Neuronal ensemble decoding using a dynamical maximum entropy model

As advances in neurotechnology allow us to access the ensemble activity of multiple neurons simultaneously, many neurophysiologic studies have investigated how to decode neuronal ensemble activity. Neuronal ensemble activity from different brain regions exhibits a variety of characteristics, requiring substantially different decoding approaches. Among various models, a maximum entropy decoder is known to exploit not only individual firing activity but also interactions between neurons, extracting information more accurately for the cases with persistent neuronal activity and/or low-frequency firing activity. However, it does not consider temporal changes in neuronal states and therefore would be susceptible to poor performance for nonstationary neuronal information processing. To address this issue, we develop a novel decoder that extends a maximum entropy decoder to take time-varying neural information into account. This decoder blends a dynamical system model of neural networks into the maximum entropy model to better suit for nonstationary circumstances. From two simulation studies, we demonstrate that the proposed dynamic maximum entropy decoder could cope well with time-varying information, which the conventional maximum entropy decoder could not achieve. The results suggest that the proposed decoder may be able to infer neural information more effectively as it exploits dynamical properties of underlying neural networks.open0

Spatio-Temporally Efficient Coding Assigns Functions to Hierarchical Structures of the Visual System

Hierarchical structures constitute a wide array of brain areas, including the visual system. One of the important questions regarding visual hierarchical structures is to identify computational principles for assigning functions that represent the external world to hierarchical structures of the visual system. Given that visual hierarchical structures contain both bottom-up and top-down pathways, the derived principles should encompass these bidirectional pathways. However, existing principles such as predictive coding do not provide an effective principle for bidirectional pathways. Therefore, we propose a novel computational principle for visual hierarchical structures as spatio-temporally efficient coding underscored by the efficient use of given resources in both neural activity space and processing time. This coding principle optimises bidirectional information transmissions over hierarchical structures by simultaneously minimising temporal differences in neural responses and maximising entropy in neural representations. Simulations demonstrated that the proposed spatio-temporally efficient coding was able to assign the function of appropriate neural representations of natural visual scenes to visual hierarchical structures. Furthermore, spatio-temporally efficient coding was able to predict well-known phenomena, including deviations in neural responses to unlearned inputs and bias in preferred orientations. Our proposed spatio-temporally efficient coding may facilitate deeper mechanistic understanding of the computational processes of hierarchical brain structures

Reversible Anionic Redox Activities in Conventional LiNi1/3 Co1/3 Mn1/3 O2 Cathodes.

Redox reactions of oxygen have been considered critical in controlling the electrochemical properties of lithium-excessive layered-oxide electrodes. However, conventional electrode materials without overlithiation remain the most practical. Typically, cationic redox reactions are believed to dominate the electrochemical processes in conventional electrodes. Herein, we show unambiguous evidence of reversible anionic redox reactions in LiNi1/3 Co1/3 Mn1/3 O2 . The typical involvement of oxygen through hybridization with transition metals is discussed, as well as the intrinsic oxygen redox process at high potentials, which is 75 % reversible during initial cycling and 63 % retained after 10 cycles. Our results clarify the reaction mechanism at high potentials in conventional layered electrodes involving both cationic and anionic reactions and indicate the potential of utilizing reversible oxygen redox reactions in conventional layered oxides for high-capacity lithium-ion batteries

Manganese based layered oxides with modulated electronic and thermodynamic properties for sodium ion batteries

Manganese based layered oxides have received increasing attention as cathode materials for sodium ion batteries due to their high theoretical capacities and good sodium ion conductivities. However, the Jahn–Teller distortion arising from the manganese (III) centers destabilizes the host structure and deteriorates the cycling life. Herein, we report that zinc-doped Na0.833[Li0.25Mn0.75]O2 can not only suppress the Jahn–Teller effect but also reduce the inherent phase separations. The reduction of manganese (III) amount in the zinc-doped sample, as predicted by first-principles calculations, has been confirmed by its high binding energies and the reduced octahedral structural variations. In the viewpoint of thermodynamics, the zinc-doped sample has lower formation energy, more stable ground states, and fewer spinodal decomposition regions than those of the undoped sample, all of which make it charge or discharge without any phase transition. Hence, the zinc-doped sample shows superior cycling performance, demonstrating that zinc doping is an effective strategy for developing high-performance layered cathode materials

Pre- and post-processing of cluster galaxies out to 5×R2005 \times R_{200}: The extreme case of A2670

We study galaxy interactions in the large scale environment around A2670, a massive ($M_{200}$ = $8.5 \pm 1.2~\times 10^{14} \mathrm{M_{\odot}}$) and interacting galaxy cluster at z = 0.0763. We first characterize the environment of the cluster out to 5$\times R_{200}$ and find a wealth of substructures, including the main cluster core, a large infalling group, and several other substructures. To study the impact of these substructures (pre-processing) and their accretion into the main cluster (post-processing) on the member galaxies, we visually examined optical images to look for signatures indicative of gravitational or hydrodynamical interactions. We find that $\sim 21$ % of the cluster galaxies have clear signs of disturbances, with most of those ($\sim60$ %) likely being disturbed by ram pressure. The number of ram-pressure stripping candidates found (101) in A2670 is the largest to date for a single system, and while they are more common in the cluster core, they can be found even at $> 4 \times R_{200}$, confirming cluster influence out to large radii. In support of a pre-processing scenario, most of the disturbed galaxies follow the substructures found, with the richest structures having more disturbed galaxies. Post-processing also seems plausible, as many galaxy-galaxy mergers are seen near the cluster core, which is not expected in relaxed clusters. In addition, there is a comparable fraction of disturbed galaxies in and outside substructures. Overall, our results highlight the complex interplay of gas stripping and gravitational interactions in actively assembling clusters up to $5\times R_{200}$, motivating wide-area studies in larger cluster samples.Comment: Accepted for publication in MNRA