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Found 7 papers, 1 papers with code
Neuronal architecture extracts statistical temporal patterns
Concretely, we demonstrate that a simple biologically inspired feedforward neuronal model is able to extract information from up to the third order cumulant to perform time series classification.
Entropy production of Multivariate Ornstein-Uhlenbeck processes correlates with consciousness levels in the human brain
Consciousness is supported by complex patterns of brain activity which are indicative of irreversible non-equilibrium dynamics.
Unfolding recurrence by Green’s functions for optimized reservoir computing
Cortical networks are strongly recurrent, and neurons have intrinsic temporal dynamics.
Unfolding recurrence by Green's functions for optimized reservoir computing
Cortical networks are strongly recurrent, and neurons have intrinsic temporal dynamics.
Capacity of the covariance perceptron
Closed-form expressions reveal superior pattern capacity in the binary classification task compared to the classical perceptron in the case of a high-dimensional input and low-dimensional output.
Propagation of spiking moments in linear Hawkes networks
The present paper provides exact mathematical expressions for the high-order moments of spiking activity in a recurrently-connected network of linear Hawkes processes.
Bayesian estimation for large scale multivariate Ornstein-Uhlenbeck model of brain connectivity
Estimation of reliable whole-brain connectivity is a crucial step towards the use of connectivity information in quantitative approaches to the study of neuropsychiatric disorders.
