No abstract

Despite recent interest in using zebrafish in human disease studies, sparked by their economics, fecundity, easy handling, and homologies to humans, the electrophysiological tools or methods for zebrafish are still inaccessible. Although zebrafish exhibit more significant larval–adult duality than any other animal, most electrophysiological studies using zebrafish are biased by using larvae these days. The results of larval studies not only differ from those conducted with adults but also are unable to delicately manage electroencephalographic montages due to their small size. Hence, we enabled non-invasive long-term multichannel electroencephalographic recording on adult zebrafish using custom-designed electrodes and perfusion system. First, we exploited demonstration of long-term recording on pentylenetetrazole-induced seizure models, and the results were quantified. Second, we studied skin–electrode impedance, which is crucial to the quality of signals. Then, seizure propagations and gender differences in adult zebrafish were exhibited for the first time. Our results provide a new pathway for future neuroscience research using zebrafish by overcoming the challenges for aquatic organisms such as precision, serviceability, and continuous water seepage.

Recently, growing interest in implantable bionics and biochemical sensors spurred the research for developing non-conventional electronics with excellent device characteristics at low operation voltages and prolonged device stability under physiological conditions. Herein, we report high-performance aqueous electrolyte-gated thin-film transistors using a sol-gel amorphous metal oxide semiconductor and aqueous electrolyte dielectrics based on small ionic salts. The proper selection of channel material (i.e., indium-gallium-zinc-oxide) and precautious passivation of non-channel areas enabled the development of simple but highly stable metal oxide transistors manifested by low operation voltages within 0.5 V, high transconductance of ~1.0 mS, large current on-off ratios over 107, and fast inverter responses up to several hundred hertz without device degradation even in physiologically-relevant ionic solutions. In conjunction with excellent transistor characteristics, investigation of the electrochemical nature of the metal oxide-electrolyte interface may contribute to the development of a viable bio-electronic platform directly interfacing with biological entities in vivo.

Using memristor devices as synaptic connections has been suggested with different neural architectures in the literature. Most of the published works focus on simulating some plasticity mechanism for changing memristor conductance. This paper presents a neural architecture of a character recognition neural system using Al/Pr 0.7 Ca 0.3 MnO 3 (PCMO) memristors. The PCMO memristor has an inhomogeneous barrier at the aluminum and PCMO interface which gives rise to an asymmetrical behavior when moving from high resistance to low resistance and vice versa. This paper details the design and simulations for solving this asymmetrical memristor behavior. Also, a general memory read/write framework is used to describe the running and plasticity of neural systems. The proposed neural system can be produced in hardware using a small 1 K crossbar memristor grid and CMOS neural nodes as presented in the simulation results.

This paper presents an investigation of analog synapse characteristics of a PCMO-based interface switching device with varying electrode materials. In comparison with the filamentary switching device having only 1-bit storage and variability issues, the interface switching devices exhibit excellent electrical properties such as 5-bit (32-level) multi-level cell characteristics, wafer-scale switching uniformity, and scalability of the switching energy with device area. To improve data retention of the interface switching device, we propose an Mo electrode to increase the oxidation barrier height (~ 0.4 eV) that, in turn, significantly improves the retention time and pattern classification accuracy of neural networks.