Understanding what makes human unique is a fundamental research drive for comparative psychologists. Cognitive abilities such as theory of mind, cooperation or mental time travel have been considered uniquely human. Despite empirical evidence showing that animals other than humans are able (to some extent) of these cognitive achievements, findings are still heavily contested. In this context, being able to abstract relations of similarity has also been considered one of the hallmarks of human cognition. While previous research has shown that other animals (e.g., primates) can attend to relational similarity, less is known about what invertebrates can do. In this talk, I will present a series of spatial matching tasks that previously were used with children and great apes and that I adapted for use with wild-caught bumblebees. The findings from these studies suggest striking similarities between vertebrates and invertebrates in their abilities to attend to relational similarity.

Can nonhuman animals perceive the relation-between-relations? This intriguing question has been studied over the last 40 years; nonetheless, the extent to which nonhuman species can do so remains controversial. Here, I review empirical evidence suggesting that pigeons, parrots, crows, and baboons join humans in reliably acquiring and transferring relational matching-to-sample (RMTS). Many theorists consider that RMTS captures the essence of analogy, because basic to analogy is appreciating the ‘relation between relations.’ Factors affecting RMTS performance include: prior training experience, the entropy of the sample stimulus, and whether the items that serve as sample stimuli can also serve as choice stimuli.

Alterations in sleep are hallmarks of the ageing process and emerges as risk factors for Alzheimer’s disease (AD). While the fine-tuned coalescence of sleep microstructure elements may influence age-related cognitive trajectories, its association with AD-related processes is not fully established. We investigated whether sleep arousals and the coupling of spindles and slow waves, key elements of sleep microstructure, are associated with early amyloid-beta (Aβ) brain burden, hallmark of AD neuropathology, and cognitive change at 2 years in 100 late-midlife healthy individuals. We first found that arousals interrupting sleep continuity were positively linked to Aβ burden, while, by contrast, the more prevalent arousals upholding sleep continuity were associated with lower Aβ burden and better cognition. We further found that young-like co-occurrence of spindles and slow-depolarisation slow waves is associated to lower burden of Aβ over the medial prefrontal cortex and is predictive of memory decline at 2-year follow-up. We provide empirical evidence that arousals are diverse and differently associated with early AD-related neuropathology and cognition. We further show the altered coupling of sleep microstructure elements that are key to its mnesic functions may contribute to poorer brain and cognitive trajectories. The presentation will end with preliminary data show that activity of the locus coeruleus, essential to sleep and showing some of the earliest signs of AD-related pathological processes, is associated with sleep quality. These preliminary findings are the first of a project ailed at link sleep and AD through the locus coeruleus.

The theory of predictive processing posits that the brain computes expectations to process information predictively. Empirical evidence in support of this theory, however, is scarce and largely limited to sensory areas. Here, we report a precise and adaptive mechanism in the frontal cortex of non-human primates consistent with predictive processing of temporal events. We found that the speed of neural dynamics is precisely adjusted according to the average time of an expected stimulus. This speed adjustment, in turn, enables neurons to encode stimuli in terms of deviations from expectation. This lawful relationship was evident across multiple experiments and held true during learning: when temporal statistics underwent covert changes, neural responses underwent predictable changes that reflected the new mean. Together, these results highlight a precise mathematical relationship between temporal statistics in the environment and neural activity in the frontal cortex that may serve as a mechanism for predictive temporal processing.

Empirical evidence and theoretical models both increasingly emphasize the importance of interoceptive processing in mental health. Indeed, many mood and psychiatric disorders involve disturbed feelings and/or beliefs about the visceral body. However, current methods to measure interoceptive ability are limited in a number of ways, restricting the utility and interpretation of interoceptive biomarkers in psychiatry. I will present some newly developed measures and models which aim to improve our understanding of disordered brain-body interaction in psychiatric illnesses.

I argue that the evolution of our life history, with its distinctively long, protected human childhood allows an early period of broad hypothesis search and exploration, before the demands of goal-directed exploitation set in. This cognitive profile is also found in other animals and is associated with early behaviours such as neophilia and play. I relate this developmental pattern to computational ideas about explore-exploit trade-offs, search and sampling, and to neuroscience findings. I also present several lines of new empirical evidence suggesting that young human learners are highly exploratory, both in terms of their search for external information and their search through hypothesis spaces. In fact, they are sometimes more exploratory than older learners and adults.

Transformative advances in deep learning, such as deep reinforcement learning, usually rely on gradient-based learning methods such as backpropagation through time (BPTT) as a core learning algorithm. However, BPTT is not argued to be biologically plausible, since it requires to a propagate gradients backwards in time and across neurons. Here, we propose e-prop, a novel gradient-based learning method with local and online weight update rules for recurrent neural networks, and in particular recurrent spiking neural networks (RSNNs). As a result, e-prop has the potential to provide a substantial fraction of the power of deep learning to RSNNs. In this presentation, we will motivate e-prop from the perspective of recent insights in neuroscience and show how these have to be combined to form an algorithm for online gradient descent. The mathematical results will be supported by empirical evidence in supervised and reinforcement learning tasks. We will also discuss how limitations that are inherited from gradient-based learning methods, such as sample-efficiency, can be addressed by considering an evolution-like optimization that enhances learning on particular task families. The emerging learning architecture can be used to learn tasks by a single demonstration, hence enabling one-shot learning.