Neural Activity and Information Processing Capacity of Neuronal Culture.

Whether artificial or living, neural networks perform tremendously diverse kinds of information processing, such as visual feature extraction, speech recognition, motor control, and so on. Some studies have evaluated the computational ability of living neural networks based on the performances of specific tasks, yet could not comprehensively grasp the versatile ability. In this study, we investigated dissociated culture of neurons as a physical reservoir, which generates diverse outputs through linear regression, or readout, of the dynamical states. Based on the theory of reservoir computing, the potential computational capabilities of neuronal culture were evaluated by the information processing capacity (IPC), which indicates how a target function can be achieved from the given dynamics. As a result, we found that the neuronal culture exhibited significant IPC and that IPC varied with the inter-step interval (ISI), the time step of reservoir computing. The cultures exhibited a memory capacity of 10 time steps for computation, and this memory capacity decayed at an ISI of 5 ms or shorter. In addition, the IPC had a significant positive correlation with the intensity of the evoked response relative to spontaneous activity. The multiple regression model with evoked response and ISI showed the positive effect of evoked response and 30 ms as the best ISI for IPC. These results suggest that the distinct evoked response and the optimal time step to interact with the neuronal culture are key factors to uncover computational resources from the neuronal system.

Current reinforcement model reproduces center-in-center vein trajectory of Physarum polycephalum.

Vein networks span the whole body of the amoeboid organism in the plasmodial slime mould Physarum polycephalum, and the network topology is rearranged within an hour in response to spatio-temporal variations of the environment. It has been reported that this tube morphogenesis is capable of solving mazes, and a mathematical model, named the 'current reinforcement rule', was proposed based on the adaptability of the veins. Although it is known that this model works well for reproducing some key characters of the organism's maze-solving behaviour, one important issue is still open: In the real organism, the thick veins tend to trace the shortest possible route by cutting the corners at the turn of corridors, following a center-in-center trajectory, but it has not yet been examined whether this feature also appears in the mathematical model, using corridors of finite width. In this report, we confirm that the mathematical model reproduces the center-in-center trajectory of veins around corners observed in the maze-solving experiment.

A multichannel current stimulator chip for spatiotemporal pattern stimulation of neural tissues.

We developed a prototype very-large-scale integration chip of a multichannel current stimulator for stimulating neural tissues by utilizing 0.25 µm high-voltage complementary metal-oxide-semiconductor technology. Our designed chip has 20 output channels that are driven by five current buffers arranged in parallel; each buffer controls four output channels in time-sharing mode. The amplitude of a stimulation pulse can be controlled within a range of approximately ±100 µA/phase in each output channel. The stimulus parameters, e.g., amplitude and duration, are controlled separately for each channel by digital codes stored in built-in registers. Combinations of anode and cathode electrodes to pass the current can be changed online. We integrated our stimulator chip with a multielectrode array and studied the neuronal responses to multichannel current stimulations with various temporal patterns in mouse brain slices.