Neural population geometry and optimal coding of tasks with shared latent structure

• URL: http://arxiv.org/abs/2402.16770v2
• Date: Thu, 11 Apr 2024 17:40:57 GMT
• Title: Neural population geometry and optimal coding of tasks with shared latent structure
• Authors: Albert J. Wakhloo, Will Slatton, SueYeon Chung,
• Abstract summary: We show that four geometric measures of the activity determine performance across tasks. We find that experimentally observed disentangled representations naturally emerge as an optimal solution to the multi-task learning problem.
• Score: 4.490493754303233
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Humans and animals can recognize latent structures in their environment and apply this information to efficiently navigate the world. However, it remains unclear what aspects of neural activity contribute to these computational capabilities. Here, we develop an analytical theory linking the geometry of a neural population's activity to the generalization performance of a linear readout on a set of tasks that depend on a common latent structure. We show that four geometric measures of the activity determine performance across tasks. Using this theory, we find that experimentally observed disentangled representations naturally emerge as an optimal solution to the multi-task learning problem. When data is scarce, these optimal neural codes compress less informative latent variables, and when data is abundant, they expand these variables in the state space. We validate our theory using macaque ventral stream recordings. Our results therefore tie population geometry to multi-task learning.

Related papers

• Feature Learning beyond the Lazy-Rich Dichotomy: Insights from Representational Geometry [7.517013801971377]
  We introduce an analysis framework based on representational geometry to study feature learning. We find that when a network learns features useful for solving a task, the task-relevant manifold become increasingly untangled. By tracking changes in the underlying manifold geometry, we uncover distinct learning stages throughout training.
  arXiv  Detail & Related papers  (2025-03-23T15:39:56Z)
• Towards Utilising a Range of Neural Activations for Comprehending Representational Associations [0.6554326244334868]
  We show that an approach to label intermediate representations in deep neural networks fails to capture valuable information about their behaviour. We hypothesise that non-extremal level activations contain complex information worth investigating. We use our findings to develop a method to curate data from mid-range logit samples for retraining to mitigate spurious correlations.
  arXiv  Detail & Related papers  (2024-11-15T07:54:14Z)
• Don't Cut Corners: Exact Conditions for Modularity in Biologically Inspired Representations [52.48094670415497]
  We develop a theory of when biologically inspired representations modularise with respect to source variables (sources) We derive necessary and sufficient conditions on a sample of sources that determine whether the neurons in an optimal biologically-inspired linear autoencoder modularise. Our theory applies to any dataset, extending far beyond the case of statistical independence studied in previous work.
  arXiv  Detail & Related papers  (2024-10-08T17:41:37Z)
• Efficient, probabilistic analysis of combinatorial neural codes [0.0]
  neural networks encode inputs in the form of combinations of individual neurons' activities. These neural codes present a computational challenge due to their high dimensionality and often large volumes of data. We apply methods previously applied to small examples and apply them to large neural codes generated by experiments.
  arXiv  Detail & Related papers  (2022-10-19T11:58:26Z)
• Measures of Information Reflect Memorization Patterns [53.71420125627608]
  We show that the diversity in the activation patterns of different neurons is reflective of model generalization and memorization. Importantly, we discover that information organization points to the two forms of memorization, even for neural activations computed on unlabelled in-distribution examples.
  arXiv  Detail & Related papers  (2022-10-17T20:15:24Z)
• Learnable latent embeddings for joint behavioral and neural analysis [3.6062449190184136]
  We show that CEBRA can be used for the mapping of space, uncovering complex kinematic features, and rapid, high-accuracy decoding of natural movies from visual cortex. We validate its accuracy and demonstrate its utility for both calcium and electrophysiology datasets, across sensory and motor tasks, and in simple or complex behaviors across species.
  arXiv  Detail & Related papers  (2022-04-01T19:19:33Z)
• Overcoming the Domain Gap in Neural Action Representations [60.47807856873544]
  3D pose data can now be reliably extracted from multi-view video sequences without manual intervention. We propose to use it to guide the encoding of neural action representations together with a set of neural and behavioral augmentations. To reduce the domain gap, during training, we swap neural and behavioral data across animals that seem to be performing similar actions.
  arXiv  Detail & Related papers  (2021-12-02T12:45:46Z)
• Overcoming the Domain Gap in Contrastive Learning of Neural Action Representations [60.47807856873544]
  A fundamental goal in neuroscience is to understand the relationship between neural activity and behavior. We generated a new multimodal dataset consisting of the spontaneous behaviors generated by fruit flies. This dataset and our new set of augmentations promise to accelerate the application of self-supervised learning methods in neuroscience.
  arXiv  Detail & Related papers  (2021-11-29T15:27:51Z)
• Neuronal Learning Analysis using Cycle-Consistent Adversarial Networks [4.874780144224057]
  We use a variant of deep generative models called - CycleGAN, to learn the unknown mapping between pre- and post-learning neural activities. We develop an end-to-end pipeline to preprocess, train and evaluate calcium fluorescence signals, and a procedure to interpret the resulting deep learning models.
  arXiv  Detail & Related papers  (2021-11-25T13:24:19Z)
• What can linearized neural networks actually say about generalization? [67.83999394554621]
  In certain infinitely-wide neural networks, the neural tangent kernel (NTK) theory fully characterizes generalization. We show that the linear approximations can indeed rank the learning complexity of certain tasks for neural networks. Our work provides concrete examples of novel deep learning phenomena which can inspire future theoretical research.
  arXiv  Detail & Related papers  (2021-06-12T13:05:11Z)
• A neural anisotropic view of underspecification in deep learning [60.119023683371736]
  We show that the way neural networks handle the underspecification of problems is highly dependent on the data representation. Our results highlight that understanding the architectural inductive bias in deep learning is fundamental to address the fairness, robustness, and generalization of these systems.
  arXiv  Detail & Related papers  (2021-04-29T14:31:09Z)
• Neural population geometry: An approach for understanding biological and artificial neural networks [3.4809730725241605]
  We review examples of geometrical approaches providing insight into the function of biological and artificial neural networks. Neural population geometry has the potential to unify our understanding of structure and function in biological and artificial neural networks.
  arXiv  Detail & Related papers  (2021-04-14T18:10:34Z)
• A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention [96.77554122595578]
  We introduce a parametrized representation of fixed size, which embeds and then aggregates elements from a given input set according to the optimal transport plan between the set and a trainable reference. Our approach scales to large datasets and allows end-to-end training of the reference, while also providing a simple unsupervised learning mechanism with small computational cost.
  arXiv  Detail & Related papers  (2020-06-22T08:35:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.