1,404 research outputs found
Prediction and Generalisation over Directed Actions by Grid Cells
Knowing how the effects of directed actions generalise to new situations
(e.g. moving North, South, East and West, or turning left, right, etc.) is key
to rapid generalisation across new situations. Markovian tasks can be
characterised by a state space and a transition matrix and recent work has
proposed that neural grid codes provide an efficient representation of the
state space, as eigenvectors of a transition matrix reflecting diffusion across
states, that allows efficient prediction of future state distributions. Here we
extend the eigenbasis prediction model, utilising tools from Fourier analysis,
to prediction over arbitrary translation-invariant directed transition
structures (i.e. displacement and diffusion), showing that a single set of
eigenvectors can support predictions over arbitrary directed actions via
action-specific eigenvalues. We show how to define a "sense of direction" to
combine actions to reach a target state (ignoring task-specific deviations from
translation-invariance), and demonstrate that adding the Fourier
representations to a deep Q network aids policy learning in continuous control
tasks. We show the equivalence between the generalised prediction framework and
traditional models of grid cell firing driven by self-motion to perform path
integration, either using oscillatory interference (via Fourier components as
velocity-controlled oscillators) or continuous attractor networks (via analysis
of the update dynamics). We thus provide a unifying framework for the role of
the grid system in predictive planning, sense of direction and path
integration: supporting generalisable inference over directed actions across
different tasks.Comment: In Proceedings of ICLR 202
Counterfactual Choice and Learning in a Neural Network Centered on Human Lateral Frontopolar Cortex
Decision making and learning in a real-world context require organisms to track not only the choices they make and the outcomes that follow but also other untaken, or counterfactual, choices and their outcomes. Although the neural system responsible for tracking the value of choices actually taken is increasingly well understood, whether a neural system tracks counterfactual information is currently unclear. Using a three-alternative decision-making task, a Bayesian reinforcement-learning algorithm, and fMRI, we investigated the coding of counterfactual choices and prediction errors in the human brain. Rather than representing evidence favoring multiple counterfactual choices, lateral frontal polar cortex (lFPC), dorsomedial frontal cortex (DMFC), and posteromedial cortex (PMC) encode the reward-based evidence favoring the best counterfactual option at future decisions. In addition to encoding counterfactual reward expectations, the network carries a signal for learning about counterfactual options when feedback is available—a counterfactual prediction error. Unlike other brain regions that have been associated with the processing of counterfactual outcomes, counterfactual prediction errors within the identified network cannot be related to regret theory. Furthermore, individual variation in counterfactual choice-related activity and prediction error-related activity, respectively, predicts variation in the propensity to switch to profitable choices in the future and the ability to learn from hypothetical feedback. Taken together, these data provide both neural and behavioral evidence to support the existence of a previously unidentified neural system responsible for tracking both counterfactual choice options and their outcomes
Generalisation of structural knowledge in the hippocampal-entorhinal system
A central problem to understanding intelligence is the concept of
generalisation. This allows previously learnt structure to be exploited to
solve tasks in novel situations differing in their particularities. We take
inspiration from neuroscience, specifically the hippocampal-entorhinal system
known to be important for generalisation. We propose that to generalise
structural knowledge, the representations of the structure of the world, i.e.
how entities in the world relate to each other, need to be separated from
representations of the entities themselves. We show, under these principles,
artificial neural networks embedded with hierarchy and fast Hebbian memory, can
learn the statistics of memories and generalise structural knowledge. Spatial
neuronal representations mirroring those found in the brain emerge, suggesting
spatial cognition is an instance of more general organising principles. We
further unify many entorhinal cell types as basis functions for constructing
transition graphs, and show these representations effectively utilise memories.
We experimentally support model assumptions, showing a preserved relationship
between entorhinal grid and hippocampal place cells across environments
Actionable Neural Representations: Grid Cells from Minimal Constraints
To afford flexible behaviour, the brain must build internal representations
that mirror the structure of variables in the external world. For example, 2D
space obeys rules: the same set of actions combine in the same way everywhere
(step north, then south, and you won't have moved, wherever you start). We
suggest the brain must represent this consistent meaning of actions across
space, as it allows you to find new short-cuts and navigate in unfamiliar
settings. We term this representation an `actionable representation'. We
formulate actionable representations using group and representation theory, and
show that, when combined with biological and functional constraints -
non-negative firing, bounded neural activity, and precise coding - multiple
modules of hexagonal grid cells are the optimal representation of 2D space. We
support this claim with intuition, analytic justification, and simulations. Our
analytic results normatively explain a set of surprising grid cell phenomena,
and make testable predictions for future experiments. Lastly, we highlight the
generality of our approach beyond just understanding 2D space. Our work
characterises a new principle for understanding and designing flexible internal
representations: they should be actionable, allowing animals and machines to
predict the consequences of their actions, rather than just encode
Disentangling with Biological Constraints: A Theory of Functional Cell Types
Neurons in the brain are often finely tuned for specific task variables.
Moreover, such disentangled representations are highly sought after in machine
learning. Here we mathematically prove that simple biological constraints on
neurons, namely nonnegativity and energy efficiency in both activity and
weights, promote such sought after disentangled representations by enforcing
neurons to become selective for single factors of task variation. We
demonstrate these constraints lead to disentangling in a variety of tasks and
architectures, including variational autoencoders. We also use this theory to
explain why the brain partitions its cells into distinct cell types such as
grid and object-vector cells, and also explain when the brain instead entangles
representations in response to entangled task factors. Overall, this work
provides a mathematical understanding of why, when, and how neurons represent
factors in both brains and machines, and is a first step towards understanding
of how task demands structure neural representations
Using diffusion tractography to predict cortical connection strength and distance: a quantitative comparison with tracers in the monkey
Tractography based on diffusion MRI offers the promise of characterizing many aspects of long-distance connectivity in the brain, but requires quantitative validation to assess its strengths and limitations. Here, we evaluate tractography's ability to estimate the presence and strength of connections between areas of macaque neocortex by comparing its results with published data from retrograde tracer injections. Probabilistic tractography was performed on high-quality postmortem diffusion imaging scans from two Old World monkey brains. Tractography connection weights were estimated using a fractional scaling method based on normalized streamline density. We found a correlation between log-transformed tractography and tracer connection weights of r = 0.59, twice that reported in a recent study on the macaque. Using a novel method to estimate interareal connection lengths from tractography streamlines, we regressed out the distance dependence of connection strength and found that the correlation between tractography and tracers remains positive, albeit substantially reduced. Altogether, these observations provide a valuable, data-driven perspective on both the strengths and limitations of tractography for analyzing interareal corticocortical connectivity in nonhuman primates and a framework for assessing future tractography methodological refinements objectively
Accelerating fibre orientation estimation from diffusion weighted magnetic resonance imaging using GPUs
With the performance of central processing units (CPUs) having effectively reached a limit, parallel processing offers an alternative for applications with high computational demands. Modern graphics processing units (GPUs) are massively parallel processors that can execute simultaneously thousands of light-weight processes. In this study, we propose and implement a parallel GPU-based design of a popular method that is used for the analysis of brain magnetic resonance imaging (MRI). More specifically, we are concerned with a model-based approach for extracting tissue structural information from diffusion-weighted (DW) MRI data. DW-MRI offers, through tractography approaches, the only way to study brain structural connectivity, non-invasively and in-vivo. We parallelise the Bayesian inference framework for the ball & stick model, as it is implemented in the tractography toolbox of the popular FSL software package (University of Oxford). For our implementation, we utilise the Compute Unified Device Architecture (CUDA) programming model. We show that the parameter estimation, performed through Markov Chain Monte Carlo (MCMC), is accelerated by at least two orders of magnitude, when comparing a single GPU with the respective sequential single-core CPU version. We also illustrate similar speed-up factors (up to 120x) when comparing a multi-GPU with a multi-CPU implementation
Visualization of the Genesis and Fate of Isotype-switched B Cells during a Primary Immune Response
The life history of isotype-switched B cells is unclear, in part, because of an inability to detect rare antigen-specific B cells at early times during the immune response. To address this issue, a small population of B cells carrying targeted antibody transgenes capable of class switching was monitored in immunized mice. After contacting helper T cells, the first switched B cells appeared in follicles rather than in the red pulp, as was expected. Later, some of the switched B cells transiently occupied the red pulp and marginal zone, whereas others persisted in germinal centers (GCs). Antigen-experienced IgM B cells were rarely found in GCs, indicating that these cells switched rapidly after entering GCs or did not persist in this environment
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