Neural plasticity and minimal topologies for reward-based learning

Artificial Neural Networks for online learning problems are often implemented with synaptic plasticity to achieve adaptive behaviour. A common problem is that the overall learning dynamics are emergent properties strongly dependent on the correct combination of neural architectures, plasticity rules and environmental features. Which complexity in architectures and learning rules is required to match specific control and learning problems is not clear. Here a set of homosynaptic plasticity rules is applied to topologically unconstrained neural controllers while operating and evolving in dynamic reward-based scenarios. Performances are monitored on simulations of bee foraging problems and T-maze navigation. Varying reward locations compel the neural controllers to adapt their foraging strategies over time, fostering online reward-based learning. In contrast to previous studies, the results here indicate that reward-based learning in complex dynamic scenarios can be achieved with basic plasticity rules and minimal topologies. © 2008 IEEE

Short and long term plasticity as cause-effect hypothesis testing in robotic ambiguous scenarios

Short and long term plasticity as cause-effect hypothesis testing in robotic ambiguous scenario

Online representation learning with single and multi-layer Hebbian networks for image classification

Unsupervised learning permits the development of algorithms that are able to adapt to a variety of different datasets using the same underlying rules thanks to the autonomous discovery of discriminating features during training. Recently, a new class of Hebbian-like and local unsupervised learning rules for neural networks have been developed that minimise a similarity matching costfunction. These have been shown to perform sparse representation learning. This study tests the effectiveness of one such learning rule for learning features from images. The rule implemented is derived from a nonnegative classical multidimensional scaling cost-function, and is applied to both single and multi-layer architectures. The features learned by the algorithm are then used as input to an SVM to test their effectiveness in classification on the established CIFAR-10 image dataset. The algorithm performs well in comparison to other unsupervised learning algorithms and multi-layer networks, thus suggesting its validity in the design of a new class of compact, online learning networks

Movement primitives as a robotic tool to interpret trajectories through learning-by-doing

Articulated movements are fundamental in many human and robotic tasks. While humans can learn and generalise arbitrarily long sequences of movements, and particularly can optimise them to fit the constraints and features of their body, robots are often programmed to execute point-to-point precise but fixed patterns. This study proposes a new approach to interpreting and reproducing articulated and complex trajectories as a set of known robot-based primitives. Instead of achieving accurate reproductions, the proposed approach aims at interpreting data in an agent-centred fashion, according to an agent's primitive movements. The method improves the accuracy of a reproduction with an incremental process that seeks first a rough approximation by capturing the most essential features of a demonstrated trajectory. Observing the discrepancy between the demonstrated and reproduced trajectories, the process then proceeds with incremental decompositions and new searches in sub-optimal parts of the trajectory. The aim is to achieve an agent-centred interpretation and progressive learning that fits in the first place the robots' capability, as opposed to a data-centred decomposition analysis. Tests on both geometric and human generated trajectories reveal that the use of own primitives results in remarkable robustness and generalisation properties of the method. In particular, because trajectories are understood and abstracted by means of agent-optimised primitives, the method has two main features: 1) Reproduced trajectories are general and represent an abstraction of the data. 2) The algorithm is capable of reconstructing highly noisy or corrupted data without pre-processing thanks to an implicit and emergent noise suppression and feature detection. This study suggests a novel bio-inspired approach to interpreting, learning and reproducing articulated movements and trajectories. Possible applications include drawing, writing, movement generation, object manipulation, and other tasks where the performance requires human-like interpretation and generalisation capabilities

Online representation learning with single and multi-layer Hebbian networks for image classification

Unsupervised learning permits the development of algorithms that are able to adapt to a variety of different datasets using the same underlying rules thanks to the autonomous discovery of discriminating features during training. Recently, a new class of Hebbian-like and local unsupervised learning rules for neural networks have been developed that minimise a similarity matching costfunction. These have been shown to perform sparse representation learning. This study tests the effectiveness of one such learning rule for learning features from images. The rule implemented is derived from a nonnegative classical multidimensional scaling cost-function, and is applied to both single and multi-layer architectures. The features learned by the algorithm are then used as input to an SVM to test their effectiveness in classification on the established CIFAR-10 image dataset. The algorithm performs well in comparison to other unsupervised learning algorithms and multi-layer networks, thus suggesting its validity in the design of a new class of compact, online learning networks

POET: an evo-devo method to optimize the weights of a large artificial neural networks

Large search spaces as those of artificial neural networks are difficult to search with machine learning techniques. The large amount of parameters is the main challenge for search techniques that do not exploit correlations expressed as patterns in the parameter space. Evolutionary computation with indirect genotype-phenotype mapping was proposed as a possible solution, but current methods often fail when the space is fractured and presents irregularities. This study employs an evolutionary indirect encoding inspired by developmental biology. Cellular proliferations and deletions of variable size allow for the definition of both regular large areas and small detailed areas in the parameter space. The method is tested on the search of the weights of a neural network for the classification of the MNIST dataset. The results demonstrate that even large networks such as those required for image classification can be effectively automatically designed by the proposed evolutionary developmental method. The combination of real-world problems like vision and classification, evolution and development, endows the proposed method with aspects of particular relevance to artificial life

Editorial: Neural plasticity for rich and uncertain robotic information streams

Editorial: Neural plasticity for rich and uncertain robotic information stream

Building efficient deep Hebbian networks for image classification tasks

Multi-layer models of sparse coding (deep dictionary learning) and dimensionality reduction (PCANet) have shown promise as unsupervised learning models for image classification tasks. However, the pure implementations of these models have limited generalisation capabilities and high computational cost. This work introduces the Deep Hebbian Network (DHN), which combines the advantages of sparse coding, dimensionality reduction, and convolutional neural networks for learning features from images. Unlike in other deep neural networks, in this model, both the learning rules and neural architectures are derived from cost-function minimizations. Moreover, the DHN model can be trained online due to its Hebbian components. Different configurations of the DHN have been tested on scene and image classification tasks. Experiments show that the DHN model can automatically discover highly discriminative features directly from image pixels without using any data augmentation or semi-labeling

Neural networks for efficient nonlinear online clustering

Unsupervised learning techniques, such as clustering and sparse coding, have been adapted for use with data sets exhibiting nonlinear relationships through the use of kernel machines. These techniques often require an explicit computation of the kernel matrix, which becomes expensive as the number of inputs grows, making it unsuitable for efficient online learning. This paper proposes an algorithm and a neural architecture for online approximated nonlinear kernel clustering using any shift-invariant kernel. The novel model outperforms traditional low-rank kernel approximation based clustering methods, it also requires significantly lower memory requirements than those of popular kernel k-means while showing competitive performance on large data sets

Distributed strategy adaptation with a prediction function in multi-agent task allocation

Coordinating multiple agents to complete a set of tasks under time constraints is a complex problem. Distributed consensus-based task allocation algorithms address this problem without the need for human supervision. With such algorithms, agents add tasks to their own schedule according to specified allocation strategies. Various factors, such as the available resources and number of tasks, may affect the efficiency of a particular allocation strategy. The novel idea we suggest is that each individual agent can predict locally the best task inclusion strategy, based on the limited task assignment information communicated among networked agents. Using supervised classification learning, a function is trained to predict the most appropriate strategy between two well known insertion heuristics. Using the proposed method, agents are shown to correctly predict and select the optimal insertion heuristic to achieve the overall highest number of task allocations. The adaptive agents consistently match the performances of the best non-adaptive agents across a variety of scenarios. This study aims to demonstrate the possibility and potential performance benefits of giving agents greater decision making capabilities to independently adapt the task allocation process in line with the problem of interest