Wataru Minoshima

Analysis of transition and reproducibility of spontaneous electrical activity pattern in a living neuronal network

The representations of outer world in the brain are considered to be undertaken by spatiotemporal activity patterns of neuronal circuits. In this study, we analyzed the transition of the internal states of the circuit of rat hippocampal neurons cultured on a multi-electrodes-array-dish. We analyzed transition of center of gravity and 64-dimensional feature-vectors of electrical activity patterns. Electrical activity pattern at a certain 5-ms-width-time-window was represented as a 64 dimensional “0–1” feature vector, and analyzed the stability. We confirmed that the reproducibility of the neuronal network activity increased during culture days. In addition, we applied similarity analysis to 64-dimensional feature vectors of neuronal activity. Using X-means algorithm, feature vectors were classified into “pattern repertories” based on the spatial distribution of activity.

Does Representation of Outer Objects in Living Neuronal Network Synthesize “the concept”?

Cultured living neuronal network with input-output-interface is the useful model to investigate how to autonomously create an internal object. Using living neuronal network cultured on a multielectrodes-array-dish, We elucidated the relationships between activity patterns evoked by inputs to 3 different electrodes. Activity patterns evoked by 3 different inputs were classified into several clusters including patterns evoked by 3 inputs at various ratios. Repeated input in stable temporal pattern led the clusters to fuse into single cluster. These results indicated that the Cultured living neuronal network (CLNN) separates and classifies the input from outer world and distinguished clusters are autonomously formed.

Relationship between evoked electrical responses and robotic behavior analyzed by Self-Organization Map

Toward neuroprosthetic technology, it is critical that a simple model system for interaction between brain and electric devices. For this purpose, we developed neurorobot system, Vitroid, equipped with a living neuronal network and a miniature moving robot as a body of the neurorobot. Self-Organization-Map (SOM) was employed as a generator for behavior of Vitroid. SOM was designed to map a high-dimensional feature vector to a 2-dimentional vector as the winner unit in output layer of SOM. Furthermore, neighboring units were assigned to resemble input vectors. Thus, SOM also performs pattern classifying analysis for inputted feature vector of neuronal activity. Cultured neuronal networks on Multi-Electrodes-Array (MEA) dish was alternately stimulated by two different electrodes. SOM mapped patterns induced by electrical stimulation to a 30 × 30 - 2D output layer. Only in the first step of the learning, SOM is forced to select a specific winner unit previously assigned in order to associate specific behaviors. We call this process “Seeding”. After seeding process, the winner-units correspond to the response patterns induced by two different stimuli were separately mapped. We confirmed that response patterns by two different electrical stimuli could be classified and they were almost stable. Furthermore, it revealed that spontaneous activity and evoked response shared the same patterns, suggesting that the internal autonomous activity is not only a noise, but is almost equivalent to a meaningful response. We also succeeded in collision avoidance of Vitroid by SOM-based behavior generator.

Relationship between inter-stimulus-intervals and intervals of autonomous activities in a neuronal network

To investigate relationships between neuronal network activity and electrical stimulus, we analyzed autonomous activity before and after electrical stimulus. Recordings of autonomous activity were performed using dissociated culture of rat hippocampal neurons on a multi-electrodes array (MEA) dish. Single stimulus and pared stimuli were applied to a cultured neuronal network. Single stimulus was applied every 1 min, and paired stimuli was performed by two sequential stimuli every 1 min. As a result, the patterns of synchronized activities of a neuronal network were changed after stimulus. Especially, long range synchronous activities were induced by paired stimuli. When 1 s inter-stimulus-intervals (ISI) and 1.5 s ISI paired stimuli are applied to a neuronal network, relatively long range synchronous activities expressed in case of 1.5 s ISI. Temporal synchronous activity of neuronal network is changed according to inter-stimulus-intervals (ISI) of electrical stimulus. In other words, dissociated neuronal network can maintain given information in temporal pattern and a certain type of an information maintenance mechanism was considered to be implemented in a semi-artificial dissociated neuronal network. The result is useful toward manipulation technology of neuronal activity in a brain system.

The effects of transient abolishment of electrical activity on dynamics in a dissociated neuronal network

The higher-order functions of brain depend on neuronal network electrical activity with complex spatiotemporal patterns. The background activity, such as spontaneous electrical activity observed in a dissociated culture of rat hippocampal neurons, is considered to be a fundamental component of internal state of the living neuronal network. The spontaneous activity has dynamic and complicated spatiotemporal activity patterns, thus it is uncertain that transiently discontinued activity pattern recovers to the same activity state. We elucidated the stability of the internal state of network activity after a transient abolishment of neuronal electrical activity. As a result, spontaneous activity pattern became to be bursting and intermittent pattern after the transient block of electrical activity. In addition, spontaneous neuronal activity increased in frequency. These modified patterns lasted for hours then gradually returned to the initial state. These results suggest that steady state of spontaneous activity is a result of complex equilibrium of interactions between neurons. Thus, it is required to consider the influence of the inhibition itself when the neural activity is stopped by pharmacological manipulations.

Stability of neuronal electrical activity pattern evoked by two inputs stimulation

Rat hippocampal dissociated culture on multi electrodes array dish (MED) is useful as minimalized brain model to investigation of principles of brain information processing. Rat hippocampal neuronal cells were cultured on MED with 64 microelectrodes and they reconstructed a complex network. We analyzed stability of neuronal electrical activity pattern after two distinct electrical stimuli. Distances between averaged spike pattern and spike pattern after electrical stimulation for summarizing the activity pattern to temporal axis direction and trial (sweep) axis direction. The distances of the analysis for summarized to temporal axis direction increased immediately after stimulation and after it, recovered to standard level. Moreover, stabilizing points emerge every 100 ms on both analysis for summarizing the activity pattern to temporal axis direction and trial axis direction. These results suggest that the neural network buttress by electrical stimulation and it be complex by two-point inputs.