17,399 research outputs found
Learning recurrent representations for hierarchical behavior modeling
We propose a framework for detecting action patterns from motion sequences
and modeling the sensory-motor relationship of animals, using a generative
recurrent neural network. The network has a discriminative part (classifying
actions) and a generative part (predicting motion), whose recurrent cells are
laterally connected, allowing higher levels of the network to represent high
level phenomena. We test our framework on two types of data, fruit fly behavior
and online handwriting. Our results show that 1) taking advantage of unlabeled
sequences, by predicting future motion, significantly improves action detection
performance when training labels are scarce, 2) the network learns to represent
high level phenomena such as writer identity and fly gender, without
supervision, and 3) simulated motion trajectories, generated by treating motion
prediction as input to the network, look realistic and may be used to
qualitatively evaluate whether the model has learnt generative control rules
Identification of Invariant Sensorimotor Structures as a Prerequisite for the Discovery of Objects
Perceiving the surrounding environment in terms of objects is useful for any
general purpose intelligent agent. In this paper, we investigate a fundamental
mechanism making object perception possible, namely the identification of
spatio-temporally invariant structures in the sensorimotor experience of an
agent. We take inspiration from the Sensorimotor Contingencies Theory to define
a computational model of this mechanism through a sensorimotor, unsupervised
and predictive approach. Our model is based on processing the unsupervised
interaction of an artificial agent with its environment. We show how
spatio-temporally invariant structures in the environment induce regularities
in the sensorimotor experience of an agent, and how this agent, while building
a predictive model of its sensorimotor experience, can capture them as densely
connected subgraphs in a graph of sensory states connected by motor commands.
Our approach is focused on elementary mechanisms, and is illustrated with a set
of simple experiments in which an agent interacts with an environment. We show
how the agent can build an internal model of moving but spatio-temporally
invariant structures by performing a Spectral Clustering of the graph modeling
its overall sensorimotor experiences. We systematically examine properties of
the model, shedding light more globally on the specificities of the paradigm
with respect to methods based on the supervised processing of collections of
static images.Comment: 24 pages, 10 figures, published in Frontiers Robotics and A
Evolution and Analysis of Embodied Spiking Neural Networks Reveals Task-Specific Clusters of Effective Networks
Elucidating principles that underlie computation in neural networks is
currently a major research topic of interest in neuroscience. Transfer Entropy
(TE) is increasingly used as a tool to bridge the gap between network
structure, function, and behavior in fMRI studies. Computational models allow
us to bridge the gap even further by directly associating individual neuron
activity with behavior. However, most computational models that have analyzed
embodied behaviors have employed non-spiking neurons. On the other hand,
computational models that employ spiking neural networks tend to be restricted
to disembodied tasks. We show for the first time the artificial evolution and
TE-analysis of embodied spiking neural networks to perform a
cognitively-interesting behavior. Specifically, we evolved an agent controlled
by an Izhikevich neural network to perform a visual categorization task. The
smallest networks capable of performing the task were found by repeating
evolutionary runs with different network sizes. Informational analysis of the
best solution revealed task-specific TE-network clusters, suggesting that
within-task homogeneity and across-task heterogeneity were key to behavioral
success. Moreover, analysis of the ensemble of solutions revealed that
task-specificity of TE-network clusters correlated with fitness. This provides
an empirically testable hypothesis that links network structure to behavior.Comment: Camera ready version of accepted for GECCO'1
Unsupervised Emergence of Egocentric Spatial Structure from Sensorimotor Prediction
Despite its omnipresence in robotics application, the nature of spatial knowledgeand the mechanisms that underlie its emergence in autonomous agents are stillpoorly understood. Recent theoretical works suggest that the Euclidean structure ofspace induces invariants in an agent’s raw sensorimotor experience. We hypothesizethat capturing these invariants is beneficial for sensorimotor prediction and that,under certain exploratory conditions, a motor representation capturing the structureof the external space should emerge as a byproduct of learning to predict futuresensory experiences. We propose a simple sensorimotor predictive scheme, applyit to different agents and types of exploration, and evaluate the pertinence of thesehypotheses. We show that a naive agent can capture the topology and metricregularity of its sensor’s position in an egocentric spatial frame without any a prioriknowledge, nor extraneous supervision
Extending Feynman's Formalisms for Modelling Human Joint Action Coordination
The recently developed Life-Space-Foam approach to goal-directed human action
deals with individual actor dynamics. This paper applies the model to
characterize the dynamics of co-action by two or more actors. This dynamics is
modelled by: (i) a two-term joint action (including cognitive/motivatonal
potential and kinetic energy), and (ii) its associated adaptive path integral,
representing an infinite--dimensional neural network. Its feedback adaptation
loop has been derived from Bernstein's concepts of sensory corrections loop in
human motor control and Brooks' subsumption architectures in robotics.
Potential applications of the proposed model in human--robot interaction
research are discussed.
Keywords: Psycho--physics, human joint action, path integralsComment: 6 pages, Late
Action and behavior: a free-energy formulation
We have previously tried to explain perceptual inference and learning under a free-energy principle that pursues Helmholtz’s agenda to understand the brain in terms of energy minimization. It is fairly easy to show that making inferences about the causes of sensory data can be cast as the minimization of a free-energy bound on the likelihood of sensory inputs, given an internal model of how they were caused. In this article, we consider what would happen if the data themselves were sampled to minimize this bound. It transpires that the ensuing active sampling or inference is mandated by ergodic arguments based on the very existence of adaptive agents. Furthermore, it accounts for many aspects of motor behavior; from retinal stabilization to goal-seeking. In particular, it suggests that motor control can be understood as fulfilling prior expectations about proprioceptive sensations. This formulation can explain why adaptive behavior emerges in biological agents and suggests a simple alternative to optimal control theory. We illustrate these points using simulations of oculomotor control and then apply to same principles to cued and goal-directed movements. In short, the free-energy formulation may provide an alternative perspective on the motor control that places it in an intimate relationship with perception
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