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Found 9 papers, 5 papers with code
Coin-Flipping In The Brain: Statistical Learning with Neuronal Assemblies
How intelligence arises from the brain is a central problem in science.
Computation with Sequences in a Model of the Brain
Here we show that, in the same model, time can be captured naturally as precedence through synaptic weights and plasticity, and, as a result, a range of computations on sequences of assemblies can be carried out.
Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers
By enabling flexible pre-training that can be transferred to neural recordings of different size and order, our work provides a first step towards creating a foundation model for neural decoding.
Comparing high-dimensional neural recordings by aligning their low-dimensional latent representations
One major challenge that we face in modern neuroscience is that of correspondence, e. g. we do not record the exact same neurons at the exact same times.
Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity
Our approach combines a generative modeling framework with an instance-specific alignment loss that tries to maximize the representational similarity between transformed views of the input (brain state).
Assemblies of neurons learn to classify well-separated distributions
Here we present such a mechanism, and prove rigorously that, for simple classification problems defined on distributions of labeled assemblies, a new assembly representing each class can be reliably formed in response to a few stimuli from the class; this assembly is henceforth reliably recalled in response to new stimuli from the same class.
Mine Your Own vieW: Self-Supervised Learning Through Across-Sample Prediction
State-of-the-art methods for self-supervised learning (SSL) build representations by maximizing the similarity between different transformed "views" of a sample.
Learning with Plasticity Rules: Generalization and Robustness
Here we hypothesize that (a) Brains employ synaptic plasticity rules that serve as proxies for GD; (b) These rules themselves can be learned by GD on the rule parameters; and (c) This process may be a missing ingredient for the development of ANNs that generalize well and are robust to adversarial perturbations.
Hierarchical Optimal Transport for Multimodal Distribution Alignment
Our results demonstrate that when clustered structure exists in datasets, and is consistent across trials or time points, a hierarchical alignment strategy that leverages such structure can provide significant improvements in cross-domain alignment.
