Discriminative Learning of Receptive Fields from Responses to Non-Gaussian Stimulus Ensembles

Analysis of sensory neurons'' processing characteristics requires simultaneous measurement of presented stimuli and concurrent spike responses. The functional transformation from high-dimensional stimulus space to the binary space of spike and non-spike responses is commonly described with linear-nonlinear models, whose linear filter component describes the neuron''s receptive field. From a machine learning perspective, this corresponds to the binary classification problem of discriminating spike-eliciting from non-spike-eliciting stimulus examples. The classification-based receptive field (CbRF) estimation method proposed here adapts a linear large-margin classifier to optimally predict experimental stimulus-response data and subsequently interprets learned classifier weights as the neuron''s receptive field filter. Computational learning theory provides a theoretical framework for learning from data and guarantees optimality in the sense that the risk of erroneously assigning a spike-eliciting stimulus example to the non-spike class (and vice versa) is minimized. Efficacy of the CbRF method is validated with simulations and for auditory spectro-temporal receptive field (STRF) estimation from experimental recordings in the auditory midbrain of Mongolian gerbils. Acoustic stimulation is performed with frequency-modulated tone complexes that mimic properties of natural stimuli, specifically non-Gaussian amplitude distribution and higher-order correlations. Results demonstrate that the proposed approach successfully identifies correct underlying STRFs, even in cases where second-order methods based on the spike-triggered average (STA) do not. Applied to small data samples, the method is shown to converge on smaller amounts of experimental recordings and with lower estimation variance than the generalized linear model and recent information theoretic methods. Thus, CbRF estimation may prove useful for investigation of neuronal processes in response to natural stimuli and in settings where rapid adaptation is induced by experimental design.     

Coordinating Anions “to the Rescue” of the Lithium Ion Mobility in Ternary Solid Polymer Electrolytes Plasticized With Ionic Liquids

Lithium salts with low coordinating anions such as bis(trifluoromethanesulfonyl)imide (TFSI) have been the state-of-the-art for polyethylene oxide (PEO)-based “dry” polymer electrolytes for 3 decades. Plasticizing PEO with TFSI-based ionic liquids (ILs) to form ternary solid polymer electrolytes (TSPEs) increases conductivity and Li⁺ diffusivity. However, the Li⁺ transport mechanism is unaffected compared to their “dry” counterparts and is essentially coupled to the dynamics of the polymer host matrix, which limits Li⁺ transport improvement. Thus, a paradigm shift is hereby suggested: the utilization of more coordinating anions such as trifluoromethanesulfonyl-N-cyanoamide (TFSAM), able to compete with PEO for Li⁺ solvation, to accelerate the Li⁺ transport and reach a higher Li⁺ transference number. The Li–TFSAM interaction in binary and ternary TFSAM-based electrolytes is probed by experimental methods and discussed in the context of recent computational results. In PEO-based TSPEs, TFSAM drastically accelerates the Li⁺ transport (increases Li⁺ transference number by a factor 6 and the Li⁺ conductivity by 2–3) and computer simulations reveal that lithium dynamics are effectively re-coupled from polymer to anion dynamics. Last, this concept of coordinating anions in TSPEs is successfully applied in LFP||Li metal cells leading to enhanced capacity retention (86% after 300 cycles) and an improved rate performance at 2C.