Noise-robust modes of the retinal population code have the geometry of "ridges" and correspond to neuronal communities
An appealing new principle for neural population codes is that correlations among neurons organize neural activity patterns into a discrete set of clusters, which can each be viewed as a noise-robust population codeword. Previous studies assumed that ...
Delay differential analysis of seizures in multichannel electrocorticography data
High-density electrocorticogram ECoG electrodes are capable of recording neurophysiological data with high temporal resolution with wide spatial coverage. These recordings are a window to understanding how the human brain processes information and ...
First passage time memory lifetimes for simple, multistate synapses
Memory models based on synapses with discrete and bounded strengths store new memories by forgetting old ones. Memory lifetimes in such memory systems may be defined in a variety of ways. A mean first passage time MFPT definition overcomes much of the ...
Capturing the dynamical repertoire of single neurons with generalized linear models
A key problem in computational neuroscience is to find simple, tractable models that are nevertheless flexible enough to capture the response properties of real neurons. Here we examine the capabilities of recurrent point process models known as Poisson ...
Neural decoding: A predictive viewpoint
Decoding in the context of brain-machine interface is a prediction problem, with the aim of retrieving the most accurate kinematic predictions attainable from the available neural signals. While selecting models that reduce the prediction error is done ...
Dopamine, inference, and uncertainty
The hypothesis that the phasic dopamine response reports a reward prediction error has become deeply entrenched. However, dopamine neurons exhibit several notable deviations from this hypothesis. A coherent explanation for these deviations can be ...
Learning simpler language models with the differential state framework
Learning useful information across long time lags is a critical and difficult problem for temporal neural models in tasks such as language modeling. Existing architectures that address the issue are often complex and costly to train. The differential ...
Learning rates for classification with gaussian kernels
This letter aims at refined error analysis for binary classification using support vector machine SVM with gaussian kernel and convex loss. Our first result shows that for some loss functions, such as the truncated quadratic loss and quadratic loss, SVM ...
Refined spectral clustering via embedded label propagation
Spectral clustering is a key research topic in the field of machine learning and data mining. Most of the existing spectral clustering algorithms are built on gaussian Laplacian matrices, which is sensitive to parameters. We propose a novel parameter-...
