Paper 73 Details


Online Fitting of Computational Cost to Environmental Complexity: Predictive Coding with the ε-network


Lana Sinapayen, Takashi Ikegami


Date: Thursday 7 Sept
Talk Time: TBA
Session: Neural networks 10:30


Predictive coding, Neural network, Complexity


We propose the Epsilon Network (e-network), a network that automatically adjusts its size to the complexity of a stream of data while performing online learning. The network optimises its topology during training, simultaneously adding and removing neurons and weights: it adds neurons where they can raise performance, and removes redundant neurons while preserving performance The network is a neural realisation of the e-machine devised by Crutchfield and al. (Crutchfield and Young (1989)). In this paper our network is trained to predict video frames; we evaluate it on simple, complex, and noisy videos and show that the final number of neurons is a good indicator of the complexity and predictability of the data stream.

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