Learning delays through gradients and structure: emergence of spatiotemporal patterns in spiking neural networks.

We present a Spiking Neural Network (SNN) model that incorporates learnable synaptic delays through two approaches: per-synapse delay learning via Dilated Convolutions with Learnable Spacings (DCLS) and a dynamic pruning strategy that also serves as a form of delay learning. In the latter approach, the network dynamically selects and prunes connections, optimizing the delays in sparse connectivity settings. We evaluate both approaches on the Raw Heidelberg Digits keyword spotting benchmark using Backpropagation Through Time with surrogate gradients.

Adaptive Route Memory Sequences for Insect-Inspired Visual Route Navigation.

Visual navigation is a key capability for robots and animals. Inspired by the navigational prowess of social insects, a family of insect-inspired route navigation algorithms-familiarity-based algorithms-have been developed that use stored panoramic images collected during a training route to subsequently derive directional information during route recapitulation. However, unlike the ants that inspire them, these algorithms ignore the sequence in which the training images are acquired so that all temporal information/correlation is lost.

Vehicle-to-Vehicle Flooding Datasets using MK5 On-board Unit Devices.

The availability of information is a key requirement for the proper functioning of any network. When the availability problem is brought to vehicular networks, it may hinder novel vehicular services and applications and potentially put human lives at risk, as malicious users can send a massive number of spurious packets to disrupt them. Although flooding attacks in vehicular contexts have been the focus of attention of the research community, most proposed datasets are generated using simulated data and only contain the modeled network's behavior.

Adapting to time: Why nature may have evolved a diverse set of neurons.

Brains have evolved diverse neurons with varying morphologies and dynamics that impact temporal information processing. In contrast, most neural network models use homogeneous units that vary only in spatial parameters (weights and biases). To explore the importance of temporal parameters, we trained spiking neural networks on tasks with varying temporal complexity, holding different parameter subsets constant.

Flow synthesis and multidimensional parameter screening enables exploration and optimization of copper oxide nanoparticle synthesis.

Copper-based nanoparticles (NPs) are highly valued for their wide-ranging applications, with particular significance in CO reduction. However current synthesis methods encounter challenges in scalability, batch-to-batch variation, and high energy costs. In this work, we describe a novel continuous flow synthesis approach performed at room temperature to help address these issues, producing spherical, colloidally stable copper(ii) oxide (CuO) NPs.

Synesthesia is linked to large and extensive differences in brain structure and function as determined by whole-brain biomarkers derived from the HCP (Human Connectome Project) cortical parcellation approach.

There is considerable interest in understanding the developmental origins and health implications of individual differences in brain structure and function. In this pre-registered study we demonstrate that a hidden subgroup within the general population-people with synesthesia (e.g.

Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space.

Multi-electrode arrays covering several square millimeters of neural tissue provide simultaneous access to population signals such as extracellular potentials and spiking activity of one hundred or more individual neurons. The interpretation of the recorded data calls for multiscale computational models with corresponding spatial dimensions and signal predictions. Multi-layer spiking neuron network models of local cortical circuits covering about $1\,{\text{mm}^{2}}$ have been developed, integrating experimentally obtained neuron-type-specific connectivity data and reproducing features of observed in-vivo spiking statistics.

Supervised structure learning.

This paper concerns structure learning or discovery of discrete generative models. It focuses on Bayesian model selection and the assimilation of training data or content, with a special emphasis on the order in which data are ingested. A key move-in the ensuing schemes-is to place priors on the selection of models, based upon expected free energy.

Bioinspired adaptable multiplanar mechano-vibrotactile haptic system.

Several gaps persist in haptic device development due to the multifaceted nature of the sense of touch. Existing gaps include challenges enhancing touch feedback fidelity, providing diverse haptic sensations, and ensuring wearability for delivering tactile stimuli to the fingertips. Here, we introduce the Bioinspired Adaptable Multiplanar Haptic system, offering mechanotactile/steady and vibrotactile pulse stimuli with adjustable intensity (up to 298.

Synergy Makes Direct Perception Inefficient.

A typical claim in anti-representationalist approaches to cognition such as ecological psychology or radical embodied cognitive science is that ecological information is sufficient for guiding behavior. According to this view, affordances are immediately perceptually available to the agent (in the so-called "ambient energy array"), so sensory data does not require much further inner processing. As a consequence, mental representations are explanatorily idle: perception is immediate and direct.

Narrative as active inference: an integrative account of cognitive and social functions in adaptation.

While the ubiquity and importance of narratives for human adaptation is widely recognized, there is no integrative framework for understanding the roles of narrative in human adaptation. Research has identified several cognitive and social functions of narratives that are conducive to well-being and adaptation as well as to coordinated social practices and enculturation. In this paper, we characterize the cognitive and social functions of narratives in terms of active inference, to support the claim that one of the main adaptive functions of narrative is to generate more useful (i.

Adaptive neural fault-tolerant prescribed performance control of a rehabilitation exoskeleton for lower limb passive training.

This article studies the passive tracking problem of a wearable exoskeleton for lower limb rehabilitation therapy in the face of unmodeled dynamics, interactive friction, disturbance, prescribed performance constraints, and actuator faults. Adaptive neural networks and a smooth performance function are incorporated to establish a novel fault-tolerant tracking scheme, which can not only compensate for the nonlinear uncertainties and disturbance, but also handle the actuator fault with guaranteed tracking performance. A state feedback controller is presented by using the full state information and an output feedback controller is developed when the angular velocity is unavailable.

Layer-selective deep representation to improve esophageal cancer classification.

Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their accountability and transparency level must be improved to transfer this success into clinical practice. The reliability of machine learning decisions must be explained and interpreted, especially for supporting the medical diagnosis. For this task, the deep learning techniques' black-box nature must somehow be lightened up to clarify its promising results.

Investigating visual navigation using spiking neural network models of the insect mushroom bodies.

Ants are capable of learning long visually guided foraging routes with limited neural resources. The visual scene memory needed for this behaviour is mediated by the mushroom bodies; an insect brain region important for learning and memory. In a visual navigation context, the mushroom bodies are theorised to act as familiarity detectors, guiding ants to views that are similar to those previously learned when first travelling along a foraging route.

A Bayesian model of legal syllogistic reasoning.

Bayesian approaches to legal reasoning propose causal models of the relation between evidence, the credibility of evidence, and ultimate hypotheses, or verdicts. They assume that legal reasoning is the process whereby one infers the posterior probability of a verdict based on observed evidence, or facts. In practice, legal reasoning does not operate quite that way.

Methodological adjustments for experimental studies including neurodiverse participants: A checklist for before, during, and after laboratory visits.

In this paper, we developed an experimental checklist for laboratory experiments including neurodiverse participants, particularly those with attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and dyslexia. The checklist suggests additions to the basic requirements for ethical laboratory-based studies with human participants. The suggestions emphasize physical comfort, the agency of participants concerning environmental adjustments, clarity of communication, and a focus on participants' overall well-being.

Companion animal adoption and relinquishment during the COVID-19 pandemic: The experiences of animal rescue staff and volunteers.

There has been a paucity of research into the experiences of animal rescue staff and volunteers during COVID-19. The aim of this qualitative research was to explore the impact of the COVID-19 pandemic on companion animal rescue organisations and their staff and volunteers, and to develop a set of recommendations on how to reduce the risk to companion animal welfare during a crisis. Descriptive thematic analysis was used to explore the experiences of staff and volunteers from 28 animal rescue organisations, most of which were based in the UK.

Sources of richness and ineffability for phenomenally conscious states.

Conscious states-state that there is something it is like to be in-seem both rich or full of detail and ineffable or hard to fully describe or recall. The problem of ineffability, in particular, is a longstanding issue in philosophy that partly motivates the explanatory gap: the belief that consciousness cannot be reduced to underlying physical processes. Here, we provide an information theoretic dynamical systems perspective on the richness and ineffability of consciousness.

Tests for consciousness in humans and beyond.

Which systems/organisms are conscious? New tests for consciousness ('C-tests') are urgently needed. There is persisting uncertainty about when consciousness arises in human development, when it is lost due to neurological disorders and brain injury, and how it is distributed in nonhuman species. This need is amplified by recent and rapid developments in artificial intelligence (AI), neural organoids, and xenobot technology.

Exploring the potential for online data sources to enhance species threat mapping through the case study of global bat exploitation.

Expanding digital data sources, including social media and online news, provide a low-cost way to examine human-nature interactions, such as wildlife exploitation. However, the extent to which using such data sources can expand or bias understanding of the distribution and intensity of threats has not been comprehensively assessed. To address this gap, we quantified the geographical and temporal distribution of online sources documenting the hunting and trapping, consumption, or trade of bats (Chiroptera) and compared these with the distribution of studies obtained from a systematic literature search and species listed as threatened by exploitation on the International Union for Conservation of Nature Red List.

Strain-Promoted Cycloadditions in Lipid Bilayers Triggered by Liposome Fusion.

Due to the variety of roles served by the cell membrane, its composition and structure are complex, making it difficult to study. Bioorthogonal reactions, such as the strain promoted azide-alkyne cycloaddition (SPAAC), are powerful tools for exploring the function of biomolecules in their native environment but have been largely unexplored within the context of lipid bilayers. Here, we developed a new approach to study the SPAAC reaction in liposomal membranes using azide- and strained alkyne-functionalized Förster resonance energy transfer (FRET) dye pairs.