Biology subjects

Barry, C.

Publications and source records attributed to Barry, C..

36 records · Page 2Linked to original sources

bioRxiv2021

Theta coordinates hippocampal-entorhinal ensembles during wake and rest

Precisely timed interactions between hippocampal and cortical neurons during replay epochs are thought to support memory consolidation. Indeed, research has shown replay is associated with heightened hippocampal-cortical synchrony. Yet, many caveats remain in our understanding. Namely, it remains unclear how this offline synchrony comes about, whether it is specific to particular behavioural states and how - if at all - it relates to learning. In this study, we sought to address these questions by analysing coordination between CA1 cells and neurons of the deep layers of the medial entorhinal cortex (dMEC) while rats learned a novel spatial task. During movement, we found a subset of dMEC cell which were particularly locked to hippocampal LFP theta-band oscillations and which were preferentially coordinated with hippocampal replay during offline periods. Further, dMEC synchrony with CA1 replay peaked [~]10ms after replay initiation in CA1, suggesting the distributed replay reflects extra-hippocampal information propagation, and was specific to offline periods. Finally, theta-modulated dMEC cells only became coordinated with replay after an animals first encounter with a novel spatial environment and then showed a striking experience-dependent increase in synchronisation with hippocampal replay trajectories, mirroring the animals acquisition of the novel task and coupling to the hippocampal local field. Together, these findings provide strong support for the hypothesis that synergistic hippocampal-cortical replay supports the consolidation of new memories and highlights phase locking to hippocampal theta oscillations as a potential mechanism by which such cross-structural synchrony comes about. Importantly, as CA1 phase-locking to theta is implicated in the generation of theta sequences, thought to be required for replay expression, we speculate the dMEC theta phase-locking reflects the emergence of distributed hippocampal-dMEC theta sequences and that these may support the commission of memories to long-term cortical storage.

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↗

bioRxiv2021

Ripple Band Phase Precession of Place Cell Firing during Replay

Phase coding offers several theoretical advantages for information transmission compared to an equivalent rate code. Phase coding is shown by place cells in the rodent hippocampal formation, which fire at progressively earlier phases of the movement related 6-12Hz theta rhythm as their spatial receptive fields are traversed. Importantly, however, phase coding is independent of carrier frequency, and so we asked whether it might also be exhibited by place cells during 150-250Hz ripple band activity, when they are thought to replay information to neocortex. We demonstrate that place cells which fire multiple spikes during candidate replay events do so at progressively earlier ripple phases, and that spikes fired across all replay events exhibit a negative relationship between decoded location within the firing field and ripple phase. These results provide insights into the mechanisms underlying phase coding and place cell replay, as well as the neural code propagated to downstream neurons.

neuroscience↗

bioRxiv2021

Choice of method of place cell classification determines the population of cells identified.

Place cells, spatially responsive hippocampal cells, provide the neural substrate supporting navigation and spatial memory. Historically most studies of these neurons have used electrophysiological recordings from implanted electrodes but optical methods, measuring intracellular calcium, are becoming increasingly common. Several methods have been proposed as a means to identify place cells based on their calcium activity but there is no common standard and it is unclear how reliable different approaches are. Here we tested three methods that have previously been applied to two-photon hippocampal imaging or electrophysiological data, using both model datasets and real imaging data. These methods use different parameters to identify place cells, including the peak activity in the place field, compared to other locations (the Peak method); the stability of cells activity over repeated traversals of an environment (Stability method); and a combination of these parameters with the size of the place field (Combination method). The three methods performed differently from each other on both model and real data. The Peak method showed high sensitivity and specificity for detecting model place cells and was the most robust to variations in place field width, reliability and field location. In real datasets, vastly different numbers of place cells were identified using the three methods, with little overlap between the populations identified as place cells. Therefore, choice of place cell detection method dramatically affects the number and properties of identified cells. We recommend the Peak method be used in future studies to identify place cell populations, unless there is an explicit theoretical reason for detecting cells with more narrowly defined properties. Author SummaryPlace cells are hippocampal cells that have spatially constrained receptive fields, the place field. These cells have been widely studied in the context of navigation, more recently using virtual reality environments in combination with optical methods of recording neuronal activity. However, there is a lack of consensus regarding how to identify place cells in these data. In this study we tested the sensitivity and specificity of three methods of identifying place cells. By comparing these methods and quantifying the populations of place cells they identify, we aimed to increase our understanding of exactly the populations that are currently being studied under the name "place cells". Although the appropriate method may depend on the experimental design, we generally recommend a single method going forward, which will increase consensus within the field about what should be included in a place cell population, and allow us to better compare results between studies.

neuroscience↗

bioRxiv2020

The Spatial Memory Pipeline: a model of egocentric to allocentric understanding in mammalian brains

In the mammalian brain, allocentric representations support efficient self-location and flexible navigation. A number of distinct populations of these spatial responses have been identified but no unified function has been shown to account for their emergence. Here we developed a network, trained with a simple predictive objective, that was capable of mapping egocentric information into an allocentric spatial reference frame. The prediction of visual inputs was sufficient to drive the appearance of spatial representations resembling those observed in rodents: head direction, boundary vector, and place cells, along with the recently discovered egocentric boundary cells, suggesting predictive coding as a principle for their emergence in animals. Strikingly, the network learned a solution for head direction tracking and stabilisation convergent with known biological connectivity. Moreover, like mammalian representations, responses were robust to environmental manipulations, including exposure to novel settings. In contrast to existing reinforcement learning approaches, agents equipped with this network were able to flexibly reuse learnt behaviours --adapting rapidly to unfamiliar environments. Thus, our results indicate that these representations, derived from a simple egocentric predictive framework, form an efficient basis-set for cognitive mapping.

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↗

bioRxiv2020

Interspecies Chimeric Conditions Affect the Developmental Rate of Human Pluripotent Stem Cells

Human pluripotent stem cells hold significant promise for regenerative medicine. However, long differentiation protocols and immature characteristics of stem cell-derived cell types remain challenges to the development of many therapeutic applications. In contrast to the slow differentiation of human stem cells in vitro that mirrors a nine-month gestation period, mouse stem cells develop according to a much faster three-week gestation timeline. Here, we tested if co-differentiation with mouse pluripotent stem cells could accelerate the differentiation speed of human embryonic stem cells. Following a six-week RNA-sequencing time course of neural differentiation, we identified 929 human genes that were upregulated earlier and 535 genes that exhibited earlier peaked expression profiles in chimeric cell cultures than in human cell cultures alone. Genes with accelerated upregulation were significantly enriched in Gene Ontology terms associated with neurogenesis, neuron differentiation and maturation, and synapse signaling. Moreover, chimeric mixed samples correlated with in utero human embryonic samples earlier than human cells alone, and acceleration was dose-dependent on human-mouse co-culture ratios. Differences in the timing and expression levels of genes corresponding to neuron cell types and brain region identity under chimeric conditions were also observed. The altered developmental rates and lineage outcomes described in this report have implications for accelerating human stem cell differentiation and the use of interspecies chimeric embryos in developing human organs for transplantation. Author SummaryHuman pluripotent stem cells often require long in vitro protocols to form mature cell types of clinical relevance for potential regenerative therapies, a ramification of a nine-month developmental clock in utero that also runs ex utero. What controls species-specific developmental time and whether the timer is amenable to acceleration is unknown. Further, interspecies chimeric embryos are increasingly being created to study early human development or explore the potential growth of human organs for transplantation. How the conflicting developmental speeds of cells from different species co-differentiating together affect each other is not understood. Here, using genome-wide transcriptional analysis of RNA-sequencing time courses, we show that 1) co-differentiating human embryonic stem cells intermixed with mouse stem cells accelerated elements of human developmental programs, 2) the acceleration was dose-dependent on the proportion of mouse cells, and 3) human cells in chimeric samples correlated to in utero samples earlier than human only samples. Our results provide evidence that some components of species-specific developmental clocks may be susceptible to acceleration.

developmental biology↗

bioRxiv2020

Optimizing the use of gene expression data to predict metabolic pathway memberships with unsupervised and supervised machine learning

Plant metabolites produced via diverse pathways are important for plant survival, human nutrition and medicine. However, the pathway memberships of most plant enzyme genes are unknown. While co-expression is useful for assigning genes to pathways, expression correlation may exist only under specific spatiotemporal and conditional contexts. Utilizing >600 expression values and similarity data combinations from tomato, three strategies for predicting membership in 85 pathways were explored: naive prediction (identifying pathways with the most similarly expressed genes), unsupervised and supervised learning. Optimal predictions for different pathways require distinct data combinations that, in some cases, are indicative of biological processes relevant to pathway functions. Naive prediction produced higher error rates compared with machine learning methods. In 52 pathways, unsupervised learning performed better than a supervised approach, which may be due to the limited availability of training data. Furthermore, using gene-to-pathway expression similarities led to prediction models that outperformed those based simply on gene expression levels. Our study highlights the need to extensively explore expression-based features and prediction strategies to maximize the accuracy of metabolic pathway membership assignment. We anticipate that the prediction framework outlined here can be applied to other species and also be used to improve plant pathway annotation.

plant biology↗

bioRxiv2020

Within and cross species predictions of plant specialized metabolism genes using transfer learning

Plant specialized metabolites mediate interactions between plants and the environment and have significant agronomical/pharmaceutical value. Most genes involved in specialized metabolism (SM) are unknown because of the large number of metabolites and the challenge in differentiating SM genes from general metabolism (GM) genes. Plant models like Arabidopsis thaliana have extensive, experimentally derived annotations, whereas many non-model species do not. Here we employed a machine learning strategy, transfer learning, where knowledge from A. thaliana is transferred to predict gene functions in cultivated tomato with fewer experimentally annotated genes. The first tomato SM/GM prediction model using only tomato data performs well (F-measure=0.74, compared with 0.5 for random and 1.0 for perfect predictions), but from manually curating 88 SM/GM genes, we found many mis-predicted entries were likely mis-annotated. When the SM/GM prediction models built with A. thaliana data were used to filter out genes where the A. thaliana-based model predictions disagreed with tomato annotations, the new tomato model trained with filtered data improved significantly (F-measure=0.92). Our study demonstrates that SM/GM genes can be better predicted by leveraging cross-species information. Additionally, our findings provide an example for transfer learning in genomics where knowledge can be transferred from an information-rich species to an information-poor one.

plant biology↗

bioRxiv2019

DeepInsight: A general framework for interpreting wide-band neural activity

AO_SCPLOWBSTRACTC_SCPLOWRapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data often depends on manual operations and requires considerable knowledge about the nature of the representation. Decoding provides a means to infer the information content of such recordings but typically requires highly processed data and prior knowledge of the encoding scheme. Here, we developed a deep-learning-framework able to decode sensory and behavioural variables directly from wide-band neural data. The network requires little user input and generalizes across stimuli, behaviours, brain regions, and recording techniques. Once trained, it can be analysed to determine elements of the neural code that are informative about a given variable. We validated this approach using data from rodent auditory cortex and hippocampus, identifying a novel representation of head direction encoded by putative CA1 interneurons.

neuroscience↗

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↗

bioRxiv2019

The Tolman-Eichenbaum Machine: Unifying space and relational memory through generalisation in the hippocampal formation

The hippocampal-entorhinal system is important for spatial and relational memory tasks. We formally link these domains; provide a mechanistic understanding of the hippocampal role in generalisation; and offer unifying principles underlying many entorhinal and hippocampal cell-types. We propose medial entorhinal cells form a basis describing structural knowledge, and hippocampal cells link this basis with sensory representations. Adopting these principles, we introduce the Tolman-Eichenbaum machine (TEM). After learning, TEM entorhinal cells include grid, band, border and object-vector cells. Hippocampal cells include place and landmark cells, remapping between environments. Crucially, TEM also predicts empirically recorded representations in complex non-spatial tasks. TEM predicts hippocampal remapping is not random as previously believed. Rather structural knowledge is preserved across environments. We confirm this in simultaneously recorded place and grid cells.\n\nOne Sentence SummarySimple principles of representation and generalisation unify spatial and non-spatial accounts of hippocampus and explain many cell representations.

neuroscience↗

bioRxiv2018

Entorhinal neurons exhibit cue locking in rodent VR

The regular firing pattern exhibited by medial entorhinal (mEC) grid cells of locomoting rodents is hypothesized to provide spatial metric information relevant for navigation. The development of virtual reality (VR) for head-fixed mice confers a number of experimental advantages and has become increasingly popular as a method for investigating spatially-selective cells. Recent experiments using 1D VR linear tracks have shown that some mEC cells have multiple fields in virtual space, analogous to grid cells on real linear tracks. We recorded from the mEC as mice traversed virtual tracks featuring regularly spaced repetitive cues and identified a population of cells with multiple firing fields, resembling the regular firing of grid cells. However, further analyses indicated that many of these were not, in fact, grid cells because: 1) When recorded in the open field they did not display discrete firing fields with six-fold symmetry; 2) In different VR environments their firing fields were found to match the spatial frequency of repetitive environmental cues. In contrast, cells identified as grid cells based on their open field firing patterns did not exhibit cue locking. In light of these results we highlight the importance of controlling the periodicity of the visual cues in VR and the necessity of identifying grid cells from real open field environments in order to correctly characterise spatially modulated neurons in VR experiments.

neuroscience↗

bioRxiv2018

Deforming the metric of cognitive maps distorts memory

Environmental boundaries anchor cognitive maps that support memory. However, trapezoidal boundary geometry distorts the regular firing patterns of entorhinal grid cells proposedly providing a metric for cognitive maps. Here, we test the impact of trapezoidal boundary geometry on human spatial memory using immersive virtual reality. Consistent with reduced regularity of grid patterns in rodents and a grid-cell model based on the eigenvectors of the successor representation, human positional memory was degraded in a trapezoid compared to a square environment; an effect particularly pronounced in the trapezoids narrow part. Congruent with spatial frequency changes of eigenvector grid patterns, distance estimates between remembered positions were persistently biased; revealing distorted memory maps that explained behavior better than the objective maps. Our findings demonstrate that environmental geometry affects human spatial memory similarly to rodent grid cell activity -- thus strengthening the putative link between grid cells and behavior along with their cognitive functions beyond navigation.

neuroscience↗

bioRxiv2018

Efficient neural decoding of spatial location with a deep recurrent network

Place cells in the mammalian hippocampus signal self-location with sparse spatially stable firing fields. Based on observation of place cell activity it is possible to accurately decode an animals location. The precision of this decoding sets a lower bound for the amount of information that the hippocampal population conveys about the location of the animal. In this work we use a novel recurrent neural network (RNN) decoder to infer the location of freely moving rats from single unit hippocampal recordings. RNNs are biologically plausible models of neural circuits that learn to incorporate relevant temporal context without the need to make complicated assumptions about the use of prior information to predict the current state. When decoding animal position from spike counts in 1D and 2D-environments, we show that the RNN consistently outperforms a standard Bayesian model with flat priors. In addition, we also conducted a set of sensitivity analysis on the RNN decoder to determine which neurons and sections of firing fields were the most influential. We found that the application of RNNs to neural data allowed flexible integration of temporal context, yielding improved accuracy relative to a commonly used Bayesian approach and opens new avenues for exploration of the neural code.\n\nAuthor summaryBeing able to accurately self-localize is critical for most motile organisms. In mammals, place cells in the hippocampus appear to be a central component of the brain network responsible for this ability. In this work we recorded the activity of a population of hippocampal neurons from freely moving rodents and carried out neural decoding to determine the animals locations. We found that a machine learning approach using recurrent neural networks (RNNs) allowed us to predict the rodents true positions more accurately than a standard Bayesian method with flat priors. The RNNs are able to take into account past neural activity without making assumptions about the statistics of neuronal firing. Further, by analyzing the representations learned by the network we were able to determine which neurons, and which aspects of their activity, contributed most strongly to the accurate decoding.

neuroscience↗

bioRxiv2017

To be a Grid Cell: Shuffling procedures for determining “Gridness”

Grid cells in freely behaving mammals are defined by the strikingly regular periodic spatial distribution of their firing. The standard method of identification calculates the \"Gridness\" of the spatial firing pattern, with significance being defined relative to the 95th percentile of a null distribution of the Gridness values found after randomly permuting spike times relative to behaviour. We determined the false-positive rate by applying the method to simulated firing with irregular spatially inhomogeneity (i.e. randomly distributed Gaussian patches). We found surprisingly high false positive rates (potentially approaching 20%), which were strongly dependent on the type of Gridness measure used and the number of spatial fields in the synthetic data. This likely reflects the spatial homogeneity of the distributions of spikes after shuffling compared to the inhomogeneous synthetic data. However, false positive rates were reduced (generally below 8%), and less dependent on other factors, when an alternative spatial field shuffling method was used to generate the null Gridness distribution. For comparison, we analysed single unit recordings made using tetrodes implanted into rat medial entorhinal cortex for the purpose of finding grid cells. We found 24% of active neurons were classified as grid cells via spike shuffling and 22% via field shuffling. These results, and the potentially high false-positive rate when classifying cells with patchy but irregular firing as grid cells, indicate that the proportion of cells with regular grid-like firing patterns can be over-estimated by standard methods.

neuroscience↗

bioRxiv2017

Task Demands Predict a Dynamic Switch in the Content of Awake Hippocampal Replay

Reactivation of hippocampal place cell sequences during behavioural immobility and rest has been linked with both memory consolidation and navigational planning. Yet it remains to be investigated whether these functions are temporally segregated; occurring during different behavioural states. During a self-paced spatial task, awake hippocampal replay occurring immediately before movement towards a reward location, or just after arrival at a reward location, preferentially involved cells consistent with the current trajectory. In contrast, during periods of extended immobility, no such biases were evident. Notably, the occurrence of task-focused reactivations predicted the accuracy of subsequent spatial decisions. Additionally, during immobility but not periods preceding or succeeding movement, grid cells in deep layers of entorhinal cortex replayed coherently with the hippocampus. Thus, hippocampal reactivations dynamically and abruptly switch operational mode in response to task demands; plausibly moving from a state favouring navigational planning to one geared towards memory consolidation.

neuroscience↗

bioRxiv2017

Optimal decision making using grid cells under spatial uncertainty

Minimizing spatial uncertainty is essential for navigation, but the neural mechanisms remain elusive. Here we combine predictions of a simulated grid cell system with behavioural and fMRI measures in humans during virtual navigation. First, we showed that polarising cues produce anisotropy in motion parallax. Secondly, we simulated entorhinal grid cells in an environment with anisotropic information and found that self-location is decoded best when grid-patterns are aligned with the axis of greatest information. Thirdly, when exposing human participants to polarised virtual reality environments, we found that navigation performance is anisotropic, in line with the use of parallax. Eye movements showed that participants preferentially viewed polarising cues, which correlated with navigation performance. Finally, using fMRI we found that the orientation of grid-cell-like representations in entorhinal cortex anchored to the environmental axis of greatest parallax information, orthogonal to the polarisation axis. In sum, we demonstrate a crucial role of the entorhinal grid system in reducing uncertainty in representations of self-location and find evidence for adaptive spatial computations underlying entorhinal representations in service of optimal navigation.

neuroscience↗