Reservoir computing has been recently proposed as a paradigm of how the brain
processes time-dependent complex information. It relies on a recurrent core of neurons, known as the reservoir, that receives and encodes complex inputs in a highdimensional phase space. Encoding takes place by combining the incoming input
signal with the existing state of the network, which depends on past inputs. This
provides this computational paradigm with its ability to process a temporally varying environment. ...
Reservoir computing has been recently proposed as a paradigm of how the brain
processes time-dependent complex information. It relies on a recurrent core of neurons, known as the reservoir, that receives and encodes complex inputs in a highdimensional phase space. Encoding takes place by combining the incoming input
signal with the existing state of the network, which depends on past inputs. This
provides this computational paradigm with its ability to process a temporally varying environment. While this concept was proposed a few years ago as a potential
mechanism of information processing by the brain, and in spite of the overwhelming
evidence of recurrent connectivity found in the brain, it has been difficult to validate
this hypothesis given the extreme structural complexity of the mammalian brain.
The aim of this thesis is to study how information is propagated within a biological
neural network, using the connectome of C. elegans as a model system. This study
may help to characterize possible biological network architectures such as Reservoir
Computing that explain cognitive behaviors such as thermotaxis. This paradigm
could help in the understanding of how a basic nervous system works, for further
inferences in higher complex systems.
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