Cognition seminars

The Neural Engineering Framework has been used to create a wide variety of biologically realistic brain simulations that are capable of performing simple cognitive tasks (remembering a list, counting, etc.). This includes the largest existing functional brain model. This talk will describe this method, and show some examples of using it to take high-level cognitive algorithms and convert them into a neural network that implements those algorithms. Overall, this approach gives us new ways of thinking about how the brain works and what sorts of algorithms it is capable of performing.

Understanding and remembering realistic experiences in our everyday lives requires activating many kinds of structured knowledge about the world, including spatial maps, temporal event scripts, and semantic relationships. My recent projects have explored the ways in which we build up this schematic knowledge (during a single experiment and across developmental timescales) and can strategically deploy them to construct event representations that we can store in memory or use to make predictions. I will describe my lab's ongoing work developing new experimental and analysis techniques for conducting functional MRI experiments using narratives, movies, poetry, virtual reality, and "memory experts" to study complex naturalistic schemas.

The neural mechanisms underlying decision-making are typically examined by statistical analysis of large numbers of trials from sequentially recorded single neurons. Averaging across sequential recordings, however, obscures important aspects of decision-making such as variations in confidence and 'changes of mind' (CoM) that occur at variable times on different trials. I will show that the covert decision variables (DV) can be tracked dynamically on single behavioral trials via simultaneous recording of large neural populations in prefrontal cortex. Vacillations of the neural DV, in turn, identify candidate CoM in monkeys, which closely match the known properties of human CoM. Thus simultaneous population recordings can provide insight into transient, internal cognitive states that are otherwise undetectable.

The concept of a dynamic excitation / inhibition ratio, that can shape information flow in cortical circuits during complex behavioural tasks due to circuit disinhibition, has recently arisen as an important and conserved processing motif. It has been also recognized that, in cortical circuits, a subpopulation of GABAergic cells that express vasoactive intestinal polypeptide (VIP) innervates selectively inhibitory interneurons, providing for circuit disinhibition as a possible outcome, depending on the network state and behavioural context. In this talk, I will highlight the latest discoveries on the dynamic organization of hippocampal disinhibitory circuits with a focus on VIP-expressing interneurons. I will discuss the neuron types that can be involved in disinhibition and their local circuit and long-range synaptic connections. I will also discuss some recent findings on how hippocampal VIP circuits may coordinate spatial learning.

To uncover the circuit-level alterations that underlie atypical sensory processing associated with autism, we have adopted a symptom-to-circuit approach in theFmr1-/- mouse model of Fragile X syndrome (FXS). Using a go/no-go task and in vivo 2-photon calcium imaging, we find that impaired visual discrimination in Fmr1-/- mice correlates with marked deficits in orientation tuning of principal neurons in primary visual cortex, and a decrease in the activity of parvalbumin (PV) interneurons. Restoring visually evoked activity in PV cells in Fmr1-/- mice with a chemogenetic (DREADD) strategy was sufficient to rescue their behavioural performance. Strikingly, human subjects with FXS exhibit similar impairments in visual discrimination as Fmr1-/- mice. These results suggest that manipulating inhibition may help sensory processing in FXS. More recently, we find that the ability of Fmr1-/- mice to perform the visual discrimination task is also drastically impaired in the presence of visual or auditory distractors, suggesting that sensory hypersensitivity may affect perceptual learning in autism.

Speakers will highlight research on the developmental processes underlying cognitive control and the effects of environmental risk factors on neural pathways in human cognitive development. Gaia Scerif, from University of Oxford, will be giving a talk on Using developmental cognitive neuroscience tools to investigate mechanisms of atypical cognitive control, followed by Kirsten Donald, from University of Cape Town, who will give a talk titled Neuroimaging the very young high risk brain: lessons from a south African birth cohort.

Cognitive tasks typically require the integration of working memory, contextual processing, and planning to be carried out in close coordination. However, these computations are typically studied within neuroscience as independent modular processes in the brain. In this talk I will present an alternative view, that neural representations of mappings between expected stimuli and contingent goal actions can unify working memory and planning computations. We term these stored maps contingency representations. We developed a "conditional delayed logic" task capable of disambiguating the types of representations used during performance of delay tasks. Human behaviour in this task is consistent with the contingency representation, and not with traditional sensory models of working memory. In task-optimized artificial recurrent neural network models, we investigated the representational geometry and dynamical circuit mechanisms supporting contingency-based computation, and show how contingency representation explains salient observations of neuronal tuning properties in prefrontal cortex. Finally, our theory generates novel and falsifiable predictions for single-unit and population neural recordings.

Prof Caswell Barry, Professorial Research Fellow, Cell & Developmental Biology, Division of Biosciences, University College London. He and his team are trying to understand how the brain works - how it creates that experience of being human, and more specifically, how the brain creates, stores, and updates memories for places and events. They are trying to answer this is by studying areas of the brain linked to memory, the hippocampus and associated sections of cortex – by recording the activity of neurons in these areas we can visualise and hopefully understand the processes the trigger memory formation and retrieval.

In his bestseller 'Thinking, Fast and Slow', Daniel Kahneman popularized the idea that there are two fundamentally different process of thought: a 'System 1' process that is unconscious and instinctive, and a 'System 2' process that is deliberative and requires conscious attention. There is a growing recognition that machine learning is mostly stuck at the 'System 1' level of cognition, and that moving to 'System 2' methods are key to solving long-standing challenges such as out-of-distribution generalization. In this talk, AlphaZero will be used as a case-study of the power of combining 'System 1' and 'System 2' processes. The similarities and differences between AlphaZero and human learning will be explored, along with drawing lessons for the future of machine learning.

Retinal projections often poorly represent the structure of the physical world: well-defined boundaries within the eye may correspond to irrelevant features of the physical world, while critical features of the physical world may be nearly invisible at the retinal projection. Visual cortex is equipped with specialized mechanisms for sorting these two types of features according to their utility in interpreting the scene, however we know little or nothing about their perceptual computations. I will present novel paradigms for the characterization of these processes in human vision, alongside examples of how the associated empirical results can be combined with targeted models to shape our understanding of the underlying perceptual mechanisms. Although the emerging view is far from complete, it challenges compartmentalized notions of bottom-up/top-down object segmentation, and suggests instead that these two modes are best viewed as an integrated perceptual mechanism.

How are actions linked with subsequent outcomes to guide choices? The nucleus accumbens (NAc), which is implicated in this process, receives glutamatergic inputs from the prelimbic cortex (PL) and midline regions of the thalamus (mTH). However, little is known about what is represented in PL or mTH neurons that project to NAc (PL-NAc and mTH-NAc). By comparing these inputs during a reinforcement learning task in mice, we discovered that i) PL-NAc preferentially represents actions and choices, ii) mTH-NAc preferentially represents cues, iii) choice-selective activity in PL-NAc is organized in sequences that persist beyond the outcome. Through computational modelling, we demonstrate that these sequences can support the neural implementation of temporal difference learning, a powerful algorithm to connect actions and outcomes across time. Finally, we test and confirm predictions of our circuit model by direct manipulation of PL-NAc neurons. Thus, we integrate experiment and modelling to suggest a neural solution for credit assignment.

Cognitive architectures are attempts to build larger-scale models of minds. This talk will explore how structure-mapping models of analogical matching, retrieval, and generalization are used in the Companion cognitive architecture. Examples will include modeling conceptual change, learning by reading, and analogical Q/A training.

The curse of dimensionality plagues models of reinforcement learning and decision-making. The process of abstraction solves this by constructing abstract variables describing features shared by different specific instances, reducing dimensionality and enabling generalization in novel situations. We characterized neural representations in monkeys performing a task where a hidden variable described the temporal statistics of stimulus-response-outcome mappings. Abstraction was defined operationally using the generalization performance of neural decoders across task conditions not used for training. This type of generalization requires a particular geometric format of neural representations. Neural ensembles in dorsolateral pre-frontal cortex, anterior cingulate cortex and hippocampus, and in simulated neural networks, simultaneously represented multiple hidden and explicit variables in a format reflecting abstraction. Task events engaging cognitive operations modulated this format. These findings elucidate how the brain and artificial systems represent abstract variables, variables critical for generalization that in turn confers cognitive flexibility.

It is a truth universally acknowledged that relational reasoning is important for learning in Science, Technology, Engineering, and Mathematics (STEM) disciplines. However, much research on relational reasoning uses examples unrelated to STEM concepts (understandably, to control for prior knowledge in many cases). In this talk I will discuss how real STEM concepts can be profitably used in relational reasoning research, using fraction concepts in mathematics as an example.

The last quarter century has provided extensive evidence that some regions of the human cortex are selectively engaged in processing a single specific domain of information, from faces, places, and bodies to language, music, and other people’s thoughts. This work dovetails with earlier theories in cognitive science highlighting domain specificity in human cognition, development, and evolution. But many questions remain unanswered about even the clearest cases of domain specificity in the brain, the selective engagement of the FFA, PPA, and EBA in the perception of faces, places, and bodies, respectively. First, these claims lack precision, saying little about what is computed and how, and relying on human judgements to decide what counts as a face, place, or body. Second, they provide no account of the reliably varying responses of these regions across different “preferred” images, or across different “nonpreferred” images for each category. Third, the category selectivity of each region is vulnerable to refutation if any of the vast set of as-yet-untested nonpreferred images turns out to produce a stronger response than preferred images for that region. Fourth, and most fundamentally, they provide no account of why, from a computational point of view, brains should exhibit this striking degree of functional specificity in the first place, and why we should have the particular visual specializations we do, for faces, places, and bodies, but not (apparently) for food or snakes. The advent of convolutional neural networks (CNNs) to model visual processing in the ventral pathway has opened up many opportunities to address these long-standing questions in new ways. I will describe ongoing efforts in our lab to harness CNNs to do just that.