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Found 11 papers, 3 papers with code
Approximating nonlinear functions with latent boundaries in low-rank excitatory-inhibitory spiking networks
Overall, our work proposes a new perspective on spiking networks that may serve as a starting point for a mechanistic understanding of biological spike-based computation.
Compact task representations as a normative model for higher-order brain activity
More specifically, we focus on MDPs whose state is based on action-observation histories, and we show how to compress the state space such that unnecessary redundancy is eliminated, while task-relevant information is preserved.
Understanding spiking networks through convex optimization
Here we turn these findings around and show that virtually all inhibition-dominated SNNs can be understood through the lens of convex optimization, with network connectivity, timescales, and firing thresholds being intricately linked to the parameters of underlying convex optimization problems.
Biological credit assignment through dynamic inversion of feedforward networks
Here, we show that feedforward network transformations can be effectively inverted through dynamics.
Extracting Latent Structure From Multiple Interacting Neural Populations
Developments in neural recording technology are rapidly enabling the recording of populations of neurons in multiple brain areas simultaneously, as well as the identification of the types of neurons being recorded (e. g., excitatory vs. inhibitory).
Unsupervised learning of an efficient short-term memory network
Specifically, we show how these networks can learn to efficiently represent their present and past inputs, based on local learning rules only.
Demixed principal component analysis of population activity in higher cortical areas reveals independent representation of task parameters
Neurons in higher cortical areas, such as the prefrontal cortex, are known to be tuned to a variety of sensory and motor variables.
Firing rate predictions in optimal balanced networks
This is an important problem because firing rates are one of the most important measures of network activity, in both the study of neural computation and neural network dynamics.
Learning optimal spike-based representations
We define a loss function for the quality of the population read-out and derive the dynamical equations for both neurons and synapses from the requirement to minimize this loss.
Demixed Principal Component Analysis
Standard dimensionality reduction techniques such as principal component analysis (PCA) can provide a succinct and complete description of the data, but the description is constructed independent of the relevant task variables and is often hard to interpret.
Linear readout from a neural population with partial correlation data
We compare the discrimination thresholds read out from the population of recorded neurons with the discrimination threshold of the monkey and show that our method predicts different results than simpler, average schemes of noise correlations."
