Stimulus-selective lateral signaling between olfactory afferents enables parallel encoding of distinct CO2 dynamics

An important problem in sensory processing is how lateral interactions that mediate the integration of information across sensory channels function with respect to their stimulus tunings. We demonstrate a novel form of stimulus-selective crosstalk between olfactory channels that occurs between primary olfactory receptor neurons (ORNs). Neurotransmitter release from ORNs can be driven by two distinct sources of excitation, feedforward activity derived from the odorant receptor and lateral input originating from specific subsets of other ORNs. Consequently, levels of presynaptic release can become dissociated from firing rate. Stimulus-selective lateral signaling results in the distributed representation of CO2, a behaviorally important environmental cue that elicits spiking in only a single ORN class, across multiple olfactory channels. Different CO2-responsive channels preferentially transmit distinct stimulus dynamics, thereby expanding the coding bandwidth for CO2. These results generalize to additional odors and olfactory channels, revealing a subnetwork of lateral interactions between ORNs that reshape the spatial and temporal structure of odor representations in a stimulus-specific manner.One Sentence SummaryA novel subnetwork of stimulus-selective lateral interactions between primary olfactory sensory neurons enables new sensory computations.

Synthesis of Uniformly Excited Concentric Ring Arrays by the Strategy of Partial Density Tapering and the Algorithm of Differential Evolution

A new strategy of density tapering called the partial density tapering (PDT) accompanied with the algorithm of differential evolution (DE) is proposed to suppress the peak sidelobe level (PSL) of uniform excited concentric ring arrays (UECRA) with isotropic elements. Through performing the PDT, a sound starting solution for DE can be generated. Then, the ring filling factor (RFF) is introduced so that the optimization of the number of elements can be transformed into the optimization of RFFs within the tapered thresholds, and thereby the real coding can be directly used with respect to the consideration of parallel encoding strategy. Finally, the UECRA featuring improved PSL performance can be obtained by limited runs of conventional DE. Several numerical instances for UECRA, with aperture sizes ranging from small to large scale, confirmed the outperformance of the proposed method.

Error-Vulnerable Pattern-Aware Binary-to-Ternary Data Mapping for Improving Storage Density of 3LC Phase Change Memory

Multi-level cell (MLC) phase-change memory (PCM) is an attractive solution for next-generation memory that is composed of resistance-based nonvolatile devices. MLC PCM is superior to dynamic random-access memory (DRAM) with regard to scalability and leakage power. Therefore, various studies have focused on the feasibility of MLC PCM-based main memory. The key challenges in replacing DRAM with MLC PCM are low reliability, limited lifetime, and long write latency, which are predominantly affected by the most error-vulnerable data pattern. Based on the physical characteristics of the PCM, where the reliability depends on the data pattern, a tri-level-cell (3LC) PCM has significantly higher performance and lifetime than a four-level-cell (4LC) PCM. However, a storage density is limited by binary-to-ternary data mapping. This paper introduces error-vulnerable pattern-aware binary-to-ternary data mapping utilizing 3LC PCM without an error-correction code (ECC) to enhance the storage density. To mitigate the storage density loss caused by the 3LC PCM, a two-way encoding is applied. The performance degradation is minimized through parallel encoding. The experimental results demonstrate that the proposed method improves the storage density by 17.9%. Additionally, the lifetime and performance are enhanced by 36.1% and 38.8%, respectively, compared with those of a 4LC PCM with an ECC.

Parallel encoding of information into visual short-term memory

Humans can temporarily retain information in their highly limited short-term memory. Traditionally, objects are thought to be attentionally selected and committed to short-term memory one-by-one. However, few studies directly test this serial encoding assumption. Here, we demonstrate that information from separate objects can be encoded into short-term memory in parallel. We developed models of serial and parallel encoding that describe probabilities of items being present in short-term memory throughout the encoding process, and tested them in a whole-report design. Empirical data from four experiments in healthy individuals were fitted best by the parallel encoding model, even when items were presented unilaterally (processed within one hemisphere). Our results demonstrate that information from several items can be attentionally selected and consequently encoded into short-term memory simultaneously. This suggests the popular feature integration theory needs to be reformulated to account for parallel encoding, and provides important boundaries for computational models of short-term memory.

Separate mesocortical and mesolimbic pathways encode effort and reward learning signals

Optimal decision making mandates organisms learn the relevant features of choice options. Likewise, knowing how much effort we should expend can assume paramount importance. A mesolimbic network supports reward learning, but it is unclear whether other choice features, such as effort learning, rely on this same network. Using computational fMRI, we show parallel encoding of effort and reward prediction errors (PEs) within distinct brain regions, with effort PEs expressed in dorsomedial prefrontal cortex and reward PEs in ventral striatum. We show a common mesencephalic origin for these signals evident in overlapping, but spatially dissociable, dopaminergic midbrain regions expressing both types of PE. During action anticipation, reward and effort expectations were integrated in ventral striatum, consistent with a computation of an overall net benefit of a stimulus. Thus, we show that motivationally relevant stimulus features are learned in parallel dopaminergic pathways, with formation of an integrated utility signal at choice.