Biology subjects

MacAskill, A. F.

Publications and source records attributed to MacAskill, A. F..

13 recordsLinked to original sources

bioRxiv2026

Nucleus reuniens is organised as parallel circuits rather than a prefrontal-hippocampal relay

Interactions between the prefrontal cortex (PFC) and hippocampus (HPC) are crucial for flexible and memory guided behaviour. Because there are no direct projections from PFC to HPC, it is widely presumed that information is shared through a disynaptic projection via the nucleus reuniens (nRE), a ventral midline thalamic nucleus. Yet this model relies on the implicit assumption that has not been directly tested: that prefrontal afferents monosynaptically recruit hippocampal-projecting nRE neurons. Here we combine whole-brain anatomical mapping, projection-specific circuit tracing and electrophysiology to test this model. We find that nRE contains largely distinct populations of prefrontal- and hippocampal-projecting neurons, with minimal overlap between them. Unexpectedly, prefrontal afferent input to nRE provides little anatomical or functional input to hippocampal-projecting neurons, instead preferentially targeting neurons projecting back to PFC. These findings challenge the prevailing view that nRE acts as a simple relay between PFC and HPC and instead reveal parallel projection-defined circuits likely to make distinct contributions to hippocampal-prefrontal communication.

neuroscience↗

bioRxiv2026

Learning temporal structure engages hippocampus and guides value-based behaviour

The ability to learn and exploit structured relationships between events is fundamental to adaptive behaviour and episodic-like memory, yet the neural mechanisms that support such learning remain poorly understood. Progress has been limited by the difficulty of dissociating relational structure from sensory cues, value, and motor output in experimentally tractable tasks. Here we introduce a temporally structured olfactory task for mice that isolates relational structure from cue identity and reward statistics. Mice rapidly learned to use the ordered relationship between two sequentially presented odours to predict outcome. Because individual odours were equally associated with reward across contexts, their predictive value depended entirely on their relationship to preceding cues. Behavioural analyses and reinforcement learning models revealed a gradual shift to the use of structured representations as learning progressed. Dopamine activity in the nucleus accumbens reflected this transition. Over learning, identical sensory inputs became associated with opposite predicted outcomes depending only on their temporal position, consistent with dopamine prediction errors reflecting latent task structure rather than cue identity. Trial-by-trial variability in dopamine dynamics was quantitatively captured by the same models that described behavioural strategy, linking dopamine prediction errors directly to these learned structural representations. Finally, using a matched control task that preserved sensory input and reward statistics while eliminating structure, we found that learning and using temporal structure selectively increased ventral hippocampal activity. Together, these results establish a minimal, non-spatial paradigm for studying structural learning in mice, link behavioural representations to dopamine signalling, and show that isolating relational structure from sensory and reward confounds selectively recruits hippocampal circuitry long theorised to support it.

neuroscience↗

bioRxiv2025

Acetylcholine reflects uncertainty during hidden state inference

To act adaptively, animals must infer features of the environment that cannot be observed directly, such as which option is currently rewarding, or which context they are in. These internal estimates, known as hidden states, guide behaviour but are inherently uncertain. Theoretical models propose that efficient inference requires tracking both the most likely state and the uncertainty of that estimate. While neural representations of state identity have been described in cortical and hippocampal circuits, the origin of uncertainty signals remains unclear. Here we show that acetylcholine (ACh) reflects this uncertainty during hidden state inference. Using fibre photometry in mice performing a probabilistic two-armed bandit task, we found that ACh release in medial prefrontal cortex and ventral hippocampus tracked recent omission history and predicted whether animals would switch choice on the next trial. Blocking muscarinic receptors with scopolamine selectively impaired loss-driven switching without affecting stable performance. A hidden-state inference model in which ACh modulated how uncertainty from past experience shaped future beliefs reproduced both the physiological and behavioural effects, whereas alternative models did not. These results identify ACh as a neuromodulatory signal reflecting uncertainty during hidden-state inference, linking theoretical models of belief updating to their biological implementation in the mammalian brain.

neuroscience↗

bioRxiv2025

Contextual inference through flexible integration of environmental features and behavioural outcomes

The ability to use the context we are in to flexibly adjust our decision-making is vital for navigating a complex world. To do this, the brain must i) use environmental features and behavioural outcomes to distinguish between different, often hidden contexts; and ii) learn how to use these inferred contexts to guide behaviour. However, how these two interacting processes can be performed simultaneously remains unclear. Within the brain it is thought that interaction between the prefrontal cortex (PFC) and hippocampus (HPC) supports contextual inference. We show that models of the HPC using environmental features readily support context-specific behaviour, but struggle to differentiate ambiguous contexts during learning. In contrast, models of the PFC using behavioural outcomes can stably differentiate contexts during periods of learning, but struggle to guide context-specific behaviour. Supporting feature-based with outcome-based strategies during learning overcomes the limitations of both approaches, allowing for the formation of distinct contextual representations that support contextual inference. Moreover, agents using this joint approach reproduce both behavioural- and cellular-level phenomena associated with the interaction between PFC and HPC. Together, these results provide insight into how the brain uses contextual information to guide flexible behaviour.

neuroscience↗

bioRxiv2024

C1q and immunoglobulins mediate activity-dependent synapse loss in the adult brain

C1q, the initiating protein of the classical complement cascade, mediates synapse loss in development and disease. In various mouse models of neurologic diseases, including Alzheimer's disease, C1q, which is secreted by microglia, the brain's resident macrophages, is found deposited on synapses in vulnerable brain regions. However, what underlies C1q deposition on synapses in the adult brain is unclear. Using in vivo chemogenetics, we demonstrate that neuronal hyperactivity acts as a trigger for region-specific deposition of C1q, which is required for activity-dependent synapse loss. Further, using spatial transcriptomics, live cell tracking, super-resolution microscopy and other molecular and cellular tools, we report a role for B lymphocyte lineage cells and immunoglobulins in the activity-dependent C1q deposition and synapse loss. Overall, our work suggests a link between neuronal hyperactivity and C1q-mediated synapse loss in the adult brain and introduces immunoglobulins as players in this process.

neuroscience↗

bioRxiv2024

Hidden state inference requires abstract contextual representations in ventral hippocampus

The ability to form and utilize subjective, latent contextual representations to influence decision making is a crucial determinant of everyday life. The hippocampus is widely hypothesized to bind together otherwise abstract combinations of stimuli to represent such latent contexts, and to allow their use to support the process of hidden state inference. Yet, direct evidence for this remains limited. Here we show that the CA1 area of the ventral hippocampus is necessary for mice to perform hidden state inference during a 2-armed bandit task. vCA1 neurons robustly differentiate between the two abstract contexts required for this strategy in a manner similar to the differentiation of spatial locations, despite the contexts being formed only from past probabilistic outcomes. These findings offer insight into how latent contextual information is used to optimize decision-making processes, and emphasize a key role of the hippocampus in hidden state inference.

neuroscience↗

bioRxiv2023

Nucleus accumbens dopamine release reflects Bayesian inference during instrumental learning

Dopamine release in the nucleus accumbens has been hypothesized to signal reward prediction error, the difference between observed and predicted reward, suggesting a biological implementation for reinforcement learning. Rigorous tests of this hypothesis require assumptions about how the brain maps sensory signals to reward predictions, yet this mapping is still poorly understood. In particular, the mapping is non-trivial when sensory signals provide ambiguous information about the hidden state of the environment. Previous work using classical conditioning tasks has suggested that reward predictions are generated conditional on probabilistic beliefs about the hidden state, such that dopamine implicitly reflects these beliefs. Here we test this hypothesis in the context of an instrumental task (a two-armed bandit), where the hidden state switches repeatedly. We measured choice behavior and recorded dLight signals reflecting dopamine release in the nucleus accumbens core. Model comparison among a wide set of cognitive models based on the behavioral data favored models that used Bayesian updating of probabilistic beliefs. These same models also quantitatively matched the dopamine measurements better than non-Bayesian alternatives. We conclude that probabilistic belief computation contributes to instrumental task performance in mice and is reflected in mesolimbic dopamine signaling.

animal behavior and cognition↗

bioRxiv2023

Heron: A Knowledge Graph editor for intuitive implementation of python based experimental pipelines.

To realise a research project idea, an experimenter faces a series of conflicting design and implementation considerations, regarding both its hardware and software components. For instance, the ease of implementation, in time and expertise, should be balanced against the ease of future reconfigurability and number of black box components. Other, often conflicting, considerations include the level of documentation and ease of reproducibility, resource availability as well as access to online communities. To alleviate this balancing act between opposing requirements we present Heron, a new Python-based platform to construct and run experimental and data analysis pipelines. Herons main principle is to allow researchers to design and implement the experimental flow as close as possible to their mental schemata of the experiment, in the form of a Knowledge Graph. Heron is designed to increase the implementation speed of experiments (and their subsequent updates), while minimising the number of incorporated black box components. It enhances the readability and reproducibility of the final implementation and allows the use of combinations of hardware and software otherwise impossible or too costly to achieve. Given this, Heron offers itself to sciences whose needs involve experiments with a large number of interconnected hardware and software components like robotics, neuroscience, behavioural sciences, physics, chemistry, environmental science, etc.. It is designed with those experimentalists in mind which: i) Demand full control of their setup. ii) Prefer not to have to choose between hardware and software that run only on a specific chip/operating system combination. iii) Appreciate the ease and speed that high-level languages (e.g. Python) and Graphical User Interfaces (GUIs) offer them. It assumes an intermediate knowledge of the Python language and ecosystem, offering a flexible and powerful way to construct experimental setups. It removes any inaccessible corners, yet keeps implementation costs significantly reduced compared to using lower level languages. Finally, its use results in a much cleaner and easier to understand code base, amicable to documentation and reproducibility efforts.

animal behavior and cognition↗

bioRxiv2023

Genetically distinct parallel projection populations from ventral hippocampus to prefrontal cortex

The ventral hippocampus is proposed to perform its multitude of roles via segregated populations of neurons identified by either genetic makeup, downstream projection, or their combination. Recently we described two parallel projections from ventral hippocampus to prefrontal cortex, segregated across the radial axis of the CA1 and subicular areas. These two populations had distinct afferent and efferent connectivity and distinct influence of approach avoidance behaviour. In this study, we extend these data by performing RNA sequencing of each population of neurons. We find that these two populations have multiple genes that are differentially expressed. These genes correspond both to genes classically thought to be distributed across the radial axis such as Calbindin 1 and Pcp4, but also to more unexpected genes including postsynaptic scaffolds and GABA receptor subunits. Notably, a number of genes differentially expressed across the two populations were associated with the development of mental illness, suggesting an imbalance in the function of these two pathways in disease may be an interesting area for future research. Together, these data reinforce the dissociation of function of projections to prefrontal cortex across the radial axis of the ventral hippocampus, and provide multiple targets for both the genetic and functional dissociation of these roles.

neuroscience↗

bioRxiv2021

Hippocampal ghrelin signalling informs the decision to eat

Hunger is an internal state that not only invigorates feeding, but also acts as a contextual cue for the higher-order control of anticipatory feeding-related behaviour. The ventral hippocampus is a brain region crucial for differentiating optimal behaviour across different contexts, but how internal context such as hunger influence hippocampal circuits to define behaviour is not known. Pyramidal neurons in the ventral hippocampus, including the ventral CA1/subiculum border (vS) express the receptor for the peripheral hunger hormone ghrelin, and ghrelin is known to cross the blood brain barrier and directly influence hippocampal circuitry. But how ghrelin influences vS has not been directly investigated. In this study, we used a combination of electrophysiology, optogenetics and in vivo calcium imaging in mice to investigate the role of vS during feeding behaviour across different states of hunger. We found that activity of a unique subpopulation of vS neurons that project to the nucleus accumbens (vS-NAc) increased when animals approached and investigated food, and this activity inhibited the transition to begin eating. Increases in peripheral ghrelin reduced vS-NAc activity during this anticipatory phase of feeding behaviour by increasing the postsynaptic influence of inhibition, and promoted the initiation of eating. Furthermore, this peripheral ghrelin-induced inhibition required postsynaptic expression of the ghrelin receptor GHSR1a in vS-NAc neurons, and removal of GHSR1a from vS-NAc neurons impaired ghrelin-induced changes in feeding-related behaviour. Together, these experiments define a ghrelin-sensitive hippocampal circuit that informs the decision to eat based on internal state.

neuroscience↗

bioRxiv2021

Control of Parallel Hippocampal Output Pathways by Amygdalar Long-Range Inhibition

Projections from the basal amygdala (BA) to the ventral hippocampus (vH) are proposed to provide information about the rewarding or threatening nature of learned associations to support appropriate goal-directed and anxiety-like behaviour. Such behaviour occurs via the differential activity of multiple, parallel populations of pyramidal neurons in vH that project to distinct downstream targets, but the nature of BA input and how it connects with these populations is unclear. Using channelrhodopsin-2-assisted circuit mapping in mice, we show that BA input to vH consists of both excitatory and inhibitory projections. Excitatory input specifically targets BA- and nucleus accumbens-projecting vH neurons, and avoids prefrontal cortex-projecting vH neurons; while inhibitory input preferentially targets BA-projecting neurons. Through this specific connectivity, BA inhibitory projections gate place-value associations by controlling the activity of nucleus accumbens-projecting vH neurons. Our results define a parallel excitatory and inhibitory projection from BA to vH that can support goal-directed behaviour.

neuroscience↗

bioRxiv2019

Push-pull regulation of exploratory behavior by two opposing hippocampal to prefrontal cortex pathways

The decision to either approach or avoid a potentially threatening environment is thought to rely upon complex connectivity between heterogenous neural populations in the ventral hippocampus and prefrontal cortex (PFC). However, how this circuitry can flexibly promote both approach or avoidance at different times has remained elusive. Here, we show that the projection to PFC is composed of two parallel circuits located in the superficial or deep hippocampal pyramidal layers. These circuits have unique upstream and downstream connectivity, and are differentially active during approach and avoidance behavior. The superficial population is preferentially connected to widespread PFC inhibitory interneurons, and its activation promotes exploration; while the deep circuit is connected to PFC pyramidal neurons and fast spiking interneurons, and its activation promotes avoidance. Together this provides a mechanism for regulation of behavior during approach avoidance conflict: through two specialized, parallel circuits that allow bidirectional hippocampal control of PFC.

neuroscience↗

bioRxiv2019

Biased connectivity of brain-wide inputs to ventral subiculum output neurons

The ventral subiculum (vS) of the mouse hippocampus coordinates diverse behaviours through heterogeneous populations of projection neurons. These neurons transmit signals to multiple brain regions by integrating thousands of local and long-range synaptic inputs. However, whether each population is selectively innervated by different afferent input remains unknown. To address this question, we employed projection-specific rabies tracing to study the input-output relationship of vS output neurons. Analysis of brain-wide inputs reveals quantitative input differences that can be explained by the spatial location of postsynaptic neurons along the proximal-distal axis of vS and the identity of the downstream target. Further, the input from nucleus reuniens, an area thought to underlie vS and prefrontal cortex (PFC) reciprocal connectivity, is unexpectedly biased away from PFC-projecting vS neurons. Overall, we reveal prominent heterogeneity in brain-wide inputs to the vS parallel output circuitry, providing a basis for the selective control of individual projections during behaviour.

neuroscience↗