Stimulus Change Research Articles

The combination of Hebbian and predictive plasticity learns invariant object representations in deep sensory networks

Recognition of objects from sensory stimuli is essential for survival. To that end, sensory networks in the brain must form object representations invariant to stimulus changes, such as size, orientation and context. Although Hebbian plasticity is known to shape sensory networks, it fails to create invariant object representations in computational models, raising the question of how the brain achieves such processing. In the present study, we show that combining Hebbian plasticity with a predictive form of plasticity leads to invariant representations in deep neural network models. We derive a local learning rule that generalizes to spiking neural networks and naturally accounts for several experimentally observed properties of synaptic plasticity, including metaplasticity and spike-timing-dependent plasticity. Finally, our model accurately captures neuronal selectivity changes observed in the primate inferotemporal cortex in response to altered visual experience. Thus, we provide a plausible normative theory emphasizing the importance of predictive plasticity mechanisms for successful representational learning.

Whole-brain, gray, and white matter time-locked functional signal changes with simple tasks and model-free analysis

Recent studies have revealed the production of time-locked blood oxygenation level-dependent (BOLD) functional MRI (fMRI) signals throughout the entire brain in response to tasks, challenging the existence of sparse and localized brain functions and highlighting the pervasiveness of potential false negative fMRI findings. "Whole-brain" actually refers to gray matter, the only tissue traditionally studied with fMRI. However, several reports have demonstrated reliable detection of BOLD signals in white matter, which have previously been largely ignored. Using simple tasks and analyses, we demonstrate BOLD signal changes across the whole brain, in both white and gray matters, in similar manner to previous reports of whole brain studies. We investigated whether white matter displays time-locked BOLD signals across multiple structural pathways in response to a stimulus in a similar manner to the cortex. We find that both white and gray matter show time-locked activations across the whole brain, with a majority of both tissue types showing statistically significant signal changes for all task stimuli investigated. We observed a wide range of signal responses to tasks, with different regions showing different BOLD signal changes to the same task. Moreover, we find that each region may display different BOLD responses to different stimuli. Overall, we present compelling evidence that, just like all gray matter, essentially all white matter in the brain shows time-locked BOLD signal changes in response to multiple stimuli, challenging the idea of sparse functional localization and the prevailing wisdom of treating white matter BOLD signals as artifacts to be removed.

Temporal Expectancy in Driving : An Automated Future

The difference in temporal expectancy between participants who are experienced in driving automated vehicles, and participants who are experienced in using only manually driven vehicles, is measured. The period in seconds between the change in stimulus, or in this case, the signal changing from green to red, and the onset of the action, in this case, the muscle tension generated in the ankles before pressing the brake, measures temporal expectancy. This exercise is carried out for each participant through a driving simulator of either manually driven features or auto-pilot features, based on the type of driving experience of the participant. The mean temporal expectancy of each participant is calculated through the cumulation of a definite number of trials. Thus, the means are used to derive the average temporal expectancy of each of the two groups based on auto-pilot or manual drivers. The group tabulated to have lesser seconds as a measure of temporal expectancy, is inferred to have better temporal expectancy.

Sensorized Skin With Biomimetic Tactility Features Based on Artificial Cross-Talk of Bimodal Resistive Sensory Inputs.

Tactility in biological organisms is a faculty that relies on a variety of specialized receptors. The bimodal sensorized skin, featured in this study, combines soft resistive composites that attribute the skin with mechano- and thermoreceptive capabilities. Mimicking the position of the different natural receptors in different depths of the skin layers, a multi-layer arrangement of the soft resistive composites is achieved. However, the magnitude of the signal response and the localization ability of the stimulus change with lighter presses of the bimodal skin. Hence, a learning-based approach is employed that can help achieve predictions about the stimulus using 4500 probes. Similar to the cognitive functions in the human brain, the cross-talk of sensory information between the two types of sensory information allows the learning architecture to make more accurate predictions of localization, depth, and temperature of the stimulus contiguously. Localization accuracies of 1.8mm, depth errors of 0.22mm, and temperature errors of 8.2 °C using 8 mechanoreceptive and 8 thermoreceptive sensing elements are achieved for the smaller inter-element distances. Combining the bimodal sensing multilayer skins with the neural network learning approach brings the artificial tactile interface one step closer to imitating the sensory capabilities of biological skin.

Adaptive Circadian Rhythms for Autonomous and Biologically Inspired Robot Behavior.

Biological rhythms are periodic internal variations of living organisms that act as adaptive responses to environmental changes. The human pacemaker is the suprachiasmatic nucleus, a brain region involved in biological functions like homeostasis or emotion. Biological rhythms are ultradian (<24 h), circadian (∼24 h), or infradian (>24 h) depending on their period. Circadian rhythms are the most studied since they regulate daily sleep, emotion, and activity. Ambient and internal stimuli, such as light or activity, influence the timing and the period of biological rhythms, making our bodies adapt to dynamic situations. Nowadays, robots experience unceasing development, assisting us in many tasks. Due to the dynamic conditions of social environments and human-robot interaction, robots exhibiting adaptive behavior have more possibilities to engage users by emulating human social skills. This paper presents a biologically inspired model based on circadian biorhythms for autonomous and adaptive robot behavior. The model uses the Dynamic Circadian Integrated Response Characteristic method to mimic human biology and control artificial biologically inspired functions influencing the robot's decision-making. The robot's clock adapts to light, ambient noise, and user activity, synchronizing the robot's behavior to the ambient conditions. The results show the adaptive response of the model to time shifts and seasonal changes of different ambient stimuli while regulating simulated hormones that are key in sleep/activity timing, stress, and autonomic basal heartbeat control during the day.

A computational growth and remodeling framework for adaptive and maladaptive pulmonary arterial hemodynamics.

Hemodynamic loading is known to contribute to the development and progression of pulmonary arterial hypertension (PAH). This loading drives changes in mechanobiological stimuli that affect cellular phenotypes and lead to pulmonary vascular remodeling. Computational models have been used to simulate mechanobiological metrics of interest, such as wall shear stress, at single time points for PAH patients. However, there is a need for new approaches that simulate disease evolution to allow for prediction of long-term outcomes. In this work, we develop a framework that models the pulmonary arterial tree through adaptive and maladaptive responses to mechanical and biological perturbations. We coupled a constrained mixture theory-based growth and remodeling framework for the vessel wall with a morphometric tree representation of the pulmonary arterial vasculature. We show that non-uniform mechanical behavior is important to establish the homeostatic state of the pulmonary arterial tree, and that hemodynamic feedback is essential for simulating disease time courses. We also employed a series of maladaptive constitutive models, such as smooth muscle hyperproliferation and stiffening, to identify critical contributors to development of PAH phenotypes. Together, these simulations demonstrate an important step toward predicting changes in metrics of clinical interest for PAH patients and simulating potential treatment approaches.

Human Amygdala Volumetric Patterns Convergently Evolved in Cooperatively Breeding and Domesticated Species

The amygdala is a hub in brain networks that supports social life and fear processing. Compared with other apes, humans have a relatively larger lateral nucleus of the amygdala, which is consistent with both the self-domestication and the cooperative breeding hypotheses of human evolution. Here, we take a comparative approach to the evolutionary origin of the relatively larger lateral amygdala nucleus in humans. We carry out phylogenetic analysis on a sample of 17 mammalian species for which we acquired single amygdala nuclei volumetric data. Our results indicate that there has been convergent evolution toward larger lateral amygdala nuclei in both domesticated and cooperatively breeding mammals. These results suggest that changes in processing fearful stimuli to reduce fear-induced aggression, which are necessary for domesticated and cooperatively breeding species alike, tap into the same neurobiological proximate mechanism. However, humans show changes not only in processing fearful stimuli but also in proactive prosociality. Since cooperative breeding, but not domestication, is also associated with increased proactive prosociality, a prominent role of the former during human evolution is more parsimonious, whereas self-domestication may have been involved as an additional stepping stone.

The sensory effects of light on the electric organ discharge rate of Gymnotus omarorum.

Gymnotiformes are nocturnal fishes inhabiting the root mats of floating plants. They use their electric organ discharge (EOD) to explore the environment and to communicate. Here, we show and describe tonic and phasic sensory-electromotor responses to light distinct from indirect effects depending on the light-induced endogenous circadian rhythm. In the dark, principally during the night, inter-EOD interval histograms are bimodal: the main peak corresponds to the basal rate and a secondary peak corresponds to high-frequency bouts. Light causes a twofold tonic but opposing effect on the EOD histogram: (i) decreasing the main mode and (ii) blocking the high-frequency bouts and consequently increasing the main peak at the expense of removal of the secondary one. Additionally, light evokes phasic responses whose amplitude increases with intensity but whose slow time course and poor adaptation differentiate from the so-called novelty responses evoked by abrupt changes in sensory stimuli of other modalities. We confirmed that Gymnotus omarorum tends to escape from light, suggesting that these phasic responses are probably part of a global 'light-avoidance response'. We interpret the data within an ecological context. Fish rest under the shade of aquatic plants during the day and light spots due to the sun's relative movement alert the fish to hide in shady zones to avoid macroptic predators and facilitate tracking the movement of floating plant islands by wind and/or water currents.

Nanotechnology-Assisted Immunogenic Cell Death for Effective Cancer Immunotherapy.

Tumor vaccines have been used to treat cancer. How to efficiently induce tumor-associated antigens (TAAs) secretion with host immune system activation is a key issue in achieving high antitumor immunity. Immunogenic cell death (ICD) is a process in which tumor cells upon an external stimulus change from non-immunogenic to immunogenic, leading to enhanced antitumor immune responses. The immune properties of ICD are damage-associated molecular patterns and TAA secretion, which can further promote dendritic cell maturation and antigen presentation to T cells for adaptive immune response provocation. In this review, we mainly summarize the latest studies focusing on nanotechnology-mediated ICD for effective cancer immunotherapy as well as point out the challenges.

Introduction to Shape Memory Material: Biomedical, Prosthetic and Orthotic Application

The need for prosthetic and orthotic devices is increasing significantly all over the world. Due to the increasing incidenceof sports injuries and road accidents, the rising number of diabetes-related amputations, and the growing prevalence ofosteosarcoma around the world are poised to drive the global market. Sports-induced injury is anticipated to drive demand,aiding market growth during the forecast period. The global prosthetics and orthotics market was valued at USD 6.39 billionin 2021 and is expected to expand at a compound annual growth rate (CAGR) of 4.3% from 2022 to 2030. Materials likepolymers (Polypropylene, Silicon, etc.), metals (Aluminium, Iron, etc.), alloys (stainless steel, bronze etc.), and carbon fibersare now used for making prosthetic and orthotic devices. Shape memory polymers (SMP) and shape memory alloys (SMA) arehaving ability to change and regain their original shape after changes in external stimuli like temperature, PH, heat, etc. Thisreview has discussed shape memory materials’ mechanical, chemical, and general properties, including their classification,advantages, disadvantages, FDA regulations, and applications in prosthetics and orthotics. This review will help prosthetists,orthotist, and biomedical engineers better understand these materials and how they increase the medical devices’ quality,durability and functionality.

Linking the Mechanics of Chewing to Biology of the Junctional Epithelium

The capacity of a tissue to continuously alter its phenotype lies at the heart of how an animal is able to quickly adapt to changes in environmental stimuli. Within tissues, differentiated cells are rigid and play a limited role in adapting to new environments; however, differentiated cells are replenished by stem cells that are defined by their phenotypic plasticity. Here we demonstrate that a Wnt-responsive stem cell niche in the junctional epithelium is responsible for the capability of this tissue to quickly adapt to changes in the physical consistency of a diet. Mechanical input from chewing is required to both establish and maintain this niche. Since the junctional epithelium directly attaches to the tooth surface via hemidesmosomes, a soft diet requires minimal mastication, and consequently, lower distortional strains are produced in the tissue. This reduced strain state is accompanied by reduced mitotic activity in both stem cells and their progeny, leading to tissue atrophy. The atrophied junctional epithelium exhibits suboptimal barrier functions, allowing the ingression of bacteria into the underlying connective tissues, which in turn trigger inflammation and mild alveolar bone loss. These data link the mechanics of chewing to the biology of tooth-supporting tissues, revealing how a stem cell niche is responsible for the remarkable adaptability of the junctional epithelium to different diets.

Finite Element Analysis of Laminated Composite Shells with Piezoelectric Elements

The concept of incorporating some degree of smartness or intelligence in a system is gaining wide acceptance for utilisation in a variety of practical applications from aircraft to medical diagnostics because of the capability of smart materials to execute specific functions intelligently in response to changes in environmental stimuli. In this paper, the effectiveness of a simple finite element model in ANSYS is established for analyzing laminated composite shells with piezoelectric elements under static and dynamic loading. The substrate in smart shell is discretised using SOLSH190 element and the piezoelectric layer is discretised using coupledfield element, SOLID5. The basic behaviour of a smart composite shell depends on the behaviour of substrate with the response reduced/controlled by the properties of smart materials used, which is predicted effectively by the present finite element model.

Deletion of the Transient Receptor Potential Vanilloid 1 Channel Attenuates Sympathoexcitation and Hypertension and Improves Glomerular Filtration Rate in 2-Kidney-1-Clip Rats.

Renal denervation lowers arterial blood pressure in both clinical populations and multiple experimental models of hypertension. This therapeutic effect is partly attributed to the removal of overactive renal sensory nerves. The TRPV1 (transient receptor potential vanilloid 1) channel is highly expressed in renal sensory nerves and detects changes in noxious and mechanosensitive stimuli, pH, and chemokines. However, the extent to which TRPV1 channels contribute to 2-kidney-1-clip (2K1C) renovascular hypertension has not been tested. We generated a novel Trpv1-/- (TRPV1 knockout) rat using CRISPR/Cas9 and 26-bp deletion in exon 3 and induced 2K1C hypertension. The majority (85%) of rat renal sensory neurons retrogradely labeled from the kidney were TRPV1-positive. Trpv1-/- rats lacked TRPV1 immunofluorescence in the dorsal root ganglia, had a delayed tail-flick response to hot but not cold water, and lacked an afferent renal nerve activity response to intrarenal infusion of the TRPV1 agonist capsaicin. Interestingly, 2K1C hypertension was significantly attenuated in male Trpv1-/- versus wild-type rats. 2K1C hypertension significantly increased the depressor response to ganglionic blockade, total renal nerve activity (efferent and afferent), and afferent renal nerve activity in wild-type rats, but these responses were attenuated in male Trpv1-/- rats. 2K1C hypertension was attenuated in female rats with no differences between female strains. Finally, glomerular filtration rate was reduced by 2K1C in wild-type rats but improved in Trpv1-/- rats. These findings suggest that renovascular hypertension requires activation of the TRPV1 channel to elevate renal afferent and sympathetic nerve activity, reduce glomerular filtration rate, and increase arterial blood pressure.

Smart Polymers for Soft Materials: From Solution Processing to Organic Solids.

Polymeric materials are ubiquitous in our everyday life, where they find a broad range of uses-spanning across common household items to advanced materials for modern technologies. In the context of the latter, so called "smart polymers" have received a lot of attention. These systems are soluble in water below their lower critical solution temperature Tℓ and often exhibit counterintuitive solvation behavior in mixed solvents. A polymer is known as smart-responsive when a slight change in external stimuli can significantly change its structure, functionm and stability. The interplay of different interactions, especially hydrogen bonds, can also be used for the design of lightweight high-performance organic solids with tunable properties. Here, a general scheme for establishing a structure-property relationship is a challenge using the conventional simulation techniques and also in standard experiments. From the theoretical side, a broad range of all-atom, multiscale, generic, and analytical techniques have been developed linking monomer level interaction details with macroscopic material properties. In this review, we briefly summarize the recent developments in the field of smart polymers, together with complementary experiments. For this purpose, we will specifically discuss the following: (1) the solution processing of responsive polymers and (2) their use in organic solids, with a goal to provide a microscopic understanding that may be used as a guiding tool for future experiments and/or simulations regarding designing advanced functional materials.

A multimode‐fused sensory memory system based on a robust self‐assembly nanoscaffolded BaTiO3:Eu2O3 memristor

AbstractBiologically inspired neuromorphic sensory memory systems based on memristor have received a lot of attention in the booming artificial intelligence industry due to significant potential to effectively process multi‐sensory signals from complex external environments. However, many memristors have significant switching parameters disperse, which is a great challenge for using memristors in bionic neuromorphic sensory memory systems. Herein, a stable ferroelectric memristor based on the Pd/BaTiO3:Eu2O3/La0.67Sr0.33MnO3 grown on Silicon structure with SrTiO3 as buffer layer is presented. The device possesses low coercive field voltage (−1.3–2.1 V) and robust endurance characteristic (~1010 cycles) through optimizing the growth temperature. More importantly, an ultra‐stable artificial multimodal sensory memory system with visual and tactile functions was reported for the first time by combining a pressure sensor, a photosensitive sensor, and a robotic arm. Utilizing the above system, the sensitivity value of the system is expressed by the conductance of the memristor to realize the gradual change of external stimulus, and multi signals inputs at the same time to this system have faithfully achieved sensory adaptation to multimodal sensors. This work paves the way for future development of memristor‐based perception systems in efficient multisensory neural robots.image

Topological insights into the neural basis of flexible behavior

It is widely accepted that there is an inextricable link between neural computations, biological mechanisms, and behavior, but it is challenging to simultaneously relate all three. Here, we show that topological data analysis (TDA) provides an important bridge between these approaches to studying how brains mediate behavior. We demonstrate that cognitive processes change the topological description of the shared activity of populations of visual neurons. These topological changes constrain and distinguish between competing mechanistic models, are connected to subjects' performance on a visual change detection task, and, via a link with network control theory, reveal a tradeoff between improving sensitivity to subtle visual stimulus changes and increasing the chance that the subject will stray off task. These connections provide a blueprint for using TDA to uncover the biological and computational mechanisms by which cognition affects behavior in health and disease.

Can an Enrichment Programme with Novel Manipulative and Scent Stimuli Change the Behaviour of Zoo-Housed European Wildcats? A Case Study.

Objects and semiochemicals may be used as enrichment in zoos. Domestic cats release Fraction 3 of Facial Pheromone (F3) by rubbing the muzzle to convey relational and territorial information. We aimed to evaluate whether and how the introduction of novel objects and scent stimuli could change the behaviour of one group (N = 5 subjects) of adult European wildcats (Felis silvestris silvestris) hosted at Parco Natura Viva-Garda Zoological Park, Italy. We assessed the behavioural changes following the introduction of novel objects (blocks and rags) and scent (synthetic F3) via observations over four experimental conditions (baseline, rags, F3 rags, blocks) using continuous focal animal sampling. Our results showed that no behavioural differences were found between the different conditions and the baseline, except for the condition with blocks when significantly less exploration was observed. Between conditions, wildcats performed significantly less individual explorative, affiliative, and agonistic behaviours, but more individual inactivity, when exposed to rags after F3 administration. Our findings suggest that the enrichment programme did not substantively affect the behaviour of the zoo-housed wildcats. However, the behavioural differences recorded between conditions suggest that, while novel objects introduced as visual stimuli (blocks) do not affect the wildcat behaviour, novel manipulative objects (rags) might impact their behaviour. Moreover, the changes in affiliative and agonistic behaviours displayed during the condition with exposure to rags sprayed with F3 suggest that such semiochemical could play an appeasement role within this study group.

Frequency unlocking-based MEMS bifurcation sensors

MEMS resonators exhibit rich dynamic behaviors under the internal resonance regime. In this work, we present a novel MEMS bifurcation sensor that exploits frequency unlocking due to a 1:3 internal resonance between two electrostatically coupled micro-resonators. The proposed detection mechanism allows the sensor to operate in binary (digital) and analog modes, depending on whether the sensor merely detects a significant jump event in the peak frequency upon unlocking or measures the shift in the peak frequency after unlocking and uses it in conjunction with a calibration curve to estimate the corresponding change in stimulus. We validate the success of this sensor paradigm by experimentally demonstrating charge detection. High charge resolutions are achieved in binary mode, up to 0.137 fC, and in analog mode, up to 0.01 fC. The proposed binary sensor enables extraordinarily high detection resolutions due to the excellent frequency stability under internal resonance and the high signal-to-noise ratio of the shift in peak frequency. Our findings offer new opportunities for high-performance ultrasensitive sensors.

Is superstitious responding a matter of detectability? A replication of Killeen (1978).

Organisms may sometimes behave as if a contingency exists between behavior and consequences, even if this is not actually the case. Killeen (1978) suggested that such superstition occurs because of factors that bias subjects to behave "superstitiously" rather than because of failures of discrimination. We systematically replicated Killeen's experiment and compared contingency discrimination between different consequences. Six pigeons responded in a matching-to-sample procedure in which a response-independent or response-dependent stimulus change, food delivery, or blackout occurred. The pigeons reported whether the consequence was response dependent or response independent by choosing between two side keys. Discrimination was strongest after stimulus changes, weaker after blackouts, and weakest after food deliveries. These differences persisted even after additional training, suggesting asymmetries that may reflect differences in the disruptive effects of different consequences on remembering and/or behavioral mnemonics. Importantly, the pigeons were not biased to report response-dependent consequences unless that response was consistent with locational biases; that is, they behaved "superstitiously" when there was a reason to be biased to do so. These findings corroborate Killeen's and demonstrate that behavior may deviate from contingencies not necessarily because subjects cannot discriminate those contingencies but because they are biased to behave otherwise.

Decoding of the neural representation of the visual RGB color model.

RGB color is a basic visual feature. Here we use machine learning and visual evoked potential (VEP) of electroencephalogram (EEG) data to investigate the decoding features of the time courses and space location that extract it, and whether they depend on a common brain cortex channel. We show that RGB color information can be decoded from EEG data and, with the task-irrelevant paradigm, features can be decoded across fast changes in VEP stimuli. These results are consistent with the theory of both event-related potential (ERP) and P300 mechanisms. The latency on time course is shorter and more temporally precise for RGB color stimuli than P300, a result that does not depend on a task-relevant paradigm, suggesting that RGB color is an updating signal that separates visual events. Meanwhile, distribution features are evident for the brain cortex of EEG signal, providing a space correlate of RGB color in classification accuracy and channel location. Finally, space decoding of RGB color depends on the channel classification accuracy and location obtained through training and testing EEG data. The result is consistent with channel power value distribution discharged by both VEP and electrophysiological stimuli mechanisms.