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Found 21 papers, 6 papers with code
How does the brain compute with probabilities?
This perspective piece is the result of a Generative Adversarial Collaboration (GAC) tackling the question `How does neural activity represent probability distributions?'.
Task adaption by biologically inspired stochastic comodulation
Brain representations must strike a balance between generalizability and adaptability.
Desiderata for normative models of synaptic plasticity
In this review, we organize work on normative plasticity models in terms of a set of desiderata which, when satisfied, are designed to guarantee that a model has a clear link between plasticity and adaptive behavior, consistency with known biological evidence about neural plasticity, and specific testable predictions.
Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution
Neuroscience has long been an essential driver of progress in artificial intelligence (AI).
A probabilistic framework for task-aligned intra- and inter-area neural manifold estimation
Latent manifolds provide a compact characterization of neural population activity and of shared co-variability across brain areas.
Impression learning: Online representation learning with synaptic plasticity
Understanding how the brain constructs statistical models of the sensory world remains a longstanding challenge for computational neuroscience.
A sampling-based circuit for optimal decision making
Many features of human and animal behavior can be understood in the framework of Bayesian inference and optimal decision making, but the biological substrate of such processes is not fully understood.
Across-animal odor decoding by probabilistic manifold alignment
Identifying the common structure of neural dynamics across subjects is key for extracting unifying principles of brain computation and for many brain machine interface applications.
Current State and Future Directions for Learning in Biological Recurrent Neural Networks: A Perspective Piece
We provide a brief review of the common assumptions about biological learning with findings from experimental neuroscience and contrast them with the efficiency of gradient-based learning in recurrent neural networks.
Online hyperparameter optimization by real-time recurrent learning
Conventional hyperparameter optimization methods are computationally intensive and hard to generalize to scenarios that require dynamically adapting hyperparameters, such as life-long learning.
Efficient estimation of neural tuning during naturalistic behavior
Recent technological advances in systems neuroscience have led to a shift away from using simple tasks, with low-dimensional, well-controlled stimuli, towards trying to understand neural activity during naturalistic behavior.
Learning efficient task-dependent representations with synaptic plasticity
Neural populations encode the sensory world imperfectly: their capacity is limited by the number of neurons, availability of metabolic and other biophysical resources, and intrinsic noise.
Flexible information routing in neural populations through stochastic comodulation
It has been observed that trial-to-trial neural activity is modulated by a shared, low-dimensional, stochastic signal that introduces task-irrelevant noise.
Evaluating biological plausibility of learning algorithms the lazy way
To which extent can successful machine learning inform our understanding of biological learning?
A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks
We present a framework for compactly summarizing many recent results in efficient and/or biologically plausible online training of recurrent neural networks (RNN).
Using local plasticity rules to train recurrent neural networks
To learn useful dynamics on long time scales, neurons must use plasticity rules that account for long-term, circuit-wide effects of synaptic changes.
Estimating Nonlinear Neural Response Functions using GP Priors and Kronecker Methods
Jointly characterizing neural responses in terms of several external variables promises novel insights into circuit function, but remains computationally prohibitive in practice.
Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics
We introduce a novel generative mixture model that accounts for the class-specific statistics of stimulus intensities, and we derive a neural circuit that learns the input classes and their intensities.
Spatio-temporal Representations of Uncertainty in Spiking Neural Networks
It has been long argued that, because of inherent ambiguity and noise, the brain needs to represent uncertainty in the form of probability distributions.
Correlations strike back (again): the case of associative memory retrieval
It has long been recognised that statistical dependencies in neuronal activity need to be taken into account when decoding stimuli encoded in a neural population.
Two is better than one: distinct roles for familiarity and recollection in retrieving palimpsest memories
Storing a new pattern in a palimpsest memory system comes at the cost of interfering with the memory traces of previously stored items.
