Biological neurons communicate with each other using two broad categories of spike event coding: rate-based and temporal.Rate-based coding communicates analog information on a continuous scale through the intensity of bursts of spikes while temporal coding relies on the timing of spike events.It has been shown that temporal coding has higher information capacity than rate based coding, but is much more challenging to model due to difficulties estimating spike-time statistics.In this paper we demonstrate how history dependent NMDA-modulated 'resonant' synapses organised in 'functional synaptic clusters' provide a robust mechanism for decoding temporally structured spike trains.

Spiking neural networks (SNNs) form a large class of neural models distinct from 'classical' continuous-valued networks such as multilayer perceptrons (MLPs).With event-driven dynamics and a continuoustime model, in contrast to the discrete-time model of their classical counterparts, they offer interesting advantages in representational capacity and energy consumption.However, developing models of learning for SNNs has historically proven challenging: as continuous-time systems, their dynamics are much more complex and they cannot benefit from the strong theoretical developments in MLPs such as convergence proofs and optimal gradient descent.Nor do they gain automatically from algorithmic improvements that have produced efficient matrix inversion and batch training methods.Research has focussed largely on the most extensively studied learning mechanisms in SNNs: spike-timing-dependent plasticity (STDP).Although there has been progress here, there are also notable pathologies that have often been solved with a variety of ad-hoc techniques.A relatively recent interesting development is attempts to map classical convolutional neural networks to spiking implementations, but these may not leverage all the claimed advantages of spiking.This tutorial overview looks at existing techniques for learning in SNNs and offers some thoughts for future directions.

This paper presents a study of the movements of a humanoid head-and-neck robot called Eddie. Eddie has a musculo-skeletal structure similar to that found in human necks enabling it to perform head movements that are comparable with human head movements. This study compares the movements of Eddie with those of a more conventional robotic neck structure and with those of a human head. Results show that Eddie's movements are perceived as significantly more natural and by trend more lifelike than the conventional head's. No differences were found with respect to the impression of human-likeness, consciousness, and elegance.

Humans describe images in terms of nouns and adjectives while algorithms operate on images represented as sets of pixels. Bridging this gap between how humans would like to access images versus their typical representation is the goal of image parsing, which involves assigning object and attribute labels to pixels. In this article we propose treating nouns as object labels and adjectives as visual attribute labels. This allows us to formulate the image parsing problem as one of jointly estimating per-pixel object and attribute labels from a set of training images. We propose an efficient (interactive time) solution. Using the extracted labels as handles, our system empowers a user to verbally refine the results. This enables hands-free parsing of an image into pixel-wise object/attribute labels that correspond to human semantics. Verbally selecting objects of interest enables a novel and natural interaction modality that can possibly be used to interact with new generation devices (e.g., smartphones, Google Glass, livingroom devices). We demonstrate our system on a large number of real-world images with varying complexity. To help understand the trade-offs compared to traditional mouse-based interactions, results are reported for both a large-scale quantitative evaluation and a user study.

Detecting visually salient regions in images is one of the fundamental problems in computer vision. We propose a novel method to decompose an image into large scale perceptually homogeneous elements for efficient salient region detection, using a soft image abstraction representation. By considering both appearance similarity and spatial distribution of image pixels, the proposed representation abstracts out unnecessary image details, allowing the assignment of comparable saliency values across similar regions, and producing perceptually accurate salient region detection. We evaluate our salient region detection approach on the largest publicly available dataset with pixel accurate annotations. The experimental results show that the proposed method outperforms 18 alternate methods, reducing the mean absolute error by 25.2% compared to the previous best result, while being computationally more efficient.

We propose a generic method for obtaining nonparametric image warps from noisy point correspondences. Our formulation integrates a huber function into a motion coherence framework. This makes our fitting function especially robust to piecewise correspondence noise (where an image section is consistently mismatched). By utilizing over parameterized curves, we can generate realistic nonparametric image warps from very noisy correspondence. We also demonstrate how our algorithm can be used to help stitch images taken from a panning camera by warping the images onto a virtual push-broom camera imaging plane.

Semantic image segmentation is a problem of simultaneous segmentation and recognition of an input image into regions and their associated categorical labels, such as person, car or cow. A popular way to achieve this goal is to assign a label to every pixel in the input image and impose simple structural constraints on the output label space. Efficient approximation algorithms for solving this labelling problem such as α-expansion have, at best, linear runtime complexity with respect to the number of labels, making them practical only when working in a specific domain that has few classes-of-interest. However when working in a more general setting where the number of classes could easily reach tens of thousands, sub-linear complexity is desired. In this paper we propose meeting this requirement by performing cascaded inference that wraps around the α-expansion algorithm. The cascade both divides the large label set into smaller more manageable ones by way of a hierarchy, and dynamically subdivides the image into smaller and smaller regions during inference. We test our method on the SUN09 dataset with 107 accurately hand labelled classes.

Linear SVMs are efficient in both training and testing, however the data in real applications is rarely linearly separable. Non-linear kernel SVMs are too computationally intensive for applications with large-scale data sets. Recently locally linear classifiers have gained popularity due to their efficiency whilst remaining competitive with kernel methods. The vanilla nearest neighbor algorithm is one of the simplest locally linear classifiers, but it lacks robustness due to the noise often present in real-world data. In this paper, we introduce a novel local classifier, Parametric Nearest Neighbor (P-NN) and its extension Ensemble of P-NN (EP-NN). We parameterize the nearest neighbor algorithm based on the minimum weighted squared Euclidean distances between the data points and the prototypes, where a prototype is represented by a locally linear combination of some data points. Meanwhile, our method attempts to jointly learn both the prototypes and the classifier parameters discriminatively via max-margin. This makes our classifiers suitable to approximate the classification decision boundaries locally based on nonlinear functions. During testing, the computational complexity of both classifiers is linear in the product of the dimension of data and the number of prototypes. Our classification results on MNIST, USPS, LETTER, and Chars 74K are comparable and in some cases are better than many other methods such as the state-of-the-art locally linear classifiers.

The complex dynamics that emerge from systems governed by deterministic chaos offer significant advantages to the neuromorphic engineer. Included in these is the potential for a very large memory store and the ease with which chaotic systems can be controlled. By definition, a chaotic system is a periodic. However, during the course of its trajectory through state space, the chaotic system will come infinitely close to points that it has previously visited. These almost repeating trajectories are referred to as Unstable Periodic Orbits (UPOs). Normally, under the influence of chaos, the trajectory would move away exponentially fast from its previous path, thereby describing a new path on the surface of the attractor. It is possible to apply a simple delayed feedback control mechanism to a chaotic system that will constrain it within one of its UPOs. This article presents a neural implementation of this delayed feedback mechanism. The network presented here is able to stabilize different UPOs in response to different input signals, with each UPO corresponding to a dynamic recognition state for that input. We also present two learning rules for this network, which enables it to adapt to novel inputs in a self-organized manner.