Seminars

Cerebrovascular support is critical for healthy cognitive ageing. Reduced cerebral blood flow in ageing is caused, among other things, by hypertension, arteriosclerosis (i.e. stiffening of the arteries) and plaque formation. Arterial stiffness is predictive of cognitive decline, is a critical risk factor for cerebrovascular accidents, and has been linked to heightened risks for Alzheimer’s Disease and other forms of dementia. The elasticity of cerebral arteries is influenced by lifestyle factors, including cardiorespiratory fitness. Monica will discuss data obtained in their laboratory with new noninvasive measures of cerebrovascular health (pulse-DOT, a diffuse optical tomographic method for studying cerebral arteriosclerosis), in conjunction with structural and functional brain measures and cognitive assessments. These findings support a model in which localised changes in arteriosclerosis lead to specific profiles of structural, functional, and cognitive declines, paving a way to individualised interventions.

Face detection is an initial step of many social interactions involving a comparison between a visual input and a mental representation of faces, built from previous experience. Whilst emotional state was found to affect the way humans attend to faces, little research has explored the effects of emotions on the mental representation of faces. Here, we examined the specific perceptual modulation of geometric properties of the mental representations associated with state anxiety and state depression on face detection, and to compare their emotional expression. To this end, we used an adaptation of the reverse correlation technique inspired by Gosselin and Schyns’, (2003) ‘Superstitious Approach’, to construct visual representations of observers’ mental representations of faces and to relate these to their mental states. In two sessions, on separate days, participants were presented with ‘colourful’ noise stimuli and asked to detect faces, which they were told were present. Based on the noise fragments that were identified as faces, we reconstructed the pictorial mental representation utilised by each participant in each session. We found a significant correlation between the size of the mental representation of faces and participants’ level of depression. Our findings provide a preliminary insight about the way emotions affect appearance expectation of faces. To further understand whether the facial expressions of participants’ mental representations reflect their emotional state, we are conducting a validation study with a group of naïve observers who are asked to classify the reconstructed face images by emotion. Thus, we assess whether the faces communicate participants’ emotional states to others.

Post-diagnostic research on rare genetic developmental disorders presents new opportunities (and a few challenges) for discovery neuroscience and translation. In this talk, Kate will describe and discuss neurodevelopmental phenotypes arising from rare, high penetrance genomic variants which directly influence pre-synaptic vesicle cycling (SVC disorders). She will focus on Synaptotagmin-1 Associated Neurodevelopmental Disorder (also known as Baker Gordon Syndrome), first described in 2015 and now diagnosed in more than 50 children and young people worldwide. She will then present work-in-progress by her group on the neurodevelopmental spectrum of SVC disorders more broadly, and discuss opportunities for collaborative neuroscience which can bridge the gaps between genetic cause and complex neurological, cognitive and mental health outcomes.

Several species of insects have been observed to perform accurate path integration, constantly updating a vector memory of their location relative to a starting position, which they can use to take a direct return path. Foraging insects such as bees and ants are also able to store and recall the vectors to return to food locations, and to take novel shortcuts between these locations. Other insects, such as dung beetles, are observed to integrate multimodal directional cues in a manner well described by vector addition. All these processes appear to be functions of the Central Complex, a highly conserved and strongly structured circuit in the insect brain. Modelling this circuit, at the single neuron level, suggests it has general capabilities for vector encoding, vector memory, vector addition and vector rotation that can support a wide range of directed and navigational behaviours.

We recorded and analyzed the population activity of hundreds of neurons in the medial premotor areas (MPC) of rhesus monkeys performing an isochronous tapping task guided by brief flashing stimuli or auditory tones. The animals showed a strong bias towards visual metronomes, with rhythmic tapping that was more precise and accurate than for auditory metronomes. The population dynamics in state space as well as the corresponding neural sequences shared the following properties across modalities: the circular dynamics of the neural trajectories and the neural sequences formed a regenerating loop for every produced interval, producing a relative time representation; the trajectories converged in similar state space at tapping times while the moving bumps restart at this point, resetting the beat-based clock; the tempo of the synchronized tapping was encoded by a combination of amplitude modulation and temporal scaling in the neural trajectories. In addition, the modality induced a displacement in the neural trajectories in auditory and visual subspaces without greatly altering time keeping mechanism. These results suggest that the interaction between the amodal internal representation of pulse within MPC and a modality specific external input generates a neural rhythmic clock whose dynamics define the temporal execution of tapping using auditory and visual metronomes.

Faces and voices convey much of the non-verbal information that we use when communicating with other people. We look at faces and listen to voices to recognize others, understand how they are feeling, and decide how to act. Recent research in my lab aims to investigate whether there are similar coding mechanisms to represent faces and voices, and whether there are brain regions that integrate information across the visual and auditory modalities. In the first part of my talk, I will focus on an fMRI study in which we found that a region of the posterior STS exhibits modality-general representations of familiar people that can be similarly driven by someone’s face and their voice (Tsantani et al. 2019). In the second part of the talk, I will describe our recent attempts to shed light on the type of information that is represented in different face-responsive brain regions (Tsantani et al., 2021).

Gut-derived signals regulate metabolism, appetite, and behaviors important for mental health. We have performed a large-scale multidimensional screen to identify gut hormones and nutrient-sensing mechanisms in the intestine that regulate metabolism and behavior in the fruit fly Drosophila. We identified several gut hormones that affect fecundity, stress responses, metabolism, feeding, and sleep behaviors, many of which seem to act sex-specifically. We show that in response to nutrient intake, the enteroendocrine cells (EECs) of the adult Drosophila midgut release hormones that act via inter-organ relays to coordinate metabolism and feeding decisions. These findings suggest that crosstalk between the gut and other tissues regulates food choice according to metabolic needs, providing insight into how that intestine processes nutritional inputs and into the gut-derived signals that relay information regulating nutrient-specific hungers to maintain metabolic homeostasis.

The goal of neuroscience is to understand how the nervous system controls behaviour, not only in the simplified environments of the lab, but also in the natural environments for which nervous systems evolved. In pursuing this goal, neuroscience research is supported by an ever-larger toolbox, ranging from optogenetics to connectomics. However, often these tools are coupled with reductionist approaches for linking nervous systems and behaviour. This course will introduce advanced techniques for measuring and analysing behaviour, as well as three fundamental principles as necessary to understanding biological behaviour: (1) morphology and environment; (2) action-perception closed loops and purpose; and (3) individuality and historical contingencies [1]. [1] Gomez-Marin, A., & Ghazanfar, A. A. (2019). The life of behavior. Neuron, 104(1), 25-36

A major challenge for causal inference from time-series data is the trade-off between computational feasibility and accuracy. Motivated by process motifs for lagged covariance in an autoregressive model with slow mean-reversion, we propose to infer networks of causal relations via pairwise edge measure (PEMs) that one can easily compute from lagged correlation matrices. Motivated by contributions of process motifs to covariance and lagged variance, we formulate two PEMs that correct for confounding factors and for reverse causation. To demonstrate the performance of our PEMs, we consider network interference from simulations of linear stochastic processes, and we show that our proposed PEMs can infer networks accurately and efficiently. Specifically, for slightly autocorrelated time-series data, our approach achieves accuracies higher than or similar to Granger causality, transfer entropy, and convergent crossmapping -- but with much shorter computation time than possible with any of these methods. Our fast and accurate PEMs are easy-to-implement methods for network inference with a clear theoretical underpinning. They provide promising alternatives to current paradigms for the inference of linear models from time-series data, including Granger causality, vector-autoregression, and sparse inverse covariance estimation.

In popular media, there is often a connection drawn between the advent of awareness in artificial agents and those same agents simultaneously achieving human or superhuman level intelligence. In this talk, I will examine the validity and potential application of this seemingly intuitive link between consciousness and intelligence. I will do so by examining the cognitive abilities associated with three contemporary theories of conscious function: Global Workspace Theory (GWT), Information Generation Theory (IGT), and Attention Schema Theory (AST), and demonstrating that all three theories specifically relate conscious function to some aspect of domain-general intelligence in humans. With this insight, we will turn to the field of Artificial Intelligence (AI) and find that, while still far from demonstrating general intelligence, many state-of-the-art deep learning methods have begun to incorporate key aspects of each of the three functional theories. Given this apparent trend, I will use the motivating example of mental time travel in humans to propose ways in which insights from each of the three theories may be combined into a unified model. I believe that doing so can enable the development of artificial agents which are not only more generally intelligent but are also consistent with multiple current theories of conscious function.

In the first half of my talk, I will discuss our recent work on the midbrain dopamine system. The hypothesis that midbrain dopamine neurons broadcast an error signal for the prediction of reward is among the great successes of computational neuroscience. However, our recent results contradict a core aspect of this theory: that the neurons uniformly convey a scalar, global signal. I will review this work, as well as our new efforts to update models of the neural basis of reinforcement learning with our data. In the second half of my talk, I will discuss our recent findings of state-dependent decision-making mechanisms in the striatum.

Analogical reasoning –learning about new things by relating it to previous knowledge– lies at the heart of human intelligence and creativity and forms the core of educational practice. Children start creating and using analogies early on, making incredible progress moving from associative processes to successful analogical reasoning. For example, if we ask a four-year-old “Horse belongs to stable like chicken belongs to …?” they may use association and reply “egg”, whereas older children will likely give the intended relational response “chicken coop” (or other term to refer to a chicken’s home). Interestingly, despite state-of-the-art AI-language models having superhuman encyclopedic knowledge and superior memory and computational power, our pilot studies show that these large language models often make mistakes providing associative rather than relational responses to verbal analogies. For example, when we asked four- to eight-year-olds to solve the analogy “body is to feet as tree is to …?” they responded “roots” without hesitation, but large language models tend to provide more associative responses such as “leaves”. In this study we examine the similarities and differences between children's and six large language models' (Dutch/multilingual models: RobBERT, BERT-je, M-BERT, GPT-2, M-GPT, Word2Vec and Fasttext) responses to verbal analogies extracted from an online adaptive learning environment, where >14,000 7-12 year-olds from the Netherlands solved 20 or more items from a database of 900 Dutch language verbal analogies.

The development of circuit-based therapeutics to treat neurological and neuropsychiatric diseases require detailed localization and understanding of electrophysiological signals in the human brain. Electrodes can record and stimulate circuits in many ways, and we often rely on non-invasive imaging methods to predict the location to implant electrodes. However, electrophysiological and imaging signals measure the underlying tissue in a fundamentally different manner. To integrate multimodal data and benefit from these complementary measurements, I will describe an approach that considers how different measurements integrate signals across the underlying tissue. I will show how this approach helps relate fMRI and intracranial EEG measurements and provides new insights into how electrical stimulation influences human brain networks.