Biology subjects

de Cothi, W.

Publications and source records attributed to de Cothi, W..

9 recordsLinked to original sources

bioRxiv2024

Unifying Subicular Function: A Predictive Map Approach

The successor representation has emerged as a powerful model for understanding mammalian navigation and memory; explaining the spatial coding properties of hippocampal place cells and entorhinal grid cells. However, the diverse spatial responses of subicular neurons, the primary output of the hippocampus, have eluded a unified account. Here, we demonstrate that incorporating rodent behavioural biases into the successor representation successfully reproduces the heterogeneous activity patterns of subicular neurons. This framework accounts for the emergence of boundary and corner cells - neuronal types absent in upstream hippocampal regions. We provide evidence that subicular firing patterns are more accurately described by the successor representation than a purely spatial or boundary vector cell model of subiculum. Our work reveals a temporal hierarchy in hippocampal-subicular processing, with subiculum encoding predictive representations over longer time horizons than CA1, capturing extended behavioural patterns and environmental affordances.

neuroscience↗

bioRxiv2023

Theta phase precession at encoding predicts subsequent memory of sensory-driven vector fields, & occurs in memory-dependent fields at retrieval

Theta phase precession is thought to confer key computational advantages (e.g. temporal compression suiting spike-timing related plasticity, cognitive relations as phase distances, and population-level coding for directions and sequences). However, direct evidence speaking to: 1) its widely-theorised role in enhancing memorability; 2) its dependence upon sensory input, is lacking. We leveraged the Vector trace cell (VTC) phenomenon to examine these issues. VTCs in subiculum show a simple, unambiguous memory correlate: VTCs remember the distance and direction to a cue after the cue is removed, with a new trace field which was not present before the cue was inserted. Regarding memorability, here we show that subsequently-remembered cue fields (those which become trace fields) exhibit higher levels of phase precession than subsequently-forgotten cue fields (those which produce no trace). Thus, phase precession does appear to enhance memorability, consistent with long-established theory. The second issue concerns the extent of phase precession in sensory-elicited vs memory-dependent firing. Phase precession in CA1 is strongly disrupted following deprivation of its Entorhinal, but not CA3, inputs; this could indicate that theta phase precession is largely sensory-driven and absent in memory-dependent fields. Here, however, we show that phase precession is robust in subicular VTC trace fields, i.e. when the cue that originally elicited the new vector field is no longer present. Thus, the much-theorised benefits of phase precession likely apply to memory-dependent fields. These findings have wide implications for oscillatory-based models of memory.

neuroscience↗

bioRxiv2022

RatInABox: A toolkit for modelling locomotion and neuronal activity in continuous environments

Generating synthetic locomotory and neural data is a useful yet cumbersome step commonly required to study theoretical models of the brains role in spatial navigation. This process can be time consuming and, without a common framework, makes it difficult to reproduce or compare studies which each generate test data in different ways. In response we present RatInABox, an open-source Python toolkit designed to model realistic rodent locomotion and generate synthetic neural data from spatially modulated cell types. This software provides users with (i) the ability to construct one-or two-dimensional environments with configurable barriers and visual cues, (ii) a physically realistic random motion model fitted to experimental data, (iii) rapid online calculation of neural data for many of the known self-location or velocity selective cell types in the hippocampal formation (including place cells, grid cells, boundary vector cells, head direction cells) and (iv) a framework for constructing custom cell types, multi-layer network models and data-or policy-controlled motion trajectories. The motion and neural models spatially and temporally continuous as well as topographically sensitive to boundary conditions and walls. We demonstrate that out-of-the-box parameter settings replicate many aspects of rodent foraging behaviour such as velocity statistics and the tendency of rodents to over-explore walls. Numerous tutorial scripts are provided, including examples where RatInABox is used for decoding position from neural data or to solve a navigational reinforcement learning task. We hope this tool significantly streamline computational research into the brains role in navigation.

neuroscience↗

bioRxiv2022

Rapid learning of predictive maps with STDP and theta phase precession

The predictive map hypothesis is a promising candidate principle for hippocampal function. A favoured formalisation of this hypothesis, called the successor representation, proposes that each place cell encodes the expected state occupancy of its target location in the near future. This predictive framework is supported by behavioural as well as electrophysiological evidence and has desirable consequences for both the generalisability and efficiency of reinforcement learning algorithms. However, it is unclear how the successor representation might be learnt in the brain. Error-driven temporal difference learning, commonly used to learn successor representations in artificial agents, is not known to be implemented in hippocampal networks. Instead, we demonstrate that spike-timing dependent plasticity (STDP), a form of Hebbian learning, acting on temporally compressed trajectories known as "theta sweeps", is sufficient to rapidly learn a close approximation to the successor representation. The model is biologically plausible - it uses spiking neurons modulated by theta-band oscillations, diffuse and overlapping place cell-like state representations, and experimentally matched parameters. We show how this model maps onto known aspects of hippocampal circuitry and explains substantial variance in the temporal difference successor matrix, consequently giving rise to place cells that demonstrate experimentally observed successor representation-related phenomena including backwards expansion on a 1D track and elongation near walls in 2D. Finally, our model provides insight into the observed topographical ordering of place field sizes along the dorsal-ventral axis by showing this is necessary to prevent the detrimental mixing of larger place fields, which encode longer timescale successor representations, with more fine-grained predictions of spatial location.

neuroscience↗

bioRxiv2022

Comparable human spatial memory distortions in physical, desktop virtual and immersive virtual environments

Boundaries define regions of space and are integral to episodic memories. The impact of boundaries on spatial memory and neural representations of space has been extensively studied in freely-moving rodents. But less is known in humans and many prior studies have employed desktop virtual reality (VR) which lacks the body-based self-motion cues of the physical world, diminishing the potentially strong input from path integration to spatial memory. We replicated a desktop-VR study testing the impact of boundaries on spatial memory (Hartley et al., 2004) in a physical room (2.4m x 2.4m, 2m tall) by having participants (N = 27) learn the location of a circular stool and then after a short delay replace it where they thought they had found it. During the delay, the wall boundaries were either expanded or contracted. We compared performance to groups of participants undergoing the same procedure in a laser-scanned replica in both desktop VR (N = 44) and freely-walking head mounted display (HMD) VR (N = 39) environments. Performance was measured as goodness of fit between the spatial distributions of group responses and seven modelled distributions that prioritised different metrics based on boundary geometry or walking paths to estimate the stool location. The best fitting model was a weighted linear combination of all the geometric spatial models, but an individual model derived from place cell firing in Hartley et al. 2004 also fit well. High levels of disorientation in all three environments prevented detailed analysis on the contribution of path integration. We found identical model fits across the three environments, though desktop VR and HMD-VR appeared more consistent in spatial distributions of group responses than the physical environment and displayed known variations in virtual depth perception. Thus, while human spatial representation appears differentially influenced by environmental boundaries, the influence is similar across virtual and physical environments. Despite differences in body-based cue availability, desktop and HMD-VR allow a good and interchangeable approximation for examining human spatial memory in small-scale physical environments.

neuroscience↗

bioRxiv2021

State transitions in the statistically stable place cell population are determined by visual change

The hippocampus plays a central role in mammalian navigation and memory, yet an implementational understanding of the rules that govern the granularity of location encoding and the spatial-statistics of the population as a whole are lacking. We analysed large numbers of CA1 place fields recorded while rats foraged in environments up to 8.75 m2. We found that place cell propensities to form fields were proportional to open-field area, gamma-distributed, and conserved across environments. The properties of place fields varied positionally with a denser distribution of smaller fields near boundaries. Remarkably, field size and density were in a dynamic equilibrium, such that population-level activity statistics remained constant. We showed that the rate of transition through the statistically stable place cell population matched the rate of change in the animals visual scenes - demonstrating that the resolution of the spatial-memory system is bounded by perceptual information afforded by the environment.

neuroscience↗

bioRxiv2020

Predictive Maps in Rats and Humans for Spatial Navigation

Much of our understanding of navigation comes from the study of individual species, often with specific tasks tailored to those species. Here, we provide a novel experimental and analytic framework, integrating across humans, rats and simulated reinforcement learning (RL) agents to interrogate the dynamics of behaviour during spatial navigation. We developed a novel open-field navigation task (Tartarus Maze) requiring dynamic adaptation (shortcuts and detours) to frequently changing obstructions in the path to a hidden goal. Humans and rats were remarkably similar in their trajectories. Both species showed the greatest similarity to RL agents utilising a successor representation, which creates a predictive map. Humans also displayed trajectory features similar to model-based RL agents, which implemented an optimal tree-search planning procedure. Our results help refine models seeking to explain mammalian navigation in dynamic environments, and highlight the utility of modelling the behaviour of different species to uncover the shared mechanisms that support behaviour.

animal behavior and cognition↗

bioRxiv2019

Neurobiological successor features for spatial navigation

The hippocampus has long been observed to encode a representation of an animals position in space. Recent evidence suggests that the nature of this representation is somewhat predictive and can be modelled by learning a successor representation (SR) between distinct positions in an environment. However, this discretisation of space is subjective making it difficult to formulate predictions about how some environmental manipulations should impact the hippocampal representation. Here we present a model of place and grid cell firing as a consequence of learning a SR from a basis set of known neurobiological features - boundary vector cells (BVCs). The model describes place cell firing as the successor features of the SR, with grid cells forming a low-dimensional representation of these successor features. We show that the place and grid cells generated using the BVC-SR model provide a good account of biological data for a variety of environmental manipulations, including dimensional stretches, barrier insertions, and the influence of environmental geometry on the hippocampal representation of space.

neuroscience↗

bioRxiv2017

Global determinants of navigation ability

Countries vary in their geographical and cultural properties. Only a few studies have explored how such variations influence how humans navigate or reason about space [1-7]. We predicted that these variations impact human cognition, resulting in an organized spatial distribution of cognition at a planetary-wide scale. To test this hypothesis we developed a mobile-app-based cognitive task, measuring non-verbal spatial navigation ability in more than 2.5 million people, sampling populations in every nation state. We focused on spatial navigation due to its universal requirement across cultures. Using a clustering approach, we find that navigation ability is clustered into five distinct, yet geographically related, groups of countries. Specifically, the economic wealth of a nation was predictive of the average navigation ability of its inhabitants, and gender inequality was predictive of the size of performance difference between males and females. Thus, cognitive abilities, at least for spatial navigation, are clustered according to economic wealth and gender inequalities globally, which has significant implications for cross-cultural studies and multi-centre clinical trials using cognitive testing.

neuroscience↗