234 research outputs found
A computational implementation of a Hebbian learning network and its application to configural forms of acquired equivalence
We describe and report the results of computer simulations of the three-layer Hebbian network informally described by Honey, Close, and Lin (2010): A general account of discrimination that has been shaped by data from configural acquired equivalence experiments that are beyond the scope of alternative models. Simulations implemented a conditional principle components analysis (CPCA) Hebbian learning algorithm and were of four published experimental demonstrations of configural acquired equivalence. Experiments involved training rats on appetitive bi-conditional discriminations in which discrete cues, (w and x) signaled food delivery (+) or its absence (-) in four different contexts (A, B, C and D): Aw+ Bw- Cw+ Dw- Ax- Bx+ Cx- Dx+. Contexts A and C acquired equivalence. In three of the experiments acquired equivalence was evident from subsequent revaluation, from compound testing or from whole-/part-reversal training. The fourth experiment added concurrent bi-conditional discriminations with the same contexts but a pair of additional discrete cues (y and z). The congruent form of the discrimination, in which A and C provided the same information about y and z, was solved relatively readily. Parametric variation allowed the network to successfully simulate the results of each of the four experiments
A differential Hebbian framework for biologically-plausible motor control
In the realm of motor control, artificial agents cannot match the performance
of their biological counterparts. We thus explore a neural control architecture
that is both biologically plausible, and capable of fully autonomous learning.
The architecture consists of feedback controllers that learn to achieve a
desired state by selecting the errors that should drive them. This selection
happens through a family of differential Hebbian learning rules that, through
interaction with the environment, can learn to control systems where the error
responds monotonically to the control signal. We next show that in a more
general case, neural reinforcement learning can be coupled with a feedback
controller to reduce errors that arise non-monotonically from the control
signal. The use of feedback control reduces the complexity of the reinforcement
learning problem, because only a desired value must be learned, with the
controller handling the details of how it is reached. This makes the function
to be learned simpler, potentially allowing to learn more complex actions. We
discuss how this approach could be extended to hierarchical architectures.Comment: 35 pages, 10 figures. Appendix: 9 pages, 2 figure
The computational magic of the ventral stream: sketch of a theory (and why some deep architectures work).
This paper explores the theoretical consequences of a simple assumption: the computational goal of the feedforward path in the ventral stream -- from V1, V2, V4 and to IT -- is to discount image transformations, after learning them during development
Large Deviations of a Spatially-Stationary Network of Interacting Neurons
In this work we determine a process-level Large Deviation Principle (LDP) for
a model of interacting neurons indexed by a lattice . The neurons
are subject to noise, which is modelled as a correlated martingale. The
probability law governing the noise is strictly stationary, and we are
therefore able to find a LDP for the probability laws governing the
stationary empirical measure generated by the neurons in a cube
of length . We use this LDP to determine an LDP for the neural network
model. The connection weights between the neurons evolve according to a
learning rule / neuronal plasticity, and these results are adaptable to a large
variety of neural network models. This LDP is of great use in the mathematical
modelling of neural networks, because it allows a quantification of the
likelihood of the system deviating from its limit, and also a determination of
which direction the system is likely to deviate. The work is also of interest
because there are nontrivial correlations between the neurons even in the
asymptotic limit, thereby presenting itself as a generalisation of traditional
mean-field models
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