Keiji Miura

Odor Representations in Olfactory Cortex: Distributed Rate Coding and Decorrelated Population Activity

VIDEO ABSTRACT How information encoded in neuronal spike trains is used to guide sensory decisions is a fundamental question. In olfaction, a single sniff is sufficient for fine odor discrimination but the neural representations on which olfactory decisions are based are unclear. Here, we recorded neural ensemble activity in the anterior piriform cortex (aPC) of rats performing an odor mixture categorization task. We show that odors evoke transient bursts locked to sniff onset and that odor identity can be better decoded using burst spike counts than by spike latencies or temporal patterns. Surprisingly, aPC ensembles also exhibited near-zero noise correlations during odor stimulation. Consequently, fewer than 100 aPC neurons provided sufficient information to account for behavioral speed and accuracy, suggesting that behavioral performance limits arise downstream of aPC. These findings demonstrate profound transformations in the dynamics of odor representations from the olfactory bulb to cortex and reveal likely substrates for odor-guided decisions.

Balanced Excitatory and Inhibitory Inputs to Cortical Neurons Decouple Firing Irregularity from Rate Modulations

In vivo cortical neurons are known to exhibit highly irregular spike patterns. Because the intervals between successive spikes fluctuate greatly, irregular neuronal firing makes it difficult to estimate instantaneous firing rates accurately. If, however, the irregularity of spike timing is decoupled from rate modulations, the estimate of firing rate can be improved. Here, we introduce a novel coding scheme to make the firing irregularity orthogonal to the firing rate in information representation. The scheme is valid if an interspike interval distribution can be well fitted by the gamma distribution and the firing irregularity is constant over time. We investigated in a computational model whether fluctuating external inputs may generate gamma process-like spike outputs, and whether the two quantities are actually decoupled. Whole-cell patch-clamp recordings of cortical neurons were performed to confirm the predictions of the model. The output spikes were well fitted by the gamma distribution. The firing irregularity remained approximately constant regardless of the firing rate when we injected a balanced input, in which excitatory and inhibitory synapses are activated concurrently while keeping their conductance ratio fixed. The degree of irregular firing depended on the effective reversal potential set by the balance between excitation and inhibition. In contrast, when we modulated conductances out of balance, the irregularity varied with the firing rate. These results indicate that the balanced input may improve the efficiency of neural coding by clamping the firing irregularity of cortical neurons. We demonstrate how this novel coding scheme facilitates stimulus decoding.

Hodge-Kodaira decomposition of evolving neural networks

Although it is very important to scrutinize recurrent structures of neural networks for elucidating brain functions, conventional methods often have difficulty in characterizing global loops within a network systematically. Here we applied the Hodge-Kodaira decomposition, a topological method, to an evolving neural network model in order to characterize its loop structure. By controlling a learning rule parametrically, we found that a model with an STDP-rule, which tends to form paths coincident with causal firing orders, had the most loops. Furthermore, by counting the number of global loops in the network, we detected the inhomogeneity inside the chaotic region, which is usually considered intractable.

Real-Time Computing of Touch Topology via Poincare–Hopf Index

While visual or tactile image data have been conventionally processed via filters or perceptron-like learning machines, the recent advances of computational topology may make it possible to successfully extract the global features from the local pixelwise data. In fact, some inventive algorithms have succeeded in computing the topological invariants, such as the number of objects or holes and irrespective of the shapes and positions of the touches. However, they are mostly offline algorithms aiming at big data. A real-time algorithm for computing topology is also needed for interactive applications such as touch sensors. Here, we propose a fast algorithm to compute the Euler characteristics of touch shapes by using the Poincare-Hopf index for each pixel. We demonstrate that our simple algorithm, implemented solely as logical operations in Arduino, correctly returns and updates the topological invariants of touches in real time.

Synchronization analysis of resonate-and-fire neuron models with delayed resets

We analyzed the synchronization properties of coupled resonate-and-fire neuron models with delayed resets. The conventional phase reduction method cannot be applied to the system as it shows periodicity only under time-delayed feedback and cannot be treated as a dynamical system. Therefore, we generalized the conventional phase reduction method to include dynamics with delay-induced periodicity. Then we applied the theory to the system and utilized the reduced phase dynamics for analyzing the synchronization properties.

A rate-independent measure of irregularity for event series and its application to neural spiking activity

Although higher-order statistics of neuronal firing have been characterized in neuroscience, many analyses ignore the nonstationarity of the background firing rate. We discuss how to measure the irregularity of interspike intervals in a rate-independent manner. Under the framework of semiparametric statistical models, we develop an estimator of firing irregularity which remains after the effects of rate modulations are removed. We found that firing irregularity is robust and reproducible in neurons in olfactory cortex irrespective of the rate modulation during the task period. As the level of irregularity varies among neurons, we classified neurons in olfactory cortex by using the proposed measure as a feature.

BURST SYNCHRONIZATION AND CHAOTIC PHENOMENA IN TWO STRONGLY COUPLED RESONATE-AND-FIRE NEURONS

In this paper, we address the synchronization properties of two pulse-coupled resonate-and-fire neuron (RFN) models. The RFN model is a spiking neuron model that has second-order membrane dynamics with a threshold and a firing reset value. Due to such dynamics, the RFN model exhibits subthreshold oscillation of the membrane potential, and is sensitive to the timing of stimuli. So far the existence of anti-phase synchronization states and their stability in a system of two pulse-coupled RFN models have been reported. However, the effects of the reset value after firing on such synchronization states have not been considered. The reset value may affect the sensitivity to the input timing, leading to change in synchronization properties in the pulse-coupled RFN models. We newly found out-of-phase burst synchronization states and related bifurcation phenomena depending on the coupling strength in the system as the reset value was changed. Focusing on the symmetry of the system, we analyzed the stability of such phenomena by using a firing time difference map constructed from 1D return maps with respect to firing time difference between two neurons. The analyses revealed the global stability of the out-of-phase synchronization states and the existence of the type I intermittency chaotic behavior.

Pulse-coupled spin torque nano oscillators with dynamic synapses for neuromorphic computing

In this work, we present a system pulse-coupled spin torque nano oscillators (STNOs) as a novel physical implementation of coupled phase oscillators for neuromorphic computing. First, we describe the dynamical properties of pulse-coupled STNOs and explain their phase description. We investigated the system dynamics of fully pulse-coupled STNOs with both static and dynamic synapses through numerical simulations. Consequently, various cooperative phenomena, such as synchronous, clustered, and coherent states, can be observed depending on synaptic interactions. Finally, we discuss neuromorphic applications of such cooperative phenomena and possible implementation of dynamic synapse for the pulse-coupled STNOs.

Silicon neuron design based on phase reduction analysis

In this paper, we propose a dynamical system design approach for silicon neurons (SiNs) based on the phase reduction theory. The design approaches for SiNs can be classified as three: the phenomenological design, the conductance-based design, and the dynamical systems design. As a part of the third approach, we propose the phase response curve (PRC)-based design for SiNs to enhance synchronization in an ensembles of SiNs. We consider the key criteria to optimize SiN design in terms of phase response properties by analyzing various circuit models of previous SiNs. Furthermore, as a case study, we demonstrate how to tune circuit parameters to obtain a desirable PRC of a resonate-and-fire neuron (RFN) circuit. Finally, we discuss the possibility of extending our approach to design a class of the generalized integrate-and-fire neuron (GIFN) circuits including the Izhikevich type SiNs.

Dynamical system design for silicon neurons using phase reduction approach

In the present paper, we apply a computer-aided phase reduction approach to dynamical system design for silicon neurons (SiNs). Firstly, we briefly review the dynamical system design for SiNs. Secondly, we summarize the phase response properties of circuit models of previous SiNs to clarify design criteria in our approach. From a viewpoint of the phase reduction theory, as a case study, we show how to tune circuit parameters of the resonate-and-fire neuron (RFN) circuit as a hybrid type SiN. Finally, we demonstrate delay-induced synchronization in a silicon spiking neural network that consists of the RFN circuits.