoretical optimal, for uniform-rate process, functions even better than a Bayes-optimal algorithm when the underlying rate deviates from a presumed uniform rate process. Given a reasonable number of synaptic connections (order 10(4)) and the rate of the input spike train, the values of the membrane time constant and the threshold found for optimizing change-point detection are close to those seen in biological neurons. These findings imply that biological neurons could act as sophisticated change-point detectors.

IntroductionBrodmann's map constructed on the basis of cellular organization has been useful as a functional map of the cortical areas, such as the sensation, association, and motion. Since cytoarchitectually distinct cortical areas have different arrangements, density, and types of neurons, it is reasonable to expect that neuronal signaling patterns may reflect the structures, and effectively operate for their specific computations. In order to examine the intrinsic relationship between neuronal firing signals and cortical functions as well as cellular structures, we develop a metric that may extract intrinsic non-Poisson irregular firing characteristics from a spike train in isolation from the firing rate fluctuation of extrinsic origin. Using a metric of local variation Lv that measures the cross-correlation of consecutive ISIs rescaled with instantaneous rate [1,2], we revealed that the firing regularity remains fairly invariant with time and rate fluctuation for individual neurons. However, it was reported that another metric measuring the instantaneous irregularity similar to Lv varied in time and with behavioral contexts in another experiment [3]. This fact indicates that the local firing metrics suggested so far are still inadequate for extracting the intrinsic firing characteristics in isolation of the extrinsic perturbation. Here we revise Lv into a new metric by enhancing the firing rate invariance, which allows the signaling patterns specific to individual neurons to be detected more sensitively, and conduct an analysis to reveal the difference of intrinsic firing dynamics among the cortical areas.Using the revised metric, we analyze spike trains from a large number of neurons recorded in eight laboratories from fifteen cerebral cortical areas. The two-dimensional map contracted by multidimensional scaling from the firing-pattern dissimilarities across the cortical areas reveals a gradient of the firing regularity in close correspondence to the functional categories of the cortical areas. Neuronal firing patterns are regular in the primary and higher-order motor areas, random in the visual areas, and bursty in the prefrontal area. These results indicate that the neuronal signaling patterns not only reflect the cortical structures, but also play a crucial role in the cerebral cortical computation specific to their functional categories.