A sulfonated polyaniline (SPAN) organic electrochemical network device (OEND) is fabricated using a simple drop‐casting method on multiple Au electrodes for use in reservoir computing (RC). The SPAN network has humidity‐dependent electrical properties. Under high humidity, the SPAN OEND exhibits mainly ionic conduction, including charging of an electric double layer and ionic diffusion. The nonlinearity and hysteresis of the current–voltage characteristics progressively increase with increasing humidity. The rich dynamic output behavior indicates wide variations for each electrode, which improves the RC performance because of the disordered network. For RC, waveform generation and short‐term memory tasks are realized by a linear combination of outputs. The waveform task accuracy and memory capacity calculated from a short‐term memory task reach 90% and 33.9, respectively. Improved spoken‐digit classification is realized with 60% accuracy by only 12 outputs, demonstrating that the SPAN OEND can manage time series dynamic data operation in RC owing to a combination of rich dynamic and nonlinear electronic properties. The results suggest that SPAN‐based electrochemical systems can be applied for material‐based computing, by exploiting their intrinsic physicochemical behavior.

Hardware‐based machine intelligence with the network architecture of reservoir computing (RC) is gaining interest because of its biological computational resemblance along with an easy and efficient neural network training approach. Herein, such a physical RC (in‐materio RC) platform consisting of a recurrent network formed by the single‐walled carbon nanotube (SWNT)–porphyrin polyoxometalate (Por–POM) complex is demonstrated. The network architecture executes the fundamental reservoir properties of nonlinearity, higher harmonic generation, and 1/fγ power law information processing ability. Based on these functionalities, an RC benchmark task of waveform generation is performed where the device achieves maximum fitting accuracy of 99.4%. Furthermore, a supervised object classification task based on a one‐hot vector target is also executed using Toyota Human Support Robot tactile inputs. The successful classification of objects of different hardness is enhanced when the device output response follows the 1/fγ power law of maximized information processing.

Reservoir computing (RC), a low-power computational framework derived from recurrent neural networks, is suitable for temporal/sequential data processing. Here, we report the development of RC devices utilizing Ag–Ag2S core–shell nanoparticles (NPs), synthesized by a simple wet chemical protocol, as the reservoir layer. We examined the NP-based reservoir layer for the required properties of RC hardware, such as echo state property, and then performed the benchmark tasks. Our study on NP-based reservoirs highlighted the importance of the dynamics between the NPs as indicated by the rich high dimensionality due to the echo state property. These dynamics affected the accuracy (up to 99%) of the target waveforms that were generated with a low number of readout channels. Our study demonstrates the great potential of Ag–Ag2S NPs for the development of next-generation RC hardware.

Leucine is a major amino acid in nutrients and proteins and is also an important precursor of higher alcohols during brewing. In Saccharomyces cerevisiae, leucine uptake is mediated by multiple amino acid permeases, including the high-affinity leucine permease Bap2. Although BAP2 transcription has been extensively analyzed, the mechanisms by which a substrate is recognized and moves through the permease remain unknown. Recently, we determined 15 amino acid residues required for Tat2-mediated tryptophan import. Here we introduced homologous mutations into Bap2 amino acid residues and showed that 7 residues played a role in leucine import. Residues I109/G110/T111 and E305 were located within the putative α-helix break in TMD1 and TMD6, respectively, according to the structurally homologous Escherichia coli arginine/agmatine antiporter AdiC. Upon leucine binding, these α-helix breaks were assumed to mediate a conformational transition in Bap2 from an outward-open to a substrate-binding occluded state. Residues Y336 (TMD7) and Y181 (TMD3) were located near I109 and E305, respectively. Bap2-mediated leucine import was inhibited by some amino acids according to the following order of severity: phenylalanine, leucine>isoleucine>methionine, tyrosine>valine>tryptophan; histidine and asparagine had no effect. Moreover, this order of severity clearly coincided with the logP values (octanol-water partition coefficients) of all amino acids except tryptophan. This result suggests that the substrate partition efficiency to the buried Bap2 binding pocket is the primary determinant of substrate specificity rather than structural amino acid side chain recognition.