370,785 research outputs found
Critical neural networks with short and long term plasticity
In recent years self organised critical neuronal models have provided
insights regarding the origin of the experimentally observed avalanching
behaviour of neuronal systems. It has been shown that dynamical synapses, as a
form of short-term plasticity, can cause critical neuronal dynamics. Whereas
long-term plasticity, such as hebbian or activity dependent plasticity, have a
crucial role in shaping the network structure and endowing neural systems with
learning abilities. In this work we provide a model which combines both
plasticity mechanisms, acting on two different time-scales. The measured
avalanche statistics are compatible with experimental results for both the
avalanche size and duration distribution with biologically observed percentages
of inhibitory neurons. The time-series of neuronal activity exhibits temporal
bursts leading to 1/f decay in the power spectrum. The presence of long-term
plasticity gives the system the ability to learn binary rules such as XOR,
providing the foundation of future research on more complicated tasks such as
pattern recognition.Comment: 8 pages, 7 figure
Negative Results in Computer Vision: A Perspective
A negative result is when the outcome of an experiment or a model is not what
is expected or when a hypothesis does not hold. Despite being often overlooked
in the scientific community, negative results are results and they carry value.
While this topic has been extensively discussed in other fields such as social
sciences and biosciences, less attention has been paid to it in the computer
vision community. The unique characteristics of computer vision, particularly
its experimental aspect, call for a special treatment of this matter. In this
paper, I will address what makes negative results important, how they should be
disseminated and incentivized, and what lessons can be learned from cognitive
vision research in this regard. Further, I will discuss issues such as computer
vision and human vision interaction, experimental design and statistical
hypothesis testing, explanatory versus predictive modeling, performance
evaluation, model comparison, as well as computer vision research culture
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