Kohyu Fukunishi

Dynamic characteristics of the auditory cortex of guinea pigs observed with multichannel optical recording

The spatiotemporal characteristics of neural activity in the guinea pig auditory cortex are investigated to determine their importance in neural processing and coding of the complex sounds. A multi-channel optical recording system has been developed for observing the cortical field of the mammalian brain in vivo. Using the voltage-sensitive dye: RH795, optical imaging was used to visualize neural activity in the guinea pig auditory cortex. Experimental results reveal a boomerang-shaped pattern of movement of activated neural cell regions for the evoked response to click as complex sounds. Parallel and sequential neural processing structure was observed. Although the exact frequency selectivities of single cells and tonotopical organization observed using microelectrode were not visible, the similar feature to the microelectrode evidences was imaged by extracting the strongly response field from the optical data.

The tonotopic representation in the auditory cortex of the guinea pig with optical recording

We examined spatio-temporal characteristics of the tonotopic representation in the auditory cortex of the anesthetized guinea pig with a multichannel optical method using voltage-sensitive dye. The response latencies increased, and the response field in the cortex became small when the stimulus intensity levels were decreased. Low frequencies were represented rostrally and high frequencies caudally. The two fields responding to different frequencies at higher intensity levels gradually overlapped as time after stimulus onset increased, though these response field did not overlap at the beginning of the response. These findings indicate that tonotopic representation varies dynamically with time after stimulus onset.

Temporal coding in the guinea-pig auditory cortex as revealed by optical imaging and its pattern-time-series analysis

The neural network structure of a guinea-pigs primary auditory cortex is estimated by applying pattern-time-series analysis to the auditory evoked responses. Spatiotemporal patterns in click-evoked responses, observed by optical recording with voltage-sensitive dye, are analyzed by time series analysis using a multivariable autoregressive (MAR) model. Oscillatory neural activities with a distribution of about 10 ∼ 40 Hz in the click-induced evoked responses are found in the cortical response field. The cortical regions where the distributed neural oscillations are generated are identified by pattern-time-series analysis. In addition, two types of cortico-cortical connections, unilateral and bilateral connections between the cortical points, are speculated to be the causes of oscillatory neural activity transfer. It can be said that the so-called synchronized neural oscillation, in the sense of coherency or correlation between the two evoked responses at the oscillatory frequency, does not necessarily represent real corticocortical neural connections at the evoked response points.

Spatio-temporal analyses of stimulus-evoked and spontaneous stochastic neural activity observed by optical imaging in guinea pig auditory cortex

Stimulus-evoked response in the cortex involves random neural activity besides the deterministic responses reproducible to the stimulus. Recently, we have developed a new bright optical system that enables us to investigate the spatio-temporal patterns of such stochastic activity in the guinea pig auditory cortex without averaging. We show that (1) the stochastic neural activity is evoked by a tone-stimulus in addition to the deterministic response, and spontaneous stochastic activity is also observed in a similar manner; (2) our statistical estimation of optical responses such as variance showed that the evoked stochastic activity was increased by the sound stimulus compared to the spontaneous activity; (3) both types of stochastic activity mainly display oscillatory behavior, in the frequency range of 5-11 Hz; (4) there are no significant differences between the stimulus-induced and spontaneous stochastic neural activity in our statistical analyses using the PSD (power-spectrum density) and the spatial correlation function; (5) the spatial area of the evoked stochastic activity is not strongly correlated with the tonotopical area of the deterministic response that is mainly localized in the caudal area of field A of the guinea pig auditory cortex. Thus, the stochastic neural activity existing in the stimulus response and the spontaneous activity in the auditory cortex are possibly generated by a common neural mechanism. These results were confirmed statistically using 27 animals.

Dendrite classification in rat hippocampal neurons according to signal propagation properties: Observation by multichannel optical recording in cultured neuronal networks

Abstract Two-dimensional neuronal networks were formed using a dissociated culture of rat hippocampal neurons on glass plates. Neural activity in response to pulse stimuli applied to the neurons by whole-cell clamp electrodes was observed with a 128-channel optical recording apparatus using a voltage-sensitive dye, RH482. Dendrites emerging from the somata of single neurons were classified according to two signal-transmission properties, those with lower conduction velocities (0.12±0.034 m/s, n=24) and those with very fast conduction velocity (faster than 1.0 m/s), by evaluating the conduction velocities of pulse responses. The distinction between these two properties seemed to be related to the morphological differences in input connectivity with the axons of neighboring neurons.

Multichannel optical recording of neuronal network activity and synaptic potentiation in dissociated cultures from rat hippocampus

The activity of neuronal networks formed by dissociated rat hippocampal neurons was observed with a 128-channel optical recording apparatus using an absorptive voltage-sensitive dye, RH482. Two-dimensional patterns of neural electrical events along somata and neurites in the networks were visualized as the responses to pulse stimuli applied to the somata of the presynaptic neurons by patch-clamp electrodes. Synaptic delay was analyzed from propagation delay of the responses along the neurites. Synaptic potentiation was also observed in postsynaptic responses that were amplified by a factor of 1.24 after tetanization. In contrast, presynaptic components were unaffected by the procedure. In the light of the present results, multichannel optical recording promises to promote our understanding of neuronal interactions at cellular level.

Species-Specific Vocalization of Guinea Pig Auditory Cortex Observed by Dye Optical Recording

Neural dynamics, or the neural behavior, of a cortical field has recently become one of the controversial matters related to the coding of sensory information, such as that of auditory, visual and somatosensory information in the mammalian cortex. Temporal neural coding such as dynamical neural assembly coding and dynamical population coding in the cortex have come to be considered much more important than was thought ten years ago (Buzsaki et al., eds. 1994), but despite extensive physiological study of the auditory cortex, we still do not know how complex sounds like natural sounds are encoded in the auditory cortex nor whether the specialized processing of vocal calls is carried out in the cortex (Pelleg-Toiba and Wollberg 1990, Wang et al. 1995, Eggermont 1995, McGee et al. 1996).

Cortical neural networks revealed by spatio-temporal neural observation and analysis on guinea pig auditory cortex

The neural network structure of the tonotopical organization in the guinea pig primary auditory cortex was studied by observing the evoked responses to tone bursts. A 128-channel optical recording system was used and the patterns were analyzed by time series analysis using on the multivariable autoregressive model. The results revealed the existence of synchronized oscillatory neural activities of about 30 Hz in the response regions to the tone bursts. In addition, the dominant sources (the cortical region) that generated the oscillation in every responding cortical region were identified. The existence of the mutual neural connections in the cortical field were identified along the tonotopical band from the dorsal to the ventral. On the other hand, the oscillation might be propagated from the source region to the cortical region in the caudal or the rostral direction. Thus neural networks of the tonotopicity in the cortex were estimated by spatio-temporal neural observation and analysis.

Equilibrium Plasma Position Control for a Large Tokamak Using Modern Control Theory

Optimal control techniques are applied to maintain the plasma in its equilibrium position in a large tokamak. The application of the state space equation to plasma position control is also discussed. Optimal controls with states, which are plasma current, OH coil current and vertical field current, and integrated plasma displacement feedbacks are formulated as linear, time-invariant expressions with quadratic performance indices. Effective plasma position control was obtained with integral state feedback in computer simulations for the JT-60. These control techniques will be applied to the JT-60.

Information Processing Mechanisms in the Mammalian Brain: Analysis of Spatio-temporal Neural Response in the Auditory Cortex

The mammalian brain can be regarded as a huge and complicated dynamical information processing system composed of single units called neurons or nerve cells. The mechanisms of brain function have, traditionally, been elucidated with the aid of single microelectrodes to measure the responses in single neurons. This approach, however, seems to be insufficient for identifying the complex dynamical system as the brain. An optical recording method, on the other hand, has made possible real-time multipoint measurement of the evoked neural activities distributed in the brain. This new recording method can be used to explore new mechanisms responsible for the dynamical neural processing activities of the brain. Such neural activities always exhibit nonlinear and nonstationary characteristics, and so straight forward application of any system identification theory to the neural system is inappropriate. On the other hand, many industrial dynamical systems, which involve nonstationary and nonlinear dynamical phenomena, exquisitely are modeled and controlled by using the extensive linear theory regarding to system identification and control. From this fact, there is a possibility that a linear identification theory such as time series analysis could be used in exploring the functioning of a nonlinear and nonstationary brain.