Associative learning in the box jellyfish Tripedalia cystophora

Defecation after magnesium supplementation enhances cognitive performance in triathletes

Constipation is correlated with diminished cognitive function, revealing a possible rectum-brain connection. In this counter-balanced crossover trial, 13 elite triathletes underwent a Stroop test to assess cognitive function and executive control. The Stroop test was conducted both with and without magnesium oxide intake, with a 1-week washout period between sessions. Oxygenation and blood distribution during the cognitive challenge were measured using Near-Infrared Spectroscopy (NIRS). Measurements were taken in both the prefrontal brain and the sub-navel region, where the highest glucose uptake was detected under the 18F-fluorodeoxyglucose Positron Emission Tomography (PET) scan. A significant reduction in completion time for the Stroop test was observed after defecation compared to the non-defecated condition (non-defecation: [27.1 ​± ​1.1] s; non-magnesium defecation: [24.4 ​± ​0.9] s; magnesium defecation: [23.4 ​± ​0.8] s, p ​< ​0.05). Stroop test performance was improved in all (100%, 13/13) of the participants after magnesium-induced defecation and most (69%, 9/13) of the participants after non-magnesium-induced defecation. While no alterations in oxygenation and blood distribution were observed in the prefrontal brain during the Stroop test, decreased oxygenation levels were observed in the sub-navel region under both defecated conditions, without significant changes in blood distribution (p ​< ​0.05). This data suggests an acute increase in oxygen consumption at this specific region. The result of this study suggests an unexplored causal link between the state of the rectum and cognitive performance. Magnesium supplementation to improved rectal emptying presents a novel application for optimizing cognitive function in athletes navigating intricate racing conditions.

Comparison of derivative-based and correlation-based methods to estimate effective connectivity in neural networks

Inferring and understanding the underlying connectivity structure of a system solely from the observed activity of its constituent components is a challenge in many areas of science. In neuroscience, techniques for estimating connectivity are paramount when attempting to understand the network structure of neural systems from their recorded activity patterns. To date, no universally accepted method exists for the inference of effective connectivity, which describes how the activity of a neural node mechanistically affects the activity of other nodes. Here, focussing on purely excitatory networks of small to intermediate size and continuous node dynamics, we provide a systematic comparison of different approaches for estimating effective connectivity. Starting with the Hopf neuron model in conjunction with known ground truth structural connectivity, we reconstruct the system's connectivity matrix using a variety of algorithms. We show that, in sparse non-linear networks with delays, combining a lagged-cross-correlation (LCC) approach with a recently published derivative-based covariance analysis method provides the most reliable estimation of the known ground truth connectivity matrix. We outline how the parameters of the Hopf model, including those controlling the bifurcation, noise, and delay distribution, affect this result. We also show that in linear networks, LCC has comparable performance to a method based on transfer entropy, at a drastically lower computational cost. We highlight that LCC works best for small sparse networks, and show how performance decreases in larger and less sparse networks. Applying the method to linear dynamics without time delays, we find that it does not outperform derivative-based methods. We comment on this finding in light of recent theoretical results for such systems. Employing the Hopf model, we then use the estimated structural connectivity matrix as the basis for a forward simulation of the system dynamics, in order to recreate the observed node activity patterns. We show that, under certain conditions, the best method, LCC, results in higher trace-to-trace correlations than derivative-based methods for sparse noise-driven systems. Finally, we apply the LCC method to empirical biological data. Choosing a suitable threshold for binarization, we reconstruct the structural connectivity of a subset of the nervous system of the nematode C. elegans. We show that the computationally simple LCC method performs better than another recently published, computationally more expensive reservoir computing-based method. We apply different methods to this dataset and find that they all lead to similar performances. Our results show that a comparatively simple method can be used to reliably estimate directed effective connectivity in sparse neural systems in the presence of spatio-temporal delays and noise. We provide concrete suggestions for the estimation of effective connectivity in a scenario common in biological research, where only neuronal activity of a small set of neurons, but not connectivity or single-neuron and synapse dynamics, are known.

Walking coral: Complex phototactic mobility in the free-living coral Cycloseris cyclolites

Not all corals are attached to the substrate; some taxa are solitary and free-living, allowing them to migrate into preferred habitats. However, the lifestyle of these mobile corals, including how they move and navigate for migration, remains largely obscure. This study investigates the specific biomechanics of Cycloseris cyclolites, a free-living coral species, during phototactic behaviour in response to blue and white light stimuli. Our results indicate a strong positive phototactic response to blue light with 86.7% (n = 15) of samples moving towards the light source, while only 20% (n = 15) samples responded similarly to white light (400-700 nm). Locomotion, characterised by periodic pulses lasting 1-2 hours, involved distances up to 220 mm in blue light trials, whereas significantly shorter distances were observed in white light trials (2, 5 and 8 mm). Trails with two light sources reinforced the preference for blue light over white, with all samples consistently moving towards the blue light and away from the white (11, 15 and 3mm). High-resolution time-laps captured the biomechanics of forward motion that appeared driven by three key factors: tissue inflation, which increased contact surface area for lift and friction; the ventral foot/pads, adjusting substrate interaction/friction; and the contraction and twisting of lateral peripheral tissues, which propelled the coral forward in a coordinated manner resembling the pulsed swimming motion of jellyfish. Our findings provide new insights into coral mobility mechanisms, emphasising the role of tissue inflation in active locomotion, with potential implications for coral neural systems, vision and habitat selection.

Disentangling the evolution of cognition: Learning in Cnidaria

Bielecki et al. Current Biology, 33, 4150-4159, (2023) described new behavioral and physiological paradigms to study associative learning and its neural basis in the Cnidaria Tripedalia cystophora. We discuss the relevance of these findings to further our understanding of the intertwined evolution of cognition and the nervous systems.

Contributions of site- and sex-specific LTPs to everyday memory

Commentaries about long-term potentiation (LTP) generally proceed with an implicit assumption that largely the same physiological effect is sampled across different experiments. However, this is clearly not the case. We illustrate the point by comparing LTP in the CA3 projections to CA1 with the different forms of potentiation in the dentate gyrus. These studies lead to the hypothesis that specialized properties of CA1-LTP are adaptations for encoding unsupervised learning and episodic memory, whereas the dentate gyrus variants subserve learning that requires multiple trials and separation of overlapping bodies of information. Recent work has added sex as a second and somewhat surprising dimension along which LTP is also differentiated. Triggering events for CA1-LTP differ between the sexes and the adult induction threshold is significantly higher in females; these findings help explain why males have an advantage in spatial learning. Remarkably, the converse is true before puberty: Females have the lower LTP threshold and are better at spatial memory problems. A mechanism has been identified for the loss-of-function in females but not for the gain-of-function in males. We propose that the many and disparate demands of natural environments, with different processing requirements across ages and between sexes, led to the emergence of multiple LTPs. This article is part of a discussion meeting issue 'Long-term potentiation: 50 years on'.

Uncertainty and anxiety: Evolution and neurobiology

Anxiety is a complex phenomenon: Its eliciting stimuli and circumstances, component behaviors, and functional consequences are only slowly coming to be understood. Here, we examine defense systems from field studies; laboratory studies focusing on experimental analyses of behavior; and, the fear conditioning literature, with a focus on the role of uncertainty in promoting an anxiety pattern that involves high rates of stimulus generalization and resistance to extinction. Respectively, these different areas provide information on evolved elicitors of defense (field studies); outline a defense system focused on obtaining information about uncertain threat (ethoexperimental analyses); and, provide a simple, well-researched, easily measured paradigm for analysis of nonassociative stress-enhanced fear conditioning (the SEFL). Results suggest that all of these-each of which is responsive to uncertainty-play multiple and interactive roles in anxiety. Brain system findings for some relevant models are reviewed, with suggestions that further analyses of current models may be capable of providing a great deal of additional information about these complex interactions and their underlying biology.

Inferring Consciousness in Phylogenetically Distant Organisms

The neural dynamics of subjectivity (NDS) approach to the biological explanation of consciousness is outlined and applied to the problem of inferring consciousness in animals phylogenetically distant from ourselves. The NDS approach holds that consciousness or felt experience is characteristic of systems whose nervous systems have been shaped to realize subjectivity through a combination of network interactions and large-scale dynamic patterns. Features of the vertebrate brain architecture that figure in other accounts of the biology of consciousness are viewed as inessential. Deep phylogenetic branchings in the animal kingdom occurred before the evolution of complex behavior, cognition, and sensing. These capacities arose independently in brain architectures that differ widely across arthropods, vertebrates, and cephalopods, but with conservation of large-scale dynamic patterns of a kind that have an apparent link to felt experience in humans. An evolutionary perspective also motivates a strongly gradualist view of consciousness; a simple distinction between conscious and nonconscious animals will probably be replaced with a view that admits differences of degree, perhaps on many dimensions.

Evolutionary origin of the nervous system from Ctenophora prospective

Nervous system is one of the key adaptations underlying the evolutionary success of the majority of animal groups. Ctenophores (or comb jellies) are gelatinous marine invertebrates that were probably the first lineage to diverge from the rest of animals. Due to the key phylogenetic position and multiple unique adaptations, the noncentralized nervous system of comb jellies has been in the center of the debate around the origin of the nervous system in the animal kingdom and whether it happened only once or twice. Here, we discuss the latest findings in ctenophore neuroscience and multiple challenges on the way to build a clear evolutionary picture of the origin of the nervous system.

Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics

Biological systems interact directly with the environment and learn by receiving multimodal feedback via sensory stimuli that shape the formation of internal neuronal representations. Drawing inspiration from biological concepts such as exploration and sensory processing that eventually lead to behavioral conditioning, we present a robotic system handling objects through multimodal learning. A small-scale organic neuromorphic circuit locally integrates and adaptively processes multimodal sensory stimuli, enabling the robot to interact intelligently with its surroundings. The real-time handling of sensory stimuli via low-voltage organic neuromorphic devices with synaptic functionality forms multimodal associative connections that lead to behavioral conditioning, and thus the robot learns to avoid potentially dangerous objects. This work demonstrates that adaptive neuro-inspired circuitry with multifunctional organic materials, can accommodate locally efficient bio-inspired learning for advancing intelligent robotics.

Executive functions and brain morphology of male and female dominant and subordinate cichlid fish

BACKGROUND

Living in a social dominance hierarchy presents different benefits and challenges for dominant and subordinate males and females, which might in turn affect their cognitive needs. Despite the extensive research on social dominance in group-living species, there is still a knowledge gap regarding how social status impacts brain morphology and cognitive abilities.

METHODS

Here, we tested male and female dominants and subordinates of Neolamprologus pulcher, a social cichlid fish species with size-based hierarchy. We ran three executive cognitive function tests for cognitive flexibility (reversal learning test), self-control (detour test), and working memory (object permanence test), followed by brain and brain region size measurements.

RESULTS

Performance was not influenced by social status or sex. However, dominants exhibited a brain-body slope that was relatively steeper than that of subordinates. Furthermore, individual performance in reversal learning and detour tests correlated with brain morphology, with some trade-offs among major brain regions like telencephalon, cerebellum, and optic tectum.

CONCLUSION

As individuals' brain growth strategies varied depending on social status without affecting executive functions, the different associated challenges might yield a potential effect on social cognition instead. Overall, the findings highlight the importance of studying the individual and not just species to understand better how the individual's ecology might shape its brain and cognition.

Social complexity affects cognitive abilities but not brain structure in a Poeciliid fish

Some cognitive abilities are suggested to be the result of a complex social life, allowing individuals to achieve higher fitness through advanced strategies. However, most evidence is correlative. Here, we provide an experimental investigation of how group size and composition affect brain and cognitive development in the guppy (Poecilia reticulata). For 6 months, we reared sexually mature females in one of 3 social treatments: a small conspecific group of 3 guppies, a large heterospecific group of 3 guppies and 3 splash tetras (Copella arnoldi)-a species that co-occurs with the guppy in the wild, and a large conspecific group of 6 guppies. We then tested the guppies' performance in self-control (inhibitory control), operant conditioning (associative learning), and cognitive flexibility (reversal learning) tasks. Using X-ray imaging, we measured their brain size and major brain regions. Larger groups of 6 individuals, both conspecific and heterospecific groups, showed better cognitive flexibility than smaller groups but no difference in self-control and operant conditioning tests. Interestingly, while social manipulation had no significant effect on brain morphology, relatively larger telencephalons were associated with better cognitive flexibility. This suggests alternative mechanisms beyond brain region size enabled greater cognitive flexibility in individuals from larger groups. Although there is no clear evidence for the impact on brain morphology, our research shows that living in larger social groups can enhance cognitive flexibility. This indicates that the social environment plays a role in the cognitive development of guppies.

Associative learning: Box jellyfish learns to avoid bumps

Operant conditioning - learning to do something for a desired outcome - has never been convincingly demonstrated in Cnidaria. A study now shows that box jellyfish, Tripedalia cystophora, can learn to avoid bumping into an obstacle based on visual cues.