3,595 research outputs found
Biologically plausible multi-dimensional reinforcement learning in neural networks
How does the brain learn to map multi-dimensional sensory inputs to multi-dimensional motor outputs when it can only observe single rewards for the coordinated outputs of the whole network of neurons that make up the brain? We introduce Multi-AGREL, a novel, biologically plausible multi-layer neural network model for multi-dimensional reinforcement learning. We demonstrate that Multi-AGREL can learn non-linear mappings from inputs to multi-dimensional outputs by using only scalar reward feedback. We further show that in Multi-AGREL, the changes in the connection weights follow the gradient that minimizes global prediction error, and that all information required for synaptic plasticity is locally present
Closing the loop between neural network simulators and the OpenAI Gym
Since the enormous breakthroughs in machine learning over the last decade,
functional neural network models are of growing interest for many researchers
in the field of computational neuroscience. One major branch of research is
concerned with biologically plausible implementations of reinforcement
learning, with a variety of different models developed over the recent years.
However, most studies in this area are conducted with custom simulation scripts
and manually implemented tasks. This makes it hard for other researchers to
reproduce and build upon previous work and nearly impossible to compare the
performance of different learning architectures. In this work, we present a
novel approach to solve this problem, connecting benchmark tools from the field
of machine learning and state-of-the-art neural network simulators from
computational neuroscience. This toolchain enables researchers in both fields
to make use of well-tested high-performance simulation software supporting
biologically plausible neuron, synapse and network models and allows them to
evaluate and compare their approach on the basis of standardized environments
of varying complexity. We demonstrate the functionality of the toolchain by
implementing a neuronal actor-critic architecture for reinforcement learning in
the NEST simulator and successfully training it on two different environments
from the OpenAI Gym
Biologically plausible deep learning -- but how far can we go with shallow networks?
Training deep neural networks with the error backpropagation algorithm is
considered implausible from a biological perspective. Numerous recent
publications suggest elaborate models for biologically plausible variants of
deep learning, typically defining success as reaching around 98% test accuracy
on the MNIST data set. Here, we investigate how far we can go on digit (MNIST)
and object (CIFAR10) classification with biologically plausible, local learning
rules in a network with one hidden layer and a single readout layer. The hidden
layer weights are either fixed (random or random Gabor filters) or trained with
unsupervised methods (PCA, ICA or Sparse Coding) that can be implemented by
local learning rules. The readout layer is trained with a supervised, local
learning rule. We first implement these models with rate neurons. This
comparison reveals, first, that unsupervised learning does not lead to better
performance than fixed random projections or Gabor filters for large hidden
layers. Second, networks with localized receptive fields perform significantly
better than networks with all-to-all connectivity and can reach backpropagation
performance on MNIST. We then implement two of the networks - fixed, localized,
random & random Gabor filters in the hidden layer - with spiking leaky
integrate-and-fire neurons and spike timing dependent plasticity to train the
readout layer. These spiking models achieve > 98.2% test accuracy on MNIST,
which is close to the performance of rate networks with one hidden layer
trained with backpropagation. The performance of our shallow network models is
comparable to most current biologically plausible models of deep learning.
Furthermore, our results with a shallow spiking network provide an important
reference and suggest the use of datasets other than MNIST for testing the
performance of future models of biologically plausible deep learning.Comment: 14 pages, 4 figure
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