The PhD project is focused on identifying the role of conscious perception through a neuroimaging and computational investigation. The project aims to characterise the neural correlates that support the formation of episodic memories and explain these findings with the Simultaneous Type/ Serial Token (STST) model, a neural network model of temporal attention. The project is competition-funded under the Midlands Integrative Biosciences Training Partnership, supported by the UK Biotechnology and Biological Sciences Research Council.

Facial expressions are inherently dynamic, and our visual system is sensitive to subtle changes in their temporal sequence. However, researchers often use dynamic morphs of photographs—simplified, linear representations of motion—to study the neural correlates of dynamic face perception. To explore the brain's sensitivity to natural facial motion, we constructed a novel dynamic face database using generative neural networks, trained on a verified set of video-recorded emotional expressions. The resulting deepfakes, consciously indistinguishable from videos, enabled us to separate biological motion from photorealistic form. Results showed that conventional dynamic morphs elicit distinct responses in the brain compared to videos and photos, suggesting they violate expectations (n400) and have reduced social salience (late positive potential). This suggests that dynamic morphs misrepresent facial dynamism, resulting in misleading insights about the neural and behavioural correlates of face perception. Deepfakes and videos elicited largely similar neural responses, suggesting they could be used as a proxy for real faces in vision research, where video recordings cannot be experimentally manipulated. And yet, despite being consciously undetectable as fake, deepfakes elicited an expectation violation response in the brain. This points to a neural sensitivity to naturalistic facial motion, beyond conscious awareness. Despite some differences in neural responses, the realism and manipulability of deepfakes make them a valuable asset for research where videos are unfeasible. Using these stimuli, we proposed a novel marker for the conscious perception of naturalistic facial motion – Frontal delta activity – which was elevated for videos and deepfakes, but not for photos or dynamic morphs.

Conscious emotional experiences are very rich in their nature, and can encompass anything ranging from the most intense panic when facing immediate threat, to the overwhelming love felt when meeting your newborn. It is then no surprise that capturing all aspects of emotional consciousness, such as intensity, valence, and bodily responses, into one theory has become the topic of much debate. Key questions in the field concern how we can actually measure emotions and which type of experiments can help us distill the neural correlates of emotional consciousness. In this talk I will give a brief overview of theories of emotional consciousness and where they disagree, after which I will dive into the evidence proposed to support these theories. Along the way I will discuss to what extent studying emotional consciousness is ‘special’ and will suggest several tools and experimental contrasts we have at our disposal to further our understanding on this intriguing topic.

How does our brain generate consciousness, that is, the subjective experience of what it is like to see face or hear a sound? Do we become aware of a stimulus during early sensory processing or only later when information is shared in a wide-spread fronto-parietal network? Neural correlates of consciousness are typically identified by comparing brain activity when a constant stimulus (e.g., a face) is perceived versus not perceived. However, in most previous experiments, conscious perception was systematically confounded with post-perceptual processes such as decision-making and report. In this talk, I will present recent EEG and fMRI studies dissociating neural correlates of consciousness and task-related processing in visual and auditory perception. Our results suggest that consciousness emerges during early sensory processing, while late, fronto-parietal activity is associated with post-perceptual processes rather than awareness. These findings challenge predominant theories of consciousness and highlight the importance of considering task relevance as a confound across different neuroscientific methods, experimental paradigms and sensory modalities.

Neural correlates of visual working memory have been found in early visual, parietal, and prefrontal regions. These findings have spurred fruitful debate over how and where in the brain memories might be represented. Here, I will present data from multiple experiments to demonstrate how a focus on behavioral requirements can unveil a more comprehensive understanding of the visual working memory system. Specifically, items in working memory must be maintained in a highly robust manner, resilient to interference. At the same time, storage mechanisms must preserve a high degree of flexibility in case of changing behavioral goals. Several examples will be explored in which visual memory representations are shown to undergo transformations, and even shift their cortical locus alongside their coding format based on specifics of the task.

The neural correlates of decision-making have been investigated extensively, and recent work aims to identify under what conditions cortex is actually necessary for making accurate decisions. We discovered that mice with distinct cognitive experiences, beyond sensory and motor learning, use different cortical areas and neural activity patterns to solve the same task, revealing past learning as a critical determinant of whether cortex is necessary for decision tasks. We used optogenetics and calcium imaging to study the necessity and neural activity of multiple cortical areas in mice with different training histories. Posterior parietal cortex and retrosplenial cortex were mostly dispensable for accurate performance of a simple navigation-based visual discrimination task. In contrast, these areas were essential for the same simple task when mice were previously trained on complex tasks with delay periods or association switches. Multi-area calcium imaging showed that, in mice with complex-task experience, single-neuron activity had higher selectivity and neuron-neuron correlations were weaker, leading to codes with higher task information. Therefore, past experience is a key factor in determining whether cortical areas have a causal role in decision tasks.

Flow is defined as an altered state of consciousness with excessive attention and enormous sense of pleasure, when engaged in a challenging task, first postulated by a psychologist, the late M. Csikszentmihayli. The main focus of this talk will be “Team Flow,” but there were two lines of previous studies in our laboratory as its background. First is inter-body and inter-brain coordination/synchrony between individuals. Considering various rhythmic echoing/synchronization phenomena in animal behavior, it could be regarded as the biological, sub-symbolic and implicit origin of social interactions. The second line of precursor research is on the state of Solo Flow in game playing. We employed attenuation of AEP (Auditory Evoked Potential) to task-irrelevant sound probes as an objective-neural indicator of such a Flow status, and found that; 1) Mutual link between the ACC & the TP is critical, and 2) overall, top-down influence is enhanced while bottom-up causality is attenuated. Having these as the background, I will present our latest study of Team Flow in game playing. We found that; 3) the neural correlates of Team Flow is distinctively different from those of Solo Flow nor of non-flow social, 4) the left medial temporal cortex seems to form an integrative node for Team Flow, receiving input related to Solo Flow state from the right PFC and input related to social state from the right IFC, and 5) Intra-brain (dis)similarity of brain activity well predicts (dis)similarity of skills/cognition as well as affinity for inter-brain coherence.

It is common for animals to use self-generated movements to actively sense the surrounding environment. For instance, rodents rhythmically move their whiskers to explore the space close to their body. The mouse whisker system has become a standard model to study active sensing and sensorimotor integration through feedback loops. In this work, we developed a bioinspired spiking neural network model of the sensorimotor peripheral whisker system, modelling trigeminal ganglion, trigeminal nuclei, facial nuclei, and central pattern generator neuronal populations. This network was embedded in a virtual mouse robot, exploiting the Neurorobotics Platform, a simulation platform offering a virtual environment to develop and test robots driven by brain-inspired controllers. Eventually, the peripheral whisker system was properly connected to an adaptive cerebellar network controller. The whole system was able to drive active whisking with learning capability, matching neural correlates of behaviour experimentally recorded in mice.

We must constantly adapt the rules we use to guide our attention. To understand how the brain learns these rules, we designed a novel task that required monkeys to learn which color is the most rewarded at a given time (the current rule). However, just as in real life, the monkey was never explicitly told the rule. Instead, they had to learn it through trial and error by choosing a color, receiving feedback (amount of reward), and then updating their internal rule. After the monkeys reached a behavioral criterion, the rule changed. This change was not cued but could be inferred based on reward feedback. Behavioral modeling found monkeys used rewards to learn the rules. After the rule changed, animals adopted one of two strategies. If the change was small, reflected in a small reward prediction error, the animals continuously updated their rule. However, for large changes, monkeys ‘reset’ their belief about the rule and re-learned the rule from scratch. To understand the neural correlates of learning new rules, we recorded neurons simultaneously from the prefrontal and parietal cortex. We found that the strength of the rule representation increased with the certainty about the current rule, and that the certainty about the rule was represented both implicitly and explicitly in the population.

Integrated information theory (IIT) takes as its starting point phenomenology, rather than behavioral, functional, or neural correlates of consciousness. The theory characterizes the essential properties of phenomenal existence—which is immediate and indubitable. These are translated into physical properties, expressed operationally as cause-effect power, which must be satisfied by the neural substrate of consciousness. On this basis, the theory can account for clinical and experimental data about the presence and absence of consciousness. Current work aims at accounting for specific qualities of different experiences, such as spatial extendedness and the flow of time. Several implications of IIT have ethical relevance. One is that functional equivalence does not imply phenomenal equivalence—computers may one day be able to do everything we do, but they will not experience anything. Another is that we do have free will in the fundamental, metaphysical sense—we have true alternatives and we, not our neurons, are the true cause of our willed actions.

Neural correlates of visual working memory have been found in early visual, parietal, and prefrontal regions. These findings have spurred fruitful debate over how and where in the brain memories might be represented. Here, I will present data from multiple experiments to demonstrate how a focus on behavioral requirements can unveil a more comprehensive understanding of the visual working memory system. Specifically, items in working memory must be maintained in a highly robust manner, resilient to interference. At the same time, storage mechanisms must preserve a high degree of flexibility in case of changing behavioral goals. Several examples will be explored in which visual memory representations are shown to undergo transformations, and even shift their cortical locus alongside their coding format based on specifics of the task.

External cues imbued with significance can enhance the motivational state of an organism, trigger related memories and influence future planning and goal directed behavior. At the same time, internal thought and imaginings can moderate and counteract the impact of external motivational cues. The neural underpinnings of imagination have been largely opaque, due to the inherent inaccessibility of mental actions. The talk will describe studies utilizing imagination and tracking how its neural correlates bidirectionally interact with external motivational cues. Stimulus-response associative learning is only one form of memory organization. A more comprehensive and efficient organizational principal is the cognitive map. In the last part of the talk we will examine this concept in the case of abstract memories and social space. Social encounters provide opportunities to become intimate or estranged from others and to gain or lose power over them. The locations of others on the axes of power and affiliation can serve as reference points for our own position in the social space. Research is beginning to uncover the spatial-like neural representation of these social coordinates. We will discuss recent and growing evidence on utilizing the principals of the cognitive map across multiple domains, providing a systematic way of organizing memories to navigate life.

Understanding speech in noisy backgrounds requires selective attention to a particular speaker. Humans excel at this challenging task, while current speech recognition technology still struggles when background noise is loud. The neural mechanisms by which we process speech remain, however, poorly understood, not least due to the complexity of natural speech. Here we describe recent progress obtained through applying machine-learning to neuroimaging data of humans listening to speech in different types of background noise. In particular, we develop statistical models to relate characteristic features of speech such as pitch, amplitude fluctuations and linguistic surprisal to neural measurements. We find neural correlates of speech processing both at the subcortical level, related to the pitch, as well as at the cortical level, related to amplitude fluctuations and linguistic structures. We also show that some of these measures allow to diagnose disorders of consciousness. Our findings may be applied in smart hearing aids that automatically adjust speech processing to assist a user, as well as in the diagnosis of brain disorders.

Having conscious experience is arguably the most important reason why it matters to us whether we are alive or dead. A powerful paradigm to identify neural correlates of consciousness is binocular rivalry, wherein a constant visual stimulus evokes a varying conscious percept. It has recently been suggested that activity modulations observed during rivalry may represent the act of report rather than the conscious percept itself. Here, we performed single-unit recordings from face patches in macaque inferotemporal (IT) cortex using a novel no-report paradigm in which the animal’s conscious percept was inferred from eye movements. These experiments reveal two new results concerning the neural correlates of consciousness. First, we found that high proportions of IT neurons represented the conscious percept even without active report. Using high-channel recordings, including a new 128-channel Neuropixels-like probe, we were able to decode the conscious percept on single trials. Second, we found that even on single trials, modulation to rivalrous stimuli was weaker than that to unambiguous stimuli, suggesting that cells may encode not only the conscious percept but also the suppressed stimulus. To test this hypothesis, we varied the identity of the suppressed stimulus during binocular rivalry; we found that indeed, we could decode not only the conscious percept but also the suppressed stimulus from neural activity. Moreover, the same cells that were strongly modulated by the conscious percept also tended to be strongly modulated by the suppressed stimulus. Together, our findings indicate that (1) IT cortex possesses a true neural correlate of consciousness even in the absence of report, and (2) this correlate consists of a population code wherein single cells multiplex representation of the conscious percept and veridical physical stimulus, rather than a subset of cells perfectly reflecting consciousness.

Despite undeniable progress in the science of consciousness, there is no consensus on even fundamental theoretical and empirical questions, such as whether ‘phenomenal consciousness’ is a scientifically respectable concept, whether phenomenal consciousness overflows access consciousness, or whether the neural correlates of perceptual consciousness are in the front or in the back of the cerebral cortex. Notably, disagreement also concerns proposed theories of consciousness. However, since not all theories are mutually incompatible, there have been attempts to make theoretical progress by integrating or unifying them. I shall argue that this is preferable over proposing yet another theory, but that one should not expect it to yield a complete theory of consciousness. Rather, theoretical work in consciousness research should focus on core hypotheses about consciousness that different theories of consciousness have in common. Such a ‘minimal unifying model’ of consciousness can then be used as a basis for formulating more specific hypotheses about consciousness.

By studying the neural correlates of hand augmentation, we are exploring the boundaries of neuroplasticity seeing how it can be harnessed to improve the usability and control of prosthetic devices. Tamar Makin and Dani Clode each discuss their research and perspectives within the field of prosthetics that has led to this unique collaboration and exploration of motor augmentation and the brain.

Many innate behaviours are the result of multiple sensorimotor programs that are dynamically coordinated to produce higher-order behaviours such as courtship or architecture construction. Extendend phenotypes such as architecture are especially useful for ethological study because the structure itself is a physical record of behavioural intent. A particularly elegant and easily quantifiable structure is the spider orb-web. The geometric symmetry and regularity of these webs have long generated interest in their behavioural origin. However, quantitative analyses of this behaviour have been sparse due to the difficulty of recording web-making in real-time. To address this, we have developed a novel assay enabling real-time, high-resolution tracking of limb movements and web structure produced by the hackled orb-weaver Uloborus diversus. With its small brain size of approximately 100,000 neurons, the spider U. diversus offers a tractable model organism for the study of complex behaviours. Using deep learning frameworks for limb tracking, and unsupervised behavioural clustering methods, we have developed an atlas of stereotyped movement motifs and are investigating the behavioural state transitions of which the geometry of the web is an emergent property. In addition to tracking limb movements, we have developed algorithms to track the web’s dynamic graph structure. We aim to model the relationship between the spider’s sensory experience on the web and its motor decisions, thereby identifying the sensory and internal states contributing to this sensorimotor transformation. Parallel efforts in our group are establishing 2-photon in vivo calcium imaging protocols in this spider, eventually facilitating a search for neural correlates underlying the internal and sensory state variables identified by our behavioural models. In addition, we have assembled a genome, and are developing genetic perturbation methods to investigate the genetic underpinnings of orb-weaving behaviour. Together, we aim to understand how complex innate behaviours are coordinated by underlying neuronal and genetic mechanisms.

Human learners acquire not only disconnected bits of information, but complex interconnected networks of relational knowledge. The capacity for such learning naturally depends on the architecture of the knowledge network itself, and also on the architecture of the computational unit – the brain – that encodes and processes the information. Here, I will discuss emerging work assessing network constraints on the learnability of relational knowledge, and the neural correlates of that learning.