Self-organised criticality via retro-synaptic signals in complex neural networks
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Date
27/06/2016Author
Hernandez-Urbina, Jose Victor
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Abstract
The brain is a complex system par excellence. Its intricate structure has become clearer
recently, and it has been reported that it shares some properties common to complex
networks, such as the small-world property, the presence of hubs, and assortative mixing,
among others. These properties provide the brain with a robust architecture appropriate
for efficient information transmission across different brain regions. Nevertheless,
how these topological properties emerge in neural networks is still an open
question.
Moreover, in the last decade the observation of neuronal avalanches in neocortical
circuits suggested the presence of self-organised criticality in neural systems. The
occurrence of this kind of dynamics implies several benefits to neural computation.
However, the mechanisms that give rise to critical behaviour in these systems, and
how they interact with other neuronal processes such as synaptic plasticity are not
fully understood.
In this thesis, we study self-organised criticality and neural systems in the context
of complex networks. Our work differs from other similar approaches by stressing the
importance of analysing the influence of hubs, high clustering coefficients, and synaptic
plasticity into the collective dynamics of the system. Additionally, we introduce a
metric that we call node success to assess the effectiveness of a spike in terms of its
capacity to trigger cascading behaviour. We present a synaptic plasticity rule based
on this metric, which enables the system to reach the critical state of its collective dynamics
without the need to fine-tune any control parameter. Our results suggest that
retro-synaptic signals could be responsible for the emergence of self-organised criticality
in brain networks. Furthermore, based on the measure of node success, we find
what kind of topology allows nodes to be more successful at triggering cascades of
activity. Our study comprises four different scenarios: i) static synapses, ii) dynamic
synapses under spike-timing-dependent plasticity (STDP), iii) dynamic synapses under
node-success-driven plasticity (NSDP), and iv) dynamic synapses under both NSDP
and STDP mechanisms. We observe that small-world structures emerge when critical
dynamics are combined with STDP mechanisms in a particular type of topology.
Moreover, we go beyond simple spike pairs of STDP, and implement spike triplets to
assess their influence on the dynamics of the system. To the best of our knowledge
this is the first study that implements this version of STDP in the context of critical
dynamics in complex networks.
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