Metabolite-Sensing G Protein-Coupled Receptors-Facilitators of Diet-Related Immune Regulation.

Nutrition and the gut microbiome regulate many systems, including the immune, metabolic, and nervous systems. We propose that the host responds to deficiency (or sufficiency) of dietary and bacterial metabolites in a dynamic way, to optimize responses and survival. A family of G protein-coupled receptors (GPCRs) termed the metabolite-sensing GPCRs bind to various metabolites and transmit signals that are important for proper immune and metabolic functions. Members of this family include GPR43, GPR41, GPR109A, GPR120, GPR40, GPR84, GPR35, and GPR91. In addition, bile acid receptors such as GPR131 (TGR5) and proton-sensing receptors such as GPR65 show similar features. A consistent feature of this family of GPCRs is that they provide anti-inflammatory signals; many also regulate metabolism and gut homeostasis. These receptors represent one of the main mechanisms whereby the gut microbiome affects vertebrate physiology, and they also provide a link between the immune and metabolic systems. Insufficient signaling through one or more of these metabolite-sensing GPCRs likely contributes to human diseases such as asthma, food allergies, type 1 and type 2 diabetes, hepatic steatosis, cardiovascular disease, and inflammatory bowel diseases.

Decoding dynamic visual scenes across the brain hierarchy.

Understanding the computational mechanisms that underlie the encoding and decoding of environmental stimuli is a crucial investigation in neuroscience. Central to this pursuit is the exploration of how the brain represents visual information across its hierarchical architecture. A prominent challenge resides in discerning the neural underpinnings of the processing of dynamic natural visual scenes. Although considerable research efforts have been made to characterize individual components of the visual pathway, a systematic understanding of the distinctive neural coding associated with visual stimuli, as they traverse this hierarchical landscape, remains elusive. In this study, we leverage the comprehensive Allen Visual Coding-Neuropixels dataset and utilize the capabilities of deep learning neural network models to study neural coding in response to dynamic natural visual scenes across an expansive array of brain regions. Our study reveals that our decoding model adeptly deciphers visual scenes from neural spiking patterns exhibited within each distinct brain area. A compelling observation arises from the comparative analysis of decoding performances, which manifests as a notable encoding proficiency within the visual cortex and subcortical nuclei, in contrast to a relatively reduced encoding activity within hippocampal neurons. Strikingly, our results unveil a robust correlation between our decoding metrics and well-established anatomical and functional hierarchy indexes. These findings corroborate existing knowledge in visual coding related to artificial visual stimuli and illuminate the functional role of these deeper brain regions using dynamic stimuli. Consequently, our results suggest a novel perspective on the utility of decoding neural network models as a metric for quantifying the encoding quality of dynamic natural visual scenes represented by neural responses, thereby advancing our comprehension of visual coding within the complex hierarchy of the brain.

Diet, commensal microbiota-derived extracellular vesicles, and host immunity.

The gut microbiota has co-evolved with its host, and commensal bacteria can influence both the host's immune development and function. Recently, a role has emerged for bacterial extracellular vesicles (BEVs) as potent immune modulators. BEVs are nanosized membrane vesicles produced by all bacteria, possessing the membrane characteristics of the originating bacterium and carrying an internal cargo that may include nucleic acid, proteins, lipids, and metabolites. Thus, BEVs possess multiple avenues for regulating immune processes, and have been implicated in allergic, autoimmune, and metabolic diseases. BEVs are biodistributed locally in the gut, and also systemically, and thus have the potential to affect both the local and systemic immune responses. The production of gut microbiota-derived BEVs is regulated by host factors such as diet and antibiotic usage. Specifically, all aspects of nutrition, including macronutrients (protein, carbohydrates, and fat), micronutrients (vitamins and minerals), and food additives (the antimicrobial sodium benzoate), can regulate BEV production. This review summarizes current knowledge of the powerful links between nutrition, antibiotics, gut microbiota-derived BEV, and their effects on immunity and disease development. It highlights the potential of targeting or utilizing gut microbiota-derived BEV as a therapeutic intervention.

Diverse role of NMDA receptors for dendritic integration of neural dynamics.

Neurons, represented as a tree structure of morphology, have various distinguished branches of dendrites. Different types of synaptic receptors distributed over dendrites are responsible for receiving inputs from other neurons. NMDA receptors (NMDARs) are expressed as excitatory units, and play a key physiological role in synaptic function. Although NMDARs are widely expressed in most types of neurons, they play a different role in the cerebellar Purkinje cells (PCs). Utilizing a computational PC model with detailed dendritic morphology, we explored the role of NMDARs at different parts of dendritic branches and regions. We found somatic responses can switch from silent, to simple spikes and complex spikes, depending on specific dendritic branches. Detailed examination of the dendrites regarding their diameters and distance to soma revealed diverse response patterns, yet explain two firing modes, simple and complex spike. Taken together, these results suggest that NMDARs play an important role in controlling excitability sensitivity while taking into account the factor of dendritic properties. Given the complexity of neural morphology varying in cell types, our work suggests that the functional role of NMDARs is not stereotyped but highly interwoven with local properties of neuronal structure.

Myeloperoxidase Gene Deletion Causes Drastic Microbiome Shifts in Mice and Does Not Mitigate Dextran Sodium Sulphate-Induced Colitis.

Neutrophil-myeloperoxidase (MPO) is a heme-containing peroxidase which produces excess amounts of hypochlorous acid during inflammation. While pharmacological MPO inhibition mitigates all indices of experimental colitis, no studies have corroborated the role of MPO using knockout (KO) models. Therefore, we investigated MPO deficient mice in a murine model of colitis. Wild type (Wt) and MPO-deficient mice were treated with dextran sodium sulphate (DSS) in a chronic model of experimental colitis with three acute cycles of DSS-induced colitis over 63 days, emulating IBD relapse and remission cycles. Mice were immunologically profiled at the gut muscoa and the faecal microbiome was assessed via 16S rRNA amplicon sequencing. Contrary to previous pharmacological antagonist studies targeting MPO, MPO-deficient mice showed no protection from experimental colitis during cyclical DSS-challenge. We are the first to report drastic faecal microbiota shifts in MPO-deficient mice, showing a significantly different microbiome profile on Day 1 of treatment, with a similar shift and distinction on Day 29 (half-way point), via qualitative and quantitative descriptions of phylogenetic distances. Herein, we provide the first evidence of substantial microbiome shifts in MPO-deficiency, which may influence disease progression. Our findings have significant implications for the utility of MPO-KO mice in investigating disease models.

Simple model for encoding natural images by retinal ganglion cells with nonlinear spatial integration.

A central goal in sensory neuroscience is to understand the neuronal signal processing involved in the encoding of natural stimuli. A critical step towards this goal is the development of successful computational encoding models. For ganglion cells in the vertebrate retina, the development of satisfactory models for responses to natural visual scenes is an ongoing challenge. Standard models typically apply linear integration of visual stimuli over space, yet many ganglion cells are known to show nonlinear spatial integration, in particular when stimulated with contrast-reversing gratings. We here study the influence of spatial nonlinearities in the encoding of natural images by ganglion cells, using multielectrode-array recordings from isolated salamander and mouse retinas. We assess how responses to natural images depend on first- and second-order statistics of spatial patterns inside the receptive field. This leads us to a simple extension of current standard ganglion cell models. We show that taking not only the weighted average of light intensity inside the receptive field into account but also its variance over space can partly account for nonlinear integration and substantially improve response predictions of responses to novel images. For salamander ganglion cells, we find that response predictions for cell classes with large receptive fields profit most from including spatial contrast information. Finally, we demonstrate how this model framework can be used to assess the spatial scale of nonlinear integration. Our results underscore that nonlinear spatial stimulus integration translates to stimulation with natural images. Furthermore, the introduced model framework provides a simple, yet powerful extension of standard models and may serve as a benchmark for the development of more detailed models of the nonlinear structure of receptive fields.

Decoding Pixel-Level Image Features From Two-Photon Calcium Signals of Macaque Visual Cortex.

Images of visual scenes comprise essential features important for visual cognition of the brain. The complexity of visual features lies at different levels, from simple artificial patterns to natural images with different scenes. It has been a focus of using stimulus images to predict neural responses. However, it remains unclear how to extract features from neuronal responses. Here we address this question by leveraging two-photon calcium neural data recorded from the visual cortex of awake macaque monkeys. With stimuli including various categories of artificial patterns and diverse scenes of natural images, we employed a deep neural network decoder inspired by image segmentation technique. Consistent with the notation of sparse coding for natural images, a few neurons with stronger responses dominated the decoding performance, whereas decoding of ar tificial patterns needs a large number of neurons. When natural images using the model pretrained on artificial patterns are decoded, salient features of natural scenes can be extracted, as well as the conventional category information. Altogether, our results give a new perspective on studying neural encoding principles using reverse-engineering decoding strategies.

Dissecting cascade computational components in spiking neural networks.

Finding out the physical structure of neuronal circuits that governs neuronal responses is an important goal for brain research. With fast advances for large-scale recording techniques, identification of a neuronal circuit with multiple neurons and stages or layers becomes possible and highly demanding. Although methods for mapping the connection structure of circuits have been greatly developed in recent years, they are mostly limited to simple scenarios of a few neurons in a pairwise fashion; and dissecting dynamical circuits, particularly mapping out a complete functional circuit that converges to a single neuron, is still a challenging question. Here, we show that a recent method, termed spike-triggered non-negative matrix factorization (STNMF), can address these issues. By simulating different scenarios of spiking neural networks with various connections between neurons and stages, we demonstrate that STNMF is a persuasive method to dissect functional connections within a circuit. Using spiking activities recorded at neurons of the output layer, STNMF can obtain a complete circuit consisting of all cascade computational components of presynaptic neurons, as well as their spiking activities. For simulated simple and complex cells of the primary visual cortex, STNMF allows us to dissect the pathway of visual computation. Taken together, these results suggest that STNMF could provide a useful approach for investigating neuronal systems leveraging recorded functional neuronal activity.

Regulating synchronous oscillations of cerebellar granule cells by different types of inhibition.

Synchronous oscillations in neural populations are considered being controlled by inhibitory neurons. In the granular layer of the cerebellum, two major types of cells are excitatory granular cells (GCs) and inhibitory Golgi cells (GoCs). GC spatiotemporal dynamics, as the output of the granular layer, is highly regulated by GoCs. However, there are various types of inhibition implemented by GoCs. With inputs from mossy fibers, GCs and GoCs are reciprocally connected to exhibit different network motifs of synaptic connections. From the view of GCs, feedforward inhibition is expressed as the direct input from GoCs excited by mossy fibers, whereas feedback inhibition is from GoCs via GCs themselves. In addition, there are abundant gap junctions between GoCs showing another form of inhibition. It remains unclear how these diverse copies of inhibition regulate neural population oscillation changes. Leveraging a computational model of the granular layer network, we addressed this question to examine the emergence and modulation of network oscillation using different types of inhibition. We show that at the network level, feedback inhibition is crucial to generate neural oscillation. When short-term plasticity was equipped on GoC-GC synapses, oscillations were largely diminished. Robust oscillations can only appear with additional gap junctions. Moreover, there was a substantial level of cross-frequency coupling in oscillation dynamics. Such a coupling was adjusted and strengthened by GoCs through feedback inhibition. Taken together, our results suggest that the cooperation of distinct types of GoC inhibition plays an essential role in regulating synchronous oscillations of the GC population. With GCs as the sole output of the granular network, their oscillation dynamics could potentially enhance the computational capability of downstream neurons.

Modulation of the dynamics of cerebellar Purkinje cells through the interaction of excitatory and inhibitory feedforward pathways.

The dynamics of cerebellar neuronal networks is controlled by the underlying building blocks of neurons and synapses between them. For which, the computation of Purkinje cells (PCs), the only output cells of the cerebellar cortex, is implemented through various types of neural pathways interactively routing excitation and inhibition converged to PCs. Such tuning of excitation and inhibition, coming from the gating of specific pathways as well as short-term plasticity (STP) of the synapses, plays a dominant role in controlling the PC dynamics in terms of firing rate and spike timing. PCs receive cascade feedforward inputs from two major neural pathways: the first one is the feedforward excitatory pathway from granule cells (GCs) to PCs; the second one is the feedforward inhibition pathway from GCs, via molecular layer interneurons (MLIs), to PCs. The GC-PC pathway, together with short-term dynamics of excitatory synapses, has been a focus over past decades, whereas recent experimental evidence shows that MLIs also greatly contribute to controlling PC activity. Therefore, it is expected that the diversity of excitation gated by STP of GC-PC synapses, modulated by strong inhibition from MLI-PC synapses, can promote the computation performed by PCs. However, it remains unclear how these two neural pathways are interacted to modulate PC dynamics. Here using a computational model of PC network installed with these two neural pathways, we addressed this question to investigate the change of PC firing dynamics at the level of single cell and network. We show that the nonlinear characteristics of excitatory STP dynamics can significantly modulate PC spiking dynamics mediated by inhibition. The changes in PC firing rate, firing phase, and temporal spike pattern, are strongly modulated by these two factors in different ways. MLIs mainly contribute to variable delays in the postsynaptic action potentials of PCs while modulated by excitation STP. Notably, the diversity of synchronization and pause response in the PC network is governed not only by the balance of excitation and inhibition, but also by the synaptic STP, depending on input burst patterns. Especially, the pause response shown in the PC network can only emerge with the interaction of both pathways. Together with other recent findings, our results show that the interaction of feedforward pathways of excitation and inhibition, incorporated with synaptic short-term dynamics, can dramatically regulate the PC activities that consequently change the network dynamics of the cerebellar circuit.

Dynamic Spatiotemporal Pattern Recognition With Recurrent Spiking Neural Network.

Our real-time actions in everyday life reflect a range of spatiotemporal dynamic brain activity patterns, the consequence of neuronal computation with spikes in the brain. Most existing models with spiking neurons aim at solving static pattern recognition tasks such as image classification. Compared with static features, spatiotemporal patterns are more complex due to their dynamics in both space and time domains. Spatiotemporal pattern recognition based on learning algorithms with spiking neurons therefore remains challenging. We propose an end-to-end recurrent spiking neural network model trained with an algorithm based on spike latency and temporal difference backpropagation. Our model is a cascaded network with three layers of spiking neurons where the input and output layers are the encoder and decoder, respectively. In the hidden layer, the recurrently connected neurons with transmission delays carry out high-dimensional computation to incorporate the spatiotemporal dynamics of the inputs. The test results based on the data sets of spiking activities of the retinal neurons show that the proposed framework can recognize dynamic spatiotemporal patterns much better than using spike counts. Moreover, for 3D trajectories of a human action data set, the proposed framework achieves a test accuracy of 83.6% on average. Rapid recognition is achieved through the learning methodology-based on spike latency and the decoding process using the first spike of the output neurons. Taken together, these results highlight a new model to extract information from activity patterns of neural computation in the brain and provide a novel approach for spike-based neuromorphic computing.

High-Fiber Diet and Acetate Supplementation Change the Gut Microbiota and Prevent the Development of Hypertension and Heart Failure in Hypertensive Mice.

BACKGROUND: Dietary intake of fruit and vegetables is associated with lower incidence of hypertension, but the mechanisms involved have not been elucidated. Here, we evaluated the effect of a high-fiber diet and supplementation with the short-chain fatty acid acetate on the gut microbiota and the prevention of cardiovascular disease. METHODS: Gut microbiome, cardiorenal structure/function, and blood pressure were examined in sham and mineralocorticoid excess-treated mice with a control diet, high-fiber diet, or acetate supplementation. We also determined the renal and cardiac transcriptome of mice treated with the different diets. RESULTS: We found that high consumption of fiber modified the gut microbiota populations and increased the abundance of acetate-producing bacteria independently of mineralocorticoid excess. Both fiber and acetate decreased gut dysbiosis, measured by the ratio of Firmicutes to Bacteroidetes, and increased the prevalence of Bacteroides acidifaciens. Compared with mineralocorticoid-excess mice fed a control diet, both high-fiber diet and acetate supplementation significantly reduced systolic and diastolic blood pressures, cardiac fibrosis, and left ventricular hypertrophy. Acetate had similar effects and markedly reduced renal fibrosis. Transcriptome analyses showed that the protective effects of high fiber and acetate were accompanied by the downregulation of cardiac and renal Egr1, a master cardiovascular regulator involved in cardiac hypertrophy, cardiorenal fibrosis, and inflammation. We also observed the upregulation of a network of genes involved in circadian rhythm in both tissues and downregulation of the renin-angiotensin system in the kidney and mitogen-activated protein kinase signaling in the heart. CONCLUSIONS: A diet high in fiber led to changes in the gut microbiota that played a protective role in the development of cardiovascular disease. The favorable effects of fiber may be explained by the generation and distribution of one of the main metabolites of the gut microbiota, the short-chain fatty acid acetate. Acetate effected several molecular changes associated with improved cardiovascular health and function.

Arrhythmogenic gene remodelling in elderly patients with type 2 diabetes with aortic stenosis and normal left ventricular ejection fraction.

NEW FINDINGS: What is the central question of this study? Type 2 diabetes is associated with a higher rate of ventricular arrhythmias compared with the non-diabetic population, but the associated myocardial gene expression changes are unknown; furthermore, it is also unknown whether any changes are attributable to chronic hyperglycaemia or are a consequence of structural changes. What is the main finding and its importance? We found downregulation of left ventricular ERG gene expression and increased NCX1 gene expression in humans with type 2 diabetes compared with control patients with comparable left ventricular hypertrophy and possible myocardial fibrosis. This was associated with QT interval prolongation. Diabetes and associated chronic hyperglycaemia may therefore promote ventricular arrhythmogenesis independently of structural changes. Type 2 diabetes is associated with a higher rate of ventricular arrhythmias, and this is hypothesized to be independent of coronary artery disease or hypertension. To investigate further, we compared changes in left ventricular myocardial gene expression in type 2 diabetes patients with patients in a control group with left ventricular hypertrophy. Nine control patients and seven patients with type 2 diabetes with aortic stenosis undergoing aortic valve replacement had standard ECGs, signal-averaged ECGs and echocardiograms before surgery. During surgery, a left ventricular biopsy was taken, and mRNA expressions for genes relevant to the cardiac action potential were estimated by RT-PCR. Mathematical modelling of the action potential and calcium transient was undertaken using the O'Hara-Rudy model using scaled changes in gene expression. Echocardiography revealed similar values for left ventricular size, filling pressures and ejection fraction between groups. No difference was seen in positive signal-averaged ECGs between groups, but the standard ECG demonstrated a prolonged QT interval in the diabetes group. Gene expression of KCNH2 and KCNJ3 were lower in the diabetes group, whereas KCNJ2, KCNJ5 and SLC8A1 expression were higher. Modelling suggested that these changes would lead to prolongation of the action potential duration with generation of early after-depolarizations secondary to a reduction in density of the rapid delayed rectifier K+ current and increased Na+ -Ca2+ exchange current. These data suggest that diabetes leads to pro-arrythmogenic changes in myocardial gene expression independently of left ventricular hypertrophy or fibrosis in an elderly population.

Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains.

Advances in neuronal recording techniques are leading to ever larger numbers of simultaneously monitored neurons. This poses the important analytical challenge of how to capture compactly all sensory information that neural population codes carry in their spatial dimension (differences in stimulus tuning across neurons at different locations), in their temporal dimension (temporal neural response variations), or in their combination (temporally coordinated neural population firing). Here we investigate the utility of tensor factorizations of population spike trains along space and time. These factorizations decompose a dataset of single-trial population spike trains into spatial firing patterns (combinations of neurons firing together), temporal firing patterns (temporal activation of these groups of neurons) and trial-dependent activation coefficients (strength of recruitment of such neural patterns on each trial). We validated various factorization methods on simulated data and on populations of ganglion cells simultaneously recorded in the salamander retina. We found that single-trial tensor space-by-time decompositions provided low-dimensional data-robust representations of spike trains that capture efficiently both their spatial and temporal information about sensory stimuli. Tensor decompositions with orthogonality constraints were the most efficient in extracting sensory information, whereas non-negative tensor decompositions worked well even on non-independent and overlapping spike patterns, and retrieved informative firing patterns expressed by the same population in response to novel stimuli. Our method showed that populations of retinal ganglion cells carried information in their spike timing on the ten-milliseconds-scale about spatial details of natural images. This information could not be recovered from the spike counts of these cells. First-spike latencies carried the majority of information provided by the whole spike train about fine-scale image features, and supplied almost as much information about coarse natural image features as firing rates. Together, these results highlight the importance of spike timing, and particularly of first-spike latencies, in retinal coding.

Avenues to autoimmune arthritis triggered by diverse remote inflammatory challenges.

Environmental factors contribute to development of autoimmune diseases. For instance, human autoimmune arthritis can associate with intestinal inflammation, cigarette smoking, periodontal disease, and various infections. The cellular and, molecular pathways whereby such remote challenges might precipitate arthritis or flares remain unclear. Here, we used a transfer model of self-reactive arthritis-inducing CD4(+) cells from KRNtg mice that, upon transfer, induce a very mild form of autoinflammatory arthritis in recipient animals. This model enabled us to identify external factors that greatly aggravated disease. We show that several distinct challenges precipitated full-blown arthritis, including intestinal inflammation through DSS-induced colitis, and bronchial stress through Influenza infection. Both triggers induced strong IL-17 expression primarily in self-reactive CD4(+) cells in lymph nodes draining the site of inflammation. Moreover, treatment of mice with IL-1ß greatly exacerbated arthritis, while transfer of KRNtg CD4(+) cells lacking IL-1R significantly reduced disease and IL-17 expression. Thus, IL-1ß enhances the autoaggressive potential of self-reactive CD4(+) cells, through increased Th17 differentiation, and this influences inflammatory events in the joints. We propose that diverse challenges that cause remote inflammation (lung infection or colitis, etc.) result in IL-1ß-driven Th17 differentiation, and this precipitates arthritis in genetically susceptible individuals. Thus the etiology of autoimmune inflammatory arthritis likely relates to diverse triggers that converge to a common pathway involving IL-1ß production and Th17 cell distribution.

Spike-Triggered Covariance Analysis Reveals Phenomenological Diversity of Contrast Adaptation in the Retina.

When visual contrast changes, retinal ganglion cells adapt by adjusting their sensitivity as well as their temporal filtering characteristics. The latter has classically been described by contrast-induced gain changes that depend on temporal frequency. Here, we explored a new perspective on contrast-induced changes in temporal filtering by using spike-triggered covariance analysis to extract multiple parallel temporal filters for individual ganglion cells. Based on multielectrode-array recordings from ganglion cells in the isolated salamander retina, we found that contrast adaptation of temporal filtering can largely be captured by contrast-invariant sets of filters with contrast-dependent weights. Moreover, differences among the ganglion cells in the filter sets and their contrast-dependent contributions allowed us to phenomenologically distinguish three types of filter changes. The first type is characterized by newly emerging features at higher contrast, which can be reproduced by computational models that contain response-triggered gain-control mechanisms. The second type follows from stronger adaptation in the Off pathway as compared to the On pathway in On-Off-type ganglion cells. Finally, we found that, in a subset of neurons, contrast-induced filter changes are governed by particularly strong spike-timing dynamics, in particular by pronounced stimulus-dependent latency shifts that can be observed in these cells. Together, our results show that the contrast dependence of temporal filtering in retinal ganglion cells has a multifaceted phenomenology and that a multi-filter analysis can provide a useful basis for capturing the underlying signal-processing dynamics.

Multi-scale modelling of the epileptic brain: advantages of computational therapy exploration.

Objective: Epilepsy is a complex disease spanning across multiple scales, from ion channels in neurons to neuronal circuits across the entire brain. Over the past decades, computational models have been used to describe the pathophysiological activity of the epileptic brain from different aspects. Traditionally, each computational model can aid in optimizing therapeutic interventions, therefore, providing a particular view to design strategies for treating epilepsy. As a result, most studies are concerned with generating specific models of the epileptic brain that can help us understand the certain machinery of the pathological state. Those specific models vary in complexity and biological accuracy, with system-level models often lacking biological details.Approach: Here, we review various types of computational model of epilepsy and discuss their potential for different therapeutic approaches and scenarios, including drug discovery, surgical strategies, brain stimulation, and seizure prediction. We propose that we need to consider an integrated approach with a unified modelling framework across multiple scales to understand the epileptic brain. Our proposal is based on the recent increase in computational power, which has opened up the possibility of unifying those specific epileptic models into simulations with an unprecedented level of detail.Main results: A multi-scale epilepsy model can bridge the gap between biologically detailed models, used to address molecular and cellular questions, and brain-wide models based on abstract models which can account for complex neurological and behavioural observations.Significance: With these efforts, we move toward the next generation of epileptic brain models capable of connecting cellular features, such as ion channel properties, with standard clinical measures such as seizure severity.

High-performance deep spiking neural networks via at-most-two-spike exponential coding.

Spiking neural networks (SNNs) provide necessary models and algorithms for neuromorphic computing. A popular way of building high-performance deep SNNs is to convert ANNs to SNNs, taking advantage of advanced and well-trained ANNs. Here we propose an ANN to SNN conversion methodology that uses a time-based coding scheme, named At-most-two-spike Exponential Coding (AEC), and a corresponding AEC spiking neuron model for ANN-SNN conversion. AEC neurons employ quantization-compensating spikes to improve coding accuracy and capacity, with each neuron generating up to two spikes within the time window. Two exponential decay functions with tunable parameters are proposed to represent the dynamic encoding thresholds, based on which pixel intensities are encoded into spike times and spike times are decoded into pixel intensities. The hyper-parameters of AEC neurons are fine-tuned based on the loss function of SNN-decoded values and ANN-activation values. In addition, we design two regularization terms for the number of spikes, providing the possibility to achieve the best trade-off between accuracy, latency and power consumption. The experimental results show that, compared to other similar methods, the proposed scheme not only obtains deep SNNs with higher accuracy, but also has more significant advantages in terms of energy efficiency and inference latency. More details can be found at https://github.com/RPDS2020/AEC.git.

Robust Decoding of Rich Dynamical Visual Scenes With Retinal Spikes.

Sensory information transmitted to the brain activates neurons to create a series of coping behaviors. Understanding the mechanisms of neural computation and reverse engineering the brain to build intelligent machines requires establishing a robust relationship between stimuli and neural responses. Neural decoding aims to reconstruct the original stimuli that trigger neural responses. With the recent upsurge of artificial intelligence, neural decoding provides an insightful perspective for designing novel algorithms of brain-machine interface. For humans, vision is the dominant contributor to the interaction between the external environment and the brain. In this study, utilizing the retinal neural spike data collected over multi trials with visual stimuli of two movies with different levels of scene complexity, we used a neural network decoder to quantify the decoded visual stimuli with six different metrics for image quality assessment establishing comprehensive inspection of decoding. With the detailed and systematical study of the effect and single and multiple trials of data, different noise in spikes, and blurred images, our results provide an in-depth investigation of decoding dynamical visual scenes using retinal spikes. These results provide insights into the neural coding of visual scenes and services as a guideline for designing next-generation decoding algorithms of neuroprosthesis and other devices of brain-machine interface.

Revealing the mechanisms of semantic satiation with deep learning models.

The phenomenon of semantic satiation, which refers to the loss of meaning of a word or phrase after being repeated many times, is a well-known psychological phenomenon. However, the microscopic neural computational principles responsible for these mechanisms remain unknown. In this study, we use a deep learning model of continuous coupled neural networks to investigate the mechanism underlying semantic satiation and precisely describe this process with neuronal components. Our results suggest that, from a mesoscopic perspective, semantic satiation may be a bottom-up process. Unlike existing macroscopic psychological studies that suggest that semantic satiation is a top-down process, our simulations use a similar experimental paradigm as classical psychology experiments and observe similar results. Satiation of semantic objectives, similar to the learning process of our network model used for object recognition, relies on continuous learning and switching between objects. The underlying neural coupling strengthens or weakens satiation. Taken together, both neural and network mechanisms play a role in controlling semantic satiation.