The successful applicant will work on a multidisciplinary collaborative project aiming to determine the importance of cortical engram cells in memory formation and storage and probe the role of cortical memory engrams in the generation and retrieval of a sensory-based memory. The project as a whole combines computational modeling, electrophysiology, calcium imaging techniques, and molecular and behavioral experiments. First, the biophysical properties of engrams will be identified in a cortical area of interest, and their functional role will be unraveled in vivo. Then, computational modeling will be used to determine the role of engram cells during memory recall. This project is a collaboration between the Florey Institute of Neuroscience and Mental Health in Melbourne, Australia (Prof. L. Palmer), and the University of Dublin, Ireland (Prof. T. Ryan).

How can the hippocampal system transition from episodic one-shot learning to a multi-shot learning regime and what is the utility of the resultant neural representations? This talk will explore the role of the dentate gyrus (DG) anatomy in this context. The canonical DG model suggests it performs pattern separation. More recent experimental results challenge this standard model, suggesting DG function is more complex and also supports the precise binding of objects and events to space and the integration of information across episodes. Very recent studies attribute pattern separation and pattern integration to anatomically distinct parts of the DG (the suprapyramidal blade vs the infrapyramidal blade). We propose a computational model that investigates this distinction. In the model the two processing streams (potentially localized in separate blades) contribute to the storage of distinct episodic memories, and the integration of information across episodes, respectively. The latter forms generalized expectations across episodes, eventually forming a cognitive map. We train the model with two data sets, MNIST and plausible entorhinal cortex inputs. The comparison between the two streams allows for the calculation of a prediction error, which can drive the storage of poorly predicted memories and the forgetting of well-predicted memories. We suggest that differential processing across the DG aids in the iterative construction of spatial cognitive maps to serve the generation of location-dependent expectations, while at the same time preserving episodic memory traces of idiosyncratic events.

Qualitative coding is the process of categorizing and labeling raw data to identify themes, patterns, and concepts within qualitative research. This process requires significant time, reflection, and discussion, often characterized by inherent subjectivity and uncertainty. Here, we explore the possibility to leverage large language models (LLM) to enhance the process and assist researchers with qualitative coding. LLMs, trained on extensive human-generated text, possess an architecture that renders them capable of understanding the broader context of a conversation or text. This allows them to extract patterns and meaning effectively, making them particularly useful for the accurate extraction and coding of relevant themes. In our current approach, we employed the chatGPT 3.5 Turbo API, integrating it into the qualitative coding process for data from the SWISS100 study, specifically focusing on data derived from centenarians' experiences during the Covid-19 pandemic, as well as a systematic centenarian literature review. We provide several instances illustrating how our approach can assist researchers with extracting and coding relevant themes. With data from human coders on hand, we highlight points of convergence and divergence between AI and human thematic coding in the context of these data. Moving forward, our goal is to enhance the prototype and integrate it within an LLM designed for local storage and operation (LLaMa). Our initial findings highlight the potential of AI-enhanced qualitative coding, yet they also pinpoint areas requiring attention. Based on these observations, we formulate tentative recommendations for the optimal integration of LLMs in qualitative coding research. Further evaluations using varied datasets and comparisons among different LLMs will shed more light on the question of whether and how to integrate these models into this domain.

Working memory is about the past but for the future. Adopting such a future-focused perspective shifts the narrative of working memory as a limited-capacity storage system to working memory as an anticipatory buffer that helps us prepare for potential and sequential upcoming behaviour. In my talk, I will present a series of our recent studies that have started to reveal emerging principles of a working memory that looks forward – highlighting, amongst others, how selective attention plays a vital role in prioritising internal contents for behaviour, and the bi-directional links between visual working memory and action. These studies show how studying the dynamics of working memory, selective attention, and action together paves way for an integrated understanding of how mind serves behaviour.

The nervous system is fundamentally a closed loop control device: the output of actions continually influences the internal state and subsequent actions. This is true at the single cell and even the molecular level, where “actions” take the form of signals that are fed back to achieve a variety of functions, including homeostasis, excitability and various kinds of multistability that allow switching and storage of memory. It is also true at the behavioural level, where an animal’s motor actions directly influence sensory input on short timescales, and higher level information about goals and intended actions are continually updated on the basis of current and past actions. Studying the brain in a closed loop setting requires a multidisciplinary approach, leveraging engineering and theory as well as advances in measuring and manipulating the nervous system. I will describe our recent attempts to achieve this fusion of approaches at multiple levels in the nervous system, from synaptic signalling to closed loop brain machine interfaces.

Cognitive neuroscience has witnessed great progress since modern neuroimaging embraced an open science framework, with the adoption of shared principles (Wilkinson et al., 2016), standards (Gorgolewski et al., 2016), and ontologies (Poldrack et al., 2011), as well as practices of meta-analysis (Yarkoni et al., 2011; Dockès et al., 2020) and data sharing (Gorgolewski et al., 2015). However, while functional neuroimaging data provide correlational maps between cognitive functions and activated brain regions, its usefulness in determining causal link between specific brain regions and given behaviors or functions is disputed (Weber et al., 2010; Siddiqiet al 2022). On the contrary, neuropsychological data enable causal inference, highlighting critical neural substrates and opening a unique window into the inner workings of the brain (Price, 2018). Unfortunately, the adoption of Open Science practices in clinical settings is hampered by several ethical, technical, economic, and political barriers, and as a result, open platforms enabling access to and sharing clinical (meta)data are scarce (e.g., Larivière et al., 2021). We are working with clinicians, neuroimagers, and software developers to develop an open source platform for the storage, sharing, synthesis and meta-analysis of human clinical data to the service of the clinical and cognitive neuroscience community so that the future of neuropsychology can be transdiagnostic, open, and FAIR. We call it neurocausal (https://neurocausal.github.io).

A long standing challenge in biological and artificial intelligence is to understand how new knowledge can be constructed from known building blocks in a way that is amenable for computation by neuronal circuits. Here we focus on the task of storage and recall of structured knowledge in long-term memory. Specifically, we ask how recurrent neuronal networks can store and retrieve multiple knowledge structures. We model each structure as a set of binary relations between events and attributes (attributes may represent e.g., temporal order, spatial location, role in semantic structure), and map each structure to a distributed neuronal activity pattern using a vector symbolic architecture (VSA) scheme. We then use associative memory plasticity rules to store the binarized patterns as fixed points in a recurrent network. By a combination of signal-to-noise analysis and numerical simulations, we demonstrate that our model allows for efficient storage of these knowledge structures, such that the memorized structures as well as their individual building blocks (e.g., events and attributes) can be subsequently retrieved from partial retrieving cues. We show that long-term memory of structured knowledge relies on a new principle of computation beyond the memory basins. Finally, we show that our model can be extended to store sequences of memories as single attractors.

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.

Change is ubiquitous in living beings. In particular, the connectome and neural representations can change. Nevertheless behaviors and memories often persist over long times. In a standard model, associative memories are represented by assemblies of strongly interconnected neurons. For faithful storage these assemblies are assumed to consist of the same neurons over time. We propose a contrasting memory model with complete temporal remodeling of assemblies, based on experimentally observed changes of synapses and neural representations. The assemblies drift freely as noisy autonomous network activity or spontaneous synaptic turnover induce neuron exchange. The exchange can be described analytically by reduced, random walk models derived from spiking neural network dynamics or from first principles. The gradual exchange allows activity-dependent and homeostatic plasticity to conserve the representational structure and keep inputs, outputs and assemblies consistent. This leads to persistent memory. Our findings explain recent experimental results on temporal evolution of fear memory representations and suggest that memory systems need to be understood in their completeness as individual parts may constantly change.

Over the course of a lifetime, we process a continual stream of information. Extracted from this stream, memories must be efficiently encoded and stored in an addressable manner for retrieval. To explore potential mechanisms, we consider a familiarity detection task where a subject reports whether an image has been previously encountered. We design a feedforward network endowed with synaptic plasticity and an addressing matrix, meta-learned to optimize familiarity detection over long intervals. We find that anti-Hebbian plasticity leads to better performance than Hebbian and replicates experimental results such as repetition suppression. A combinatorial addressing function emerges, selecting a unique neuron as an index into the synaptic memory matrix for storage or retrieval. Unlike previous models, this network operates continuously, and generalizes to intervals it has not been trained on. Our work suggests a biologically plausible mechanism for continual learning, and demonstrates an effective application of machine learning for neuroscience discovery.

The nature of facial information that is stored by humans to recognise large amounts of faces is unclear despite decades of research in the field. To complicate matters further, little is known about how representations may evolve as novel faces become familiar, and there are large individual differences in the ability to recognise faces. I will present a theory I am developing and that assumes that facial representations are cost-efficient. In that framework, individual facial representations would incorporate different diagnostic features in different faces, regardless of familiarity, and would evolve depending on the relative stability in appearance over time. Further, coarse information would be prioritised over fine details in order to decrease storage demands. This would create low-cost facial representations that refine over time if appearance changes. Individual differences could partly rest on that ability to refine representation if needed. I will present data collected in the general population and in participants with developmental prosopagnosia. In support of the proposed view, typical observers and those with developmental prosopagnosia seem to rely on coarse peripheral features when they have no reason to expect someone’s appearance will change in the future.

Neurons in the brain communicate through action potentials (spikes) that are transmitted through chemical synapses. Throughout the last decades, the question how networks of spiking neurons represent and process information has remained an important challenge. Some progress has resulted from a recent family of supervised learning rules (tempotrons) for models of spiking neurons. However, these studies have viewed synaptic transmission as static and characterized synaptic efficacies as scalar quantities that change only on slow time scales of learning across trials but remain fixed on the fast time scales of information processing within a trial. By contrast, signal transduction at chemical synapses in the brain results from complex molecular interactions between multiple biochemical processes whose dynamics result in substantial short-term plasticity of most connections. Here we study the computational capabilities of spiking neurons whose synapses are dynamic and plastic, such that each individual synapse can learn its own dynamics. We derive tempotron learning rules for current-based leaky-integrate-and-fire neurons with different types of dynamic synapses. Introducing ordinal synapses whose efficacies depend only on the order of input spikes, we establish an upper capacity bound for spiking neurons with dynamic synapses. We compare this bound to independent synapses, static synapses and to the well established phenomenological Tsodyks-Markram model. We show that synaptic dynamics in principle allow the storage capacity of spiking neurons to scale with the number of input spikes and that this increase in capacity can be traded for greater robustness to input noise, such as spike time jitter. Our work highlights the feasibility of a novel computational paradigm for spiking neural circuits with plastic synaptic dynamics: Rather than being determined by the fixed number of afferents, the dimensionality of a neuron's decision space can be scaled flexibly through the number of input spikes emitted by its input layer.

The accumulation and storage of information in memory is a fundamental computation underlying animal behavior. In many brain regions and task paradigms, ranging from motor control to navigation to decision-making, such accumulation is accomplished through neural integrator circuits that enable external inputs to move a system’s population-wide patterns of neural activity along a continuous attractor. In the first portion of the talk, I will discuss our efforts to dissect the circuit mechanisms underlying a neural integrator from a rich array of anatomical, physiological, and perturbation experiments. In the second portion of the talk, I will show how the accumulation and storage of information in long-term memory may also be described by attractor dynamics, but now within the space of synaptic weights rather than neural activity. Altogether, this work suggests a conceptual unification of seemingly distinct short- and long-term memory processes.

Executing memory-guided behavior requires storage of information about experience and later recall of that information to inform choices. Awake hippocampal replay, when hippocampal neural ensembles briefly reactivate a representation related to prior experience, has been proposed to critically contribute to these memory-related processes. However, it remains unclear whether awake replay contributes to memory function by promoting the storage of past experiences, facilitating planning based on evaluation of those experiences, or both. We designed a dynamic spatial task that promotes replay before a memory-based choice and assessed how the content of replay related to past and future behavior. We found that replay content was decoupled from subsequent choice and instead was enriched for representations of previously rewarded locations and places that had not been visited recently, indicating a role in memory storage rather than in directly guiding subsequent behavior.

Neurological disorders share common high-level alterations, such as cognitive deficits, anxiety, and depression. This raises the possibility of fundamental alterations in the way information conveyed by neural firing is maintained and dispatched in the diseased brain. Using experimental epilepsy as a model of neurological disorder we tested the hypothesis of altered information processing, analyzing how neurons in the hippocampus and the entorhinal cortex store and exchange information during slow and theta oscillations. We equate the storage and sharing of information to low level, or primitive, information processing at the algorithmic level, the theoretical intermediate level between structure and function. We find that these low-level processes are organized into substates during brain states marked by theta and slow oscillations. Their internal composition and organization through time are disrupted in epilepsy, losing brain state-specificity, and shifting towards a regime of disorder in a brain region dependent manner. We propose that the alteration of information processing at an algorithmic level may be a mechanism behind the emergent and widespread co-morbidities associated with epilepsy, and perhaps other disorders.

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.

Biologists have recently come to appreciate that eukaryotic cells are home to a multiplicity of non-membrane bound compartments, many of which form and dissolve as needed for the cell to function. These dynamical “condensates” enable many central cellular functions – from ribosome assembly, to RNA regulation and storage, to signaling and metabolism. While it is clear that these compartments represent a type of separated phase, what controls their formation, how specific biological components are included or excluded, and how these structures influence physiological and biochemical processes remain largely mysterious. I will discuss recent experiments on phase separated condensates both in vitro and in vivo, and will present theoretical results that highlight a novel “magic number” effect relevant to the formation and control of two-component phase separated condensates.

Lysosomal storage diseases are a group of over 70 inherited metabolic disorders, many of which have a neurodegenerative clinical course. Treatments have been developed for a subset of these disorders and are now in routine clinical use. We have found that some neurological and neurodegenerative diseases share unanticipated links to lysosomal storage diseases providing insights into disease pathogenesis. These links also suggest treatments developed for lysosomal disorders may have unanticipated utility in other rare and common diseases.

The neuronal gene Arc is essential for long-lasting information storage in the mammalian brain and mediates various forms of synaptic plasticity. We recently discovered that Arc self-assembles into virus-like capsids that encapsulate RNA. Endogenous Arc protein is released from neurons in extracellular vesicles that mediate the transfer of Arc mRNA into new target cells. Evolutionary analysis indicates that Arc is derived from a vertebrate lineage of Ty3/gypsy retrotransposons, which are also ancestral to retroviruses such as HIV. These findings suggest that Gag retroelements have been repurposed during evolution to mediate intercellular communication in the nervous system that may underlie cognition and memory.