20,842 research outputs found
Gradient-free activation maximization for identifying effective stimuli
A fundamental question for understanding brain function is what types of
stimuli drive neurons to fire. In visual neuroscience, this question has also
been posted as characterizing the receptive field of a neuron. The search for
effective stimuli has traditionally been based on a combination of insights
from previous studies, intuition, and luck. Recently, the same question has
emerged in the study of units in convolutional neural networks (ConvNets), and
together with this question a family of solutions were developed that are
generally referred to as "feature visualization by activation maximization."
We sought to bring in tools and techniques developed for studying ConvNets to
the study of biological neural networks. However, one key difference that
impedes direct translation of tools is that gradients can be obtained from
ConvNets using backpropagation, but such gradients are not available from the
brain. To circumvent this problem, we developed a method for gradient-free
activation maximization by combining a generative neural network with a genetic
algorithm. We termed this method XDream (EXtending DeepDream with real-time
evolution for activation maximization), and we have shown that this method can
reliably create strong stimuli for neurons in the macaque visual cortex (Ponce
et al., 2019). In this paper, we describe extensive experiments characterizing
the XDream method by using ConvNet units as in silico models of neurons. We
show that XDream is applicable across network layers, architectures, and
training sets; examine design choices in the algorithm; and provide practical
guides for choosing hyperparameters in the algorithm. XDream is an efficient
algorithm for uncovering neuronal tuning preferences in black-box networks
using a vast and diverse stimulus space.Comment: 16 pages, 8 figures, 3 table
Deep Neuroevolution of Recurrent and Discrete World Models
Neural architectures inspired by our own human cognitive system, such as the
recently introduced world models, have been shown to outperform traditional
deep reinforcement learning (RL) methods in a variety of different domains.
Instead of the relatively simple architectures employed in most RL experiments,
world models rely on multiple different neural components that are responsible
for visual information processing, memory, and decision-making. However, so far
the components of these models have to be trained separately and through a
variety of specialized training methods. This paper demonstrates the surprising
finding that models with the same precise parts can be instead efficiently
trained end-to-end through a genetic algorithm (GA), reaching a comparable
performance to the original world model by solving a challenging car racing
task. An analysis of the evolved visual and memory system indicates that they
include a similar effective representation to the system trained through
gradient descent. Additionally, in contrast to gradient descent methods that
struggle with discrete variables, GAs also work directly with such
representations, opening up opportunities for classical planning in latent
space. This paper adds additional evidence on the effectiveness of deep
neuroevolution for tasks that require the intricate orchestration of multiple
components in complex heterogeneous architectures
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