Gaussian Information Bottleneck and the Non-Perturbative Renormalization Group

The renormalization group (RG) is a class of theoretical techniques used to explain the collective physics of interacting, many-body systems. It has been suggested that the RG formalism may be useful in finding and interpreting emergent low-dimensional structure in complex systems outside of the traditional physics context, such as in biology or computer science. In such contexts, one common dimensionality-reduction framework already in use is information bottleneck (IB), in which the goal is to compress an ``input'' signal X while maximizing its mutual information with some stochastic ``relevance'' variable Y. IB has been applied in the vertebrate and invertebrate processing systems to characterize optimal encoding of the future motion of the external world. Other recent work has shown that the RG scheme for the dimer model could be ``discovered'' by a neural network attempting to solve an IB-like problem. This manuscript explores whether IB and any existing formulation of RG are formally equivalent. A class of soft-cutoff non-perturbative RG techniques are defined by families of non-deterministic coarsening maps, and hence can be formally mapped onto IB, and vice versa. For concreteness, this discussion is limited entirely to Gaussian statistics (GIB), for which IB has exact, closed-form solutions. Under this constraint, GIB has a semigroup structure, in which successive transformations remain IB-optimal. Further, the RG cutoff scheme associated with GIB can be identified. Our results suggest that IB can be used to impose a notion of ``large scale'' structure, such as biological function, on an RG procedure.

Ontogenesis Model of the L2 Lexical Representation

We introduce the blueprint of the Ontogenesis Model of the L2 Lexical Representation (OM) that focuses on the development of lexical representations. The OM has three dimensions: linguistic domains (phonological, orthographic, and semantic), mappings between domains, and networks of lexical representations. The model assumes that fuzziness is a pervasive property of the L2 lexicon: most L2 lexical representations are low resolution and the ontogenetic curve of their development does not reach the optimum (i.e., the ultimate stage of their attainment with optimal encoding) in one or more dimensions. We review the findings on lexical processing and vocabulary training to show that the OM has a potential to provide an interpretation for the results that have been treated separately and to move us forward in building a comprehensive model of L2 lexical acquisition and processing.

Modulation Ranges of Different Sensations for Coding Electrically Evoked Tactile Sensory Feedback

The ability to perceive prosthetic grasping may enable amputees to better interact with external objects. This may require customized coding of multiple sensory feedback for each amputee. This study developed a protocol to determine optimal modulation ranges of sensations elicited by transcutaneous electrical nerve stimulation (TENS). These sensations that were referred to the lost fingers provided the possibility for restoring multi-modalities of sensory feedback for amputees with evoked tactile sensation (ETS) non-invasively. To match the restricted projected finger map area, smaller electrodes must be used to deliver electrical stimulation for multi-channel sensory information, which resulted in fewer types of sensations. Our protocol provided comprehensive information for optimal selection of amplitude and frequency in a personalized, pulse-width encoding paradigm. The good sensitivity for vibration and buzz in both able-bodied and amputee subjects suggested that perceptual intensity can be effectively modulated to convey sensory information via either of the sensations. The efficacy of this protocol in sensory coding for forearm amputees was demonstrated in finger-specific identification experiment. This protocol may allow customization of ETS-based sensory feedback with an optimal encoding strategy for individual amputees.

Modulation Ranges of Different Sensations for Coding Electrically Evoked Tactile Sensory Feedback

The ability to perceive prosthetic grasping may enable amputees to better interact with external objects. This may require customized coding of multiple sensory feedback for each amputee. This study developed a protocol to determine optimal modulation ranges of sensations elicited by transcutaneous electrical nerve stimulation (TENS). These sensations that were referred to the lost fingers provided the possibility for restoring multi-modalities of sensory feedback for amputees with evoked tactile sensation (ETS) non-invasively. To match the restricted projected finger map area, smaller electrodes must be used to deliver electrical stimulation for multi-channel sensory information, which resulted in fewer types of sensations. Our protocol provided comprehensive information for optimal selection of amplitude and frequency in a personalized, pulse-width encoding paradigm. The good sensitivity for vibration and buzz in both able-bodied and amputee subjects suggested that perceptual intensity can be effectively modulated to convey sensory information via either of the sensations. The efficacy of this protocol in sensory coding for forearm amputees was demonstrated in finger-specific identification experiment. This protocol may allow customization of ETS-based sensory feedback with an optimal encoding strategy for individual amputees.

Efficient storage and analysis of quantitative genomics data with the Dense Depth Data Dump (D4) format and d4tools

Modern DNA sequencing is used as a readout for diverse assays, with the count of aligned sequences, or “read depth”, serving as the quantitative signal for many underlying cellular phenomena. Despite wide use and thousands of datasets, existing formats used for the storage and analysis of read depths are limited with respect to both file size and analysis speed. For example, it is faster to recalculate sequencing depth from an alignment file than it is to analyze the text output from that calculation. We sought to improve on existing formats such as BigWig and compressed BED files by creating the Dense Depth Data Dump (D4) format and tool suite. The D4 format is adaptive in that it profiles a random sample of aligned sequence depth from the input BAM or CRAM file to determine an optimal encoding that often affords reductions in file size, while also enabling fast data access. We show that D4 uses less storage for both RNA-Seq and whole-genome sequencing and offers 3 to 440-fold speed improvements over existing formats for random access, aggregation and summarization. This performance enables scalable downstream analyses that would be otherwise difficult. The D4 tool suite (d4tools) is freely available under an MIT license at: https://github.com/38/d4-format.

Compression of dynamic tactile information in the human hand

A key problem in the study of the senses is to describe how sense organs extract perceptual information from the physics of the environment. We previously observed that dynamic touch elicits mechanical waves that propagate throughout the hand. Here, we show that these waves produce an efficient encoding of tactile information. The computation of an optimal encoding of thousands of naturally occurring tactile stimuli yielded a compact lexicon of primitive wave patterns that sparsely represented the entire dataset, enabling touch interactions to be classified with an accuracy exceeding 95%. The primitive tactile patterns reflected the interplay of hand anatomy with wave physics. Notably, similar patterns emerged when we applied efficient encoding criteria to spiking data from populations of simulated tactile afferents. This finding suggests that the biomechanics of the hand enables efficient perceptual processing by effecting a preneuronal compression of tactile information.