2,633 research outputs found
Biologically inspired learning in a layered neural net
A feed-forward neural net with adaptable synaptic weights and fixed, zero or
non-zero threshold potentials is studied, in the presence of a global feedback
signal that can only have two values, depending on whether the output of the
network in reaction to its input is right or wrong.
It is found, on the basis of four biologically motivated assumptions, that
only two forms of learning are possible, Hebbian and Anti-Hebbian learning.
Hebbian learning should take place when the output is right, while there should
be Anti-Hebbian learning when the output is wrong.
For the Anti-Hebbian part of the learning rule a particular choice is made,
which guarantees an adequate average neuronal activity without the need of
introducing, by hand, control mechanisms like extremal dynamics. A network with
realistic, i.e., non-zero threshold potentials is shown to perform its task of
realizing the desired input-output relations best if it is sufficiently
diluted, i.e. if only a relatively low fraction of all possible synaptic
connections is realized
Rotation of Late-Type Stars in Praesepe with K2
We have Fourier analyzed 941 K2 light curves of likely members of Praesepe,
measuring periods for 86% and increasing the number of rotation periods (P) by
nearly a factor of four. The distribution of P vs. (V-K), a mass proxy, has
three different regimes: (V-K)<1.3, where the rotation rate rapidly slows as
mass decreases; 1.3<(V-K)<4.5, where the rotation rate slows more gradually as
mass decreases; and (V-K)>4.5, where the rotation rate rapidly increases as
mass decreases. In this last regime, there is a bimodal distribution of
periods, with few between 2 and 10 days. We interpret this to mean
that once M stars start to slow down, they do so rapidly. The K2 period-color
distribution in Praesepe (790 Myr) is much different than in the Pleiades
(125 Myr) for late F, G, K, and early-M stars; the overall distribution
moves to longer periods, and is better described by 2 line segments. For mid-M
stars, the relationship has similarly broad scatter, and is steeper in
Praesepe. The diversity of lightcurves and of periodogram types is similar in
the two clusters; about a quarter of the periodic stars in both clusters have
multiple significant periods. Multi-periodic stars dominate among the higher
masses, starting at a bluer color in Praesepe ((V-K)1.5) than in the
Pleiades ((V-K)2.6). In Praesepe, there are relatively more light curves
that have two widely separated periods, 6 days. Some of these could
be examples of M star binaries where one star has spun down but the other has
not.Comment: Accepted by Ap
A Heterosynaptic Learning Rule for Neural Networks
In this article we intoduce a novel stochastic Hebb-like learning rule for
neural networks that is neurobiologically motivated. This learning rule
combines features of unsupervised (Hebbian) and supervised (reinforcement)
learning and is stochastic with respect to the selection of the time points
when a synapse is modified. Moreover, the learning rule does not only affect
the synapse between pre- and postsynaptic neuron, which is called homosynaptic
plasticity, but effects also further remote synapses of the pre- and
postsynaptic neuron. This more complex form of synaptic plasticity has recently
come under investigations in neurobiology and is called heterosynaptic
plasticity. We demonstrate that this learning rule is useful in training neural
networks by learning parity functions including the exclusive-or (XOR) mapping
in a multilayer feed-forward network. We find, that our stochastic learning
rule works well, even in the presence of noise. Importantly, the mean learning
time increases with the number of patterns to be learned polynomially,
indicating efficient learning.Comment: 19 page
Functional Optimization in Complex Excitable Networks
We study the effect of varying wiring in excitable random networks in which
connection weights change with activity to mold local resistance or
facilitation due to fatigue. Dynamic attractors, corresponding to patterns of
activity, are then easily destabilized according to three main modes, including
one in which the activity shows chaotic hopping among the patterns. We describe
phase transitions to this regime, and show a monotonous dependence of critical
parameters on the heterogeneity of the wiring distribution. Such correlation
between topology and functionality implies, in particular, that tasks which
require unstable behavior --such as pattern recognition, family discrimination
and categorization-- can be most efficiently performed on highly heterogeneous
networks. It also follows a possible explanation for the abundance in nature of
scale--free network topologies.Comment: 7 pages, 3 figure
Bump formation in a binary attractor neural network
This paper investigates the conditions for the formation of local bumps in
the activity of binary attractor neural networks with spatially dependent
connectivity. We show that these formations are observed when asymmetry between
the activity during the retrieval and learning is imposed. Analytical
approximation for the order parameters is derived. The corresponding phase
diagram shows a relatively large and stable region, where this effect is
observed, although the critical storage and the information capacities
drastically decrease inside that region. We demonstrate that the stability of
the network, when starting from the bump formation, is larger than the
stability when starting even from the whole pattern. Finally, we show a very
good agreement between the analytical results and the simulations performed for
different topologies of the network.Comment: about 14 page
Action in cognition: the case of language
Empirical research has shown that the processing of words and sentences is accompanied by activation of the brain's motor system in language users. The degree of precision observed in this activation seems to be contingent upon (1) the meaning of a linguistic construction and (2) the depth with which readers process that construction. In addition, neurological evidence shows a correspondence between a disruption in the neural correlates of overt action and the disruption of semantic processing of language about action. These converging lines of evidence can be taken to support the hypotheses that motor processes (1) are recruited to understand language that focuses on actions and (2) contribute a unique element to conceptual representation. This article explores the role of this motor recruitment in language comprehension. It concludes that extant findings are consistent with the theorized existence of multimodal, embodied representations of the referents of words and the meaning carried by language. Further, an integrative conceptualization of “fault tolerant comprehension” is proposed
Unstable Dynamics, Nonequilibrium Phases and Criticality in Networked Excitable Media
Here we numerically study a model of excitable media, namely, a network with
occasionally quiet nodes and connection weights that vary with activity on a
short-time scale. Even in the absence of stimuli, this exhibits unstable
dynamics, nonequilibrium phases -including one in which the global activity
wanders irregularly among attractors- and 1/f noise while the system falls into
the most irregular behavior. A net result is resilience which results in an
efficient search in the model attractors space that can explain the origin of
certain phenomenology in neural, genetic and ill-condensed matter systems. By
extensive computer simulation we also address a relation previously conjectured
between observed power-law distributions and the occurrence of a "critical
state" during functionality of (e.g.) cortical networks, and describe the
precise nature of such criticality in the model.Comment: 18 pages, 9 figure
Learning by message-passing in networks of discrete synapses
We show that a message-passing process allows to store in binary "material"
synapses a number of random patterns which almost saturates the information
theoretic bounds. We apply the learning algorithm to networks characterized by
a wide range of different connection topologies and of size comparable with
that of biological systems (e.g. ). The algorithm can be
turned into an on-line --fault tolerant-- learning protocol of potential
interest in modeling aspects of synaptic plasticity and in building
neuromorphic devices.Comment: 4 pages, 3 figures; references updated and minor corrections;
accepted in PR
Supervised Learning in Multilayer Spiking Neural Networks
The current article introduces a supervised learning algorithm for multilayer
spiking neural networks. The algorithm presented here overcomes some
limitations of existing learning algorithms as it can be applied to neurons
firing multiple spikes and it can in principle be applied to any linearisable
neuron model. The algorithm is applied successfully to various benchmarks, such
as the XOR problem and the Iris data set, as well as complex classifications
problems. The simulations also show the flexibility of this supervised learning
algorithm which permits different encodings of the spike timing patterns,
including precise spike trains encoding.Comment: 38 pages, 4 figure
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