Abstract Since dynamical systems are an integral part of many scientific domains and can be inherently computational, analyses that reveal in detail the functions they compute can provide the basis for far-reaching advances in various disciplines. One metric that enables such analysis is the information processing capacity. This method not only provides us with information about the complexity of a system’s computations in an interpretable form, but also indicates its different processing modes with different requirements on memory and nonlinearity. In this paper, we provide a guideline for adapting the application of this metric to continuous-time systems in general and spiking neural networks in particular. We investigate ways to operate ...
ABSTRACT Cognitive functions arise from the coordinated activity of neural populations distributed o...
The way brain networks maintain high transmission efficiency is believed to be fundamental in unders...
We consider the information transmission problem in neurons and its possible implications for learni...
Since dynamical systems are an integral part of many scientific domains and can be inherently comput...
Many dynamical systems, both natural and artificial, are stimulated by time dependent external signa...
(a) Illustration of the setup used to assess computational capacity. An input signal, u, is used to ...
Critical dynamics have been postulated as an ideal regime for neuronal networks in the brain, consid...
Physical dynamical systems are able to process information in a nontrivial manner. The machine learn...
According to the classical efficient-coding hypothesis, biological neurons are naturally adapted to ...
The human brain efficiently processes information by analog integration of inputs and digital, binar...
[eng] Physical dynamical systems are able to process information in a nontrivial manner. The machin...
The neural network is a powerful computing framework that has been exploited by biological evolution...
that has attracted a number of researchers is the mathematical evaluation of neural networks as info...
How much information do large brain networks integrate as a whole over the sum of their parts? Can t...
The brain is an information processing machine and thus naturally lends itself to be studied using c...
ABSTRACT Cognitive functions arise from the coordinated activity of neural populations distributed o...
The way brain networks maintain high transmission efficiency is believed to be fundamental in unders...
We consider the information transmission problem in neurons and its possible implications for learni...
Since dynamical systems are an integral part of many scientific domains and can be inherently comput...
Many dynamical systems, both natural and artificial, are stimulated by time dependent external signa...
(a) Illustration of the setup used to assess computational capacity. An input signal, u, is used to ...
Critical dynamics have been postulated as an ideal regime for neuronal networks in the brain, consid...
Physical dynamical systems are able to process information in a nontrivial manner. The machine learn...
According to the classical efficient-coding hypothesis, biological neurons are naturally adapted to ...
The human brain efficiently processes information by analog integration of inputs and digital, binar...
[eng] Physical dynamical systems are able to process information in a nontrivial manner. The machin...
The neural network is a powerful computing framework that has been exploited by biological evolution...
that has attracted a number of researchers is the mathematical evaluation of neural networks as info...
How much information do large brain networks integrate as a whole over the sum of their parts? Can t...
The brain is an information processing machine and thus naturally lends itself to be studied using c...
ABSTRACT Cognitive functions arise from the coordinated activity of neural populations distributed o...
The way brain networks maintain high transmission efficiency is believed to be fundamental in unders...
We consider the information transmission problem in neurons and its possible implications for learni...