4,918 research outputs found
Bigraphical models for protein and membrane interactions
We present a bigraphical framework suited for modeling biological systems
both at protein level and at membrane level. We characterize formally bigraphs
corresponding to biologically meaningful systems, and bigraphic rewriting rules
representing biologically admissible interactions. At the protein level, these
bigraphic reactive systems correspond exactly to systems of kappa-calculus.
Membrane-level interactions are represented by just two general rules, whose
application can be triggered by protein-level interactions in a well-de\"ined
and precise way. This framework can be used to compare and merge models at
different abstraction levels; in particular, higher-level (e.g. mobility)
activities can be given a formal biological justification in terms of low-level
(i.e., protein) interactions. As examples, we formalize in our framework the
vesiculation and the phagocytosis processes
Entanglement and Sources of Magnetic Anisotropy in Radical Pair-Based Avian Magnetoreceptors
One of the principal models of magnetic sensing in migratory birds rests on
the quantum spin-dynamics of transient radical pairs created photochemically in
ocular cryptochrome proteins. We consider here the role of electron spin
entanglement and coherence in determining the sensitivity of a radical
pair-based geomagnetic compass and the origins of the directional response. It
emerges that the anisotropy of radical pairs formed from spin-polarized
molecular triplets could form the basis of a more sensitive compass sensor than
one founded on the conventional hyperfine-anisotropy model. This property
offers new and more flexible opportunities for the design of biologically
inspired magnetic compass sensors
Sparse arrays of signatures for online character recognition
In mathematics the signature of a path is a collection of iterated integrals,
commonly used for solving differential equations. We show that the path
signature, used as a set of features for consumption by a convolutional neural
network (CNN), improves the accuracy of online character recognition---that is
the task of reading characters represented as a collection of paths. Using
datasets of letters, numbers, Assamese and Chinese characters, we show that the
first, second, and even the third iterated integrals contain useful information
for consumption by a CNN.
On the CASIA-OLHWDB1.1 3755 Chinese character dataset, our approach gave a
test error of 3.58%, compared with 5.61% for a traditional CNN [Ciresan et
al.]. A CNN trained on the CASIA-OLHWDB1.0-1.2 datasets won the ICDAR2013
Online Isolated Chinese Character recognition competition.
Computationally, we have developed a sparse CNN implementation that make it
practical to train CNNs with many layers of max-pooling. Extending the MNIST
dataset by translations, our sparse CNN gets a test error of 0.31%.Comment: 10 pages, 2 figure
Deep supervised learning using local errors
Error backpropagation is a highly effective mechanism for learning
high-quality hierarchical features in deep networks. Updating the features or
weights in one layer, however, requires waiting for the propagation of error
signals from higher layers. Learning using delayed and non-local errors makes
it hard to reconcile backpropagation with the learning mechanisms observed in
biological neural networks as it requires the neurons to maintain a memory of
the input long enough until the higher-layer errors arrive. In this paper, we
propose an alternative learning mechanism where errors are generated locally in
each layer using fixed, random auxiliary classifiers. Lower layers could thus
be trained independently of higher layers and training could either proceed
layer by layer, or simultaneously in all layers using local error information.
We address biological plausibility concerns such as weight symmetry
requirements and show that the proposed learning mechanism based on fixed,
broad, and random tuning of each neuron to the classification categories
outperforms the biologically-motivated feedback alignment learning technique on
the MNIST, CIFAR10, and SVHN datasets, approaching the performance of standard
backpropagation. Our approach highlights a potential biological mechanism for
the supervised, or task-dependent, learning of feature hierarchies. In
addition, we show that it is well suited for learning deep networks in custom
hardware where it can drastically reduce memory traffic and data communication
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