A neural cocktail-party processor
Biological Cybernetics
Elements of information theory
Elements of information theory
Spikes: exploring the neural code
Spikes: exploring the neural code
Predictability, Complexity, and Learning
Neural Computation
Bayesian spiking neurons i: Inference
Neural Computation
Chaos and Coarse Graining in Statistical Mechanics
Chaos and Coarse Graining in Statistical Mechanics
Graph spectra as a systematic tool in computational biology
Discrete Applied Mathematics
Randomness, chaos, and structure
Complexity
Creating Brain-Like Intelligence
Creating Brain-Like Intelligence
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Brains and artificial brainlike structures are paradigms of complex systems, and as such, they require a wide range of mathematical tools for their analysis. One can analyze their static structure as a network abstracted from neuroanatomical results of the arrangement of neurons and the synaptic connections between them. Such structures could underly, for instance, feature binding when neuronal groups coding for specific properties of objects are linked to neurons that represent the spatial location of the object in question. --- One can then investigate what types of dynamics such abstracted networks can support, and what dynamical phenomena can readily occur. An example is synchronization. In fact, flexible and rapid synchronization between specific groups of neurons has been suggested as a dynamical mechanism for feature binding in brains [54]. In order to identify non-trivial dynamical patterns with complex structures, one needs corresponding complexity measures, as developed in [51,52,5]. Ultimately, however, any such dynamical features derive their meaning from their role in processing information. Neurons filter and select information, encode it by transforming it into an internal representation, and possibly also decode it, for instance by deriving specific motor commands as a reaction to certain sensory information.