440 research outputs found
Short-Term Memory Through Persistent Activity: Evolution of Self-Stopping and Self-Sustaining Activity in Spiking Neural Networks
Memories in the brain are separated in two categories: short-term and
long-term memories. Long-term memories remain for a lifetime, while short-term
ones exist from a few milliseconds to a few minutes. Within short-term memory
studies, there is debate about what neural structure could implement it.
Indeed, mechanisms responsible for long-term memories appear inadequate for the
task. Instead, it has been proposed that short-term memories could be sustained
by the persistent activity of a group of neurons. In this work, we explore what
topology could sustain short-term memories, not by designing a model from
specific hypotheses, but through Darwinian evolution in order to obtain new
insights into its implementation. We evolved 10 networks capable of retaining
information for a fixed duration between 2 and 11s. Our main finding has been
that the evolution naturally created two functional modules in the network: one
which sustains the information containing primarily excitatory neurons, while
the other, which is responsible for forgetting, was composed mainly of
inhibitory neurons. This demonstrates how the balance between inhibition and
excitation plays an important role in cognition.Comment: 28 page
Motility at the origin of life: Its characterization and a model
Due to recent advances in synthetic biology and artificial life, the origin
of life is currently a hot topic of research. We review the literature and
argue that the two traditionally competing "replicator-first" and
"metabolism-first" approaches are merging into one integrated theory of
individuation and evolution. We contribute to the maturation of this more
inclusive approach by highlighting some problematic assumptions that still lead
to an impoverished conception of the phenomenon of life. In particular, we
argue that the new consensus has so far failed to consider the relevance of
intermediate timescales. We propose that an adequate theory of life must
account for the fact that all living beings are situated in at least four
distinct timescales, which are typically associated with metabolism, motility,
development, and evolution. On this view, self-movement, adaptive behavior and
morphological changes could have already been present at the origin of life. In
order to illustrate this possibility we analyze a minimal model of life-like
phenomena, namely of precarious, individuated, dissipative structures that can
be found in simple reaction-diffusion systems. Based on our analysis we suggest
that processes in intermediate timescales could have already been operative in
prebiotic systems. They may have facilitated and constrained changes occurring
in the faster- and slower-paced timescales of chemical self-individuation and
evolution by natural selection, respectively.Comment: 29 pages, 5 figures, Artificial Lif
Self-organization on social media: endo-exo bursts and baseline fluctuations
A salient dynamic property of social media is bursting behavior. In this
paper, we study bursting behavior in terms of the temporal relation between a
preceding baseline fluctuation and the successive burst response using a
frequency time series of 3,000 keywords on Twitter. We found that there is a
fluctuation threshold up to which the burst size increases as the fluctuation
increases and that above the threshold, there appears a variety of burst sizes.
We call this threshold the critical threshold. Investigating this threshold in
relation to endogenous bursts and exogenous bursts based on peak ratio and
burst size reveals that the bursts below this threshold are endogenously caused
and above this threshold, exogenous bursts emerge. Analysis of the 3,000
keywords shows that all the nouns have both endogenous and exogenous origins of
bursts and that each keyword has a critical threshold in the baseline
fluctuation value to distinguish between the two. Having a threshold for an
input value for activating the system implies that Twitter is an excitable
medium. These findings are useful for characterizing how excitable a keyword is
on Twitter and could be used, for example, to predict the response to
particular information on social media.Comment: Presented at WebAL-1: Workshop on Artificial Life and the Web 2014
(arXiv:1406.2507
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