Takashi Takekawa

Gamma rhythmic bursts: coherence control in networks of cortical pyramidal neurons

Much evidence indicates that synchronized gamma-frequency (2070 Hz) oscillation plays a significant functional role in the neocortex and hippocampus. Chattering neuron is a possible neocortical pacemaker for the gamma oscillation. Based on our recent model of chattering neurons, here we study how gamma-frequency bursting is synchronized in a network of these neurons. Using a phase oscillator description, we first examine how two coupled chattering neurons are synchronized. The analysis reveals that an incremental change of the bursting mode, such as from singlet to doublet, always accompanies a rapid transition from antisynchronous to synchronous firing. The state transition occurs regardless of what changes the bursting mode. Within each bursting mode, the neuronal activity undergoes a gradual change from synchrony to antisynchrony. Since the sensitivity to Ca2+ and the maximum conductance of Ca2+ -dependent cationic current as well as the intensity of input current systematically control the bursting mode, these quantities may be crucial for the regulation of the coherence of local cortical activity. Numerical simulations demonstrate that the modulations of the calcium sensitivity and the amplitude of the cationic current can induce rapid transitions between synchrony and asynchrony in a large-scale network of chattering neurons. The rapid synchronization of chattering neurons is shown to synchronize the activities of regular spiking pyramidal neurons at the gamma frequencies, as may be necessary for selective attention or binding processing in object recognition.

Spike detection from noisy neural data in linear‐probe recordings

Simultaneous recordings of multiple neuron activities with multi‐channel extracellular electrodes are widely used for studying information processing by the brains neural circuits. In this method, the recorded signals containing the spike events of a number of adjacent or distant neurons must be correctly sorted into spike trains of individual neurons, and a variety of methods have been proposed for this spike sorting. However, spike sorting is computationally difficult because the recorded signals are often contaminated by biological noise. Here, we propose a novel method for spike detection, which is the first stage of spike sorting and hence crucially determines overall sorting performance. Our method utilizes a model of extracellular recording data that takes into account variations in spike waveforms, such as the widths and amplitudes of spikes, by detecting the peaks of band‐pass‐filtered data. We show that the new method significantly improves the cost–performance of multi‐channel electrode recordings by increasing the number of cleanly sorted neurons.

Influences of synaptic location on the synchronization of rhythmic bursting neurons

We study how the location of synaptic input influences the stablex firing states in coupled model neurons bursting rhythmically at the gamma frequencies (20–70 Hz). The model neuron consists of two compartments and generates one, two, three or four spikes in each burst depending on the intensity of input current and the maximum conductance of M-type potassium current. If the somata are connected by reciprocal excitatory synapses, we find strong correlations between the changes in the bursting mode and those in the stable phase-locked states of the coupled neurons. The stability of the in-phase phase-locked state (synchronously firing state) tends to change when the individual neurons change their bursting patterns. If, however, the synaptic connections are terminated on the dendritic compartments, no such correlated changes occur. In this case, the coupled bursting neurons do not show the in-phase phase-locked state in any bursting mode. These results indicate that synchronization behaviour of bursting neurons significantly depends on the synaptic location, unlike a coupled system of regular spiking neurons.

Dynamic embedding of salience coding in hippocampal spatial maps

Hippocampal CA1 neurons participate in dynamic ensemble codes for space and memory. Prominent features of the environment are represented by an increased density of place cells, but cellular principles governing the formation and plasticity of such disproportionate maps are unknown. We thus imaged experience-dependent long-term changes in spatial representations at the cellular level in the CA1 deep sublayer in mice learning to navigate in a virtual-reality environment. The maps were highly dynamic but gradually stabilized as over-representations for motivational (reward) and environmental (landmark) salience emerged in different time courses by selective consolidation of relevant spatial representations. Relocation of the reward extensively reorganized pre-formed maps by a mechanism involving rapid recruitment of cells from the previous location followed by their re-stabilization, indicating that a subset of neurons encode reward-related information. The distinct properties of these CA1 cells may provide a substrate by which salient experience forms lasting and adaptable memory traces.

Medial Frontal Circuit Dynamics Represents Probabilistic Choices for Unfamiliar Sensory Experience

Neurons in medial frontal cortex (MFC) receive sensory signals that are crucial for decision-making behavior. While decision-making is easy for familiar sensory signals, it becomes more elaborative when sensory signals are less familiar to animals. It remains unclear how the population of neurons enables the coordinate transformation of such a sensory input into ambiguous choice responses. Furthermore, whether and how cortical oscillations temporally coordinate neuronal firing during this transformation has not been extensively studied. Here, we recorded neuronal population responses to familiar or unfamiliar auditory cues in rat MFC and computed their probabilistic evolution. Population responses to familiar sounds organize into neuronal trajectories containing multiplexed sensory, motor, and choice information. Unfamiliar sounds, in contrast, evoke trajectories that travel under the guidance of familiar paths and eventually diverge to unique decision states. Local field potentials exhibited beta- (15-20 Hz) and gamma-band (50-60 Hz) oscillations to which neuronal firing showed modest phase locking. Interestingly, gamma oscillation, but not beta oscillation, increased its power abruptly at some timepoint by which neural trajectories for different choices were near maximally separated. Our results emphasize the importance of the evolution of neural trajectories in rapid probabilistic decisions that utilize unfamiliar sensory information.

Automatic sorting system for large calcium imaging data

It has become possible to observe neural activity in freely moving animals via calcium imaging using a microscope, which could not be observed previously. However, it remains difficult to extract the dynamics of nerve cells from the recorded imaging data. In this study, we greatly improved the stability, and robustness of the cell activity estimation method via non-negative matrix decomposition with shrinkage estimation of the baseline. In addition, by improving the initial state of the iterative algorithm using a newly proposed method to extract the shape of the cell via image processing, a solution could be obtained with a small number of iterations. These methods were applied to artificial and real data, and their effectiveness was confirmed.

Effects of Voice Therapy on Laryngeal Motor Units During Phonation in Chronic Superior Laryngeal Nerve Paresis Dysphonia

OBJECTIVESnInjury to the superior laryngeal nerve can result in dysphonia, and in particular, loss of vocal range. It can be an especially difficult problem to address with either voice therapy or surgical intervention. Some clinicians and scientists suggest that combining vocal exercises with adjunctive neuromuscular electrical stimulation may enhance the positive effects of voice therapy for superior laryngeal nerve paresis (SLNP). However, the effects of voice therapy without neuromuscular electrical stimulation are unknown. The purpose of this retrospective study was to demonstrate the clinical effectiveness of voice therapy for rehabilitating chronic SLNP dysphonia in two subjects, using interspike interval (ISI) variability of laryngeal motor units by laryngeal electromyography (LEMG).nnnMETHODSnBoth patients underwent LEMG and were diagnosed with having 70% recruitment of the cricothyroid muscle, and 70% recruitment of the cricothyroid and thyroarytenoid muscles, respectively. Both patients received voice therapy for 3 months. Grade, roughness, breathiness, asthenia, and strain (GRBAS) scale, stroboscopic examination, aerodynamic assessment, acoustic analysis, and Voice Handicap Index-10 were performed before and after voice therapy. Mean ISI variability during steady phonation was also assessed.nnnRESULTSnAfter voice therapy, both patients showed improvement in vocal assessments by acoustic, aerodynamic, GRBAS, and Voice Handicap Index-10 analysis. LEMG indicated shortened ISIs in both cases.nnnCONCLUSIONSnThis study suggests that voice therapy for chronic SLNP dysphonia can be useful for improving SLNP and voice quality.

Synchronization Properties of Slow Cortical Oscillations

During slow-wave sleep, the brain shows slow oscillatory activity with remarkable long-range synchrony. Intracellular recordings show that the slow oscillation consists of two phases: an up state and a down state. Deriving the phase-response function of simplified neuronal systems, we examine the synchronization properties on slow oscillations between the up state and the down state. As a result, the strange interaction functions are found in some parameter ranges. These functions indicate that the states with the smaller phase lag than a critical value are all stable.