Storage of phase-coded patterns via STDP in fully-connected and sparse network: a study of the network capacity

We study the storage and retrieval of phase-coded patterns as stable dynamical attractors in recurrent neural networks, for both an analog and a integrate-and-fire spiking model. The synaptic strength is determined by a learning rule based on spike-time-dependent plasticity, with an asymmetric time window depending on the relative timing between pre- and post-synaptic activity. We store multiple patterns and study the network capacity. For the analog model, we find that the network capacity scales linearly with the network size, and that both capacity and the oscillation frequency of the retrieval state depend on the asymmetry of the learning time window. In addition to fully-connected networks, we study sparse networks, where each neuron is connected only to a small number z << N of other neurons. Connections can be short range, between neighboring neurons placed on a regular lattice, or long range, between randomly chosen pairs of neurons. We find that a small fraction of long range connections is able to amplify the capacity of the network. This imply that a small-world-network topology is optimal, as a compromise between the cost of long range connections and the capacity increase. Also in the spiking integrate and fire model the crucial result of storing and retrieval of multiple phase-coded patterns is observed. The capacity of the fully-connected spiking network is investigated, together with the relation between oscillation frequency of retrieval state and window asymmetry

Error analysis of expressive analogy task in Spanish-English bilingual school age children with and without specific language impairment

textPurpose: The relational shift hypothesis (RSH) states that, as children age, the way in which they interpret analogies shifts from a focus on object similarities to relational aspects of objects. This study investigated the validity of the RSH by describing the error patterns of typically developing (TD), low normal (LN), and language impaired (LI) bilingual school-age children when completing an expressive analogy task in A:B::C:D format (e.g. good:bad::happy:_____) in English and Spanish. Method: Participants included a total of 49 Spanish-English bilingual children between the ages of 7;4 and 8; 9 (mean = 8; 1). Ten children were identified as LI, ten scored in the LN range, and 29 were TD. Children were administered English and Spanish versions of the item twice, initially during the second grade and once again approximately one year later. Responses were recorded verbatim and coded as correct (C), thematic/category error (THEM/CAT), wrong object, correct relationship error (WO-CR), unrelated error (UNREL), or repetition/no response (REP/NR). Results: A repeated measures ANOVA was used to compare children’s analogy scores by time, ability, and language. Results demonstrated significant differences for ability. Four chi square tests investigated the error patterns of TD, LN, and LI bilingual children in English and Spanish. We compared responses provided children by response type (C, THEM/CAT, WO-CR, UNREL, or REP/NR). Results from the Spanish analogical reasoning task indicated a decrease in THEM/CAT with age for the LN and TD children. Results from the English analogical reasoning task were inconsistent. Conclusions: Results provide partial support for the RSH in LN and TD children, but not in children with LI. This difference in error patterns may provide insight into the validity of the RSH in bilingual children with specific language impairment and typically developing second language learners.Communication Sciences and Disorder

Applying the Verona Coding Definitions of Emotional Sequences (VR-CoDES) in the dental context involving patients with complex communication needs : an exploratory study

This study was conducted as part of a larger collaborative study funded by the EPSRC, between the University of St Andrews and the University of Dundee.Objective The VR-CoDES has been previously applied in the dental context. However, we know little about how dental patients with intellectual disabilities (ID) and complex communication needs express their emotional distress during dental visits. This is the first study explored the applicability of the VR-CoDES to a dental context involving patients with ID. Methods Fourteen dental consultations were video recorded and coded using the VR-CoDES, assisted with the additional guidelines for the VR-CoDES in a dental context. Both inter- and intra-coder reliabilities were checked on the seven consultations where cues were observed. Results Sixteen cues (eight non-verbal) were identified within seven of the 14 consultations. Twenty responses were observed (12 reducing space) with four multiple responses. Cohen's Kappa were 0.76 (inter-coder) and 0.88 (intra-coder). Conclusion With the additional guidelines, cues and responses were reliably identified. Cue expression was exhibited by non-verbal expression of emotion with people with ID in the literature. Further guidance is needed to improve the coding accuracy on multiple providers’ responses and to investigate potential impacts of conflicting responses on patients. Practice implications The findings provided a useful initial step towards an ongoing exploration of how healthcare providers identify and manage emotional distress of patients with ID.PostprintPeer reviewe

Feature Markov Decision Processes

General purpose intelligent learning agents cycle through (complex,non-MDP) sequences of observations, actions, and rewards. On the other hand, reinforcement learning is well-developed for small finite state Markov Decision Processes (MDPs). So far it is an art performed by human designers to extract the right state representation out of the bare observations, i.e. to reduce the agent setup to the MDP framework. Before we can think of mechanizing this search for suitable MDPs, we need a formal objective criterion. The main contribution of this article is to develop such a criterion. I also integrate the various parts into one learning algorithm. Extensions to more realistic dynamic Bayesian networks are developed in a companion article.Comment: 7 page

The self-organization of combinatoriality and phonotactics in vocalization systems

This paper shows how a society of agents can self-organize a shared vocalization system that is discrete, combinatorial and has a form of primitive phonotactics, starting from holistic inarticulate vocalizations. The originality of the system is that: (1) it does not include any explicit pressure for communication; (2) agents do not possess capabilities of coordinated interactions, in particular they do not play language games; (3) agents possess no specific linguistic capacities; and (4) initially there exists no convention that agents can use. As a consequence, the system shows how a primitive speech code may bootstrap in the absence of a communication system between agents, i.e. before the appearance of language

Feature Reinforcement Learning: Part I: Unstructured MDPs

General-purpose, intelligent, learning agents cycle through sequences of observations, actions, and rewards that are complex, uncertain, unknown, and non-Markovian. On the other hand, reinforcement learning is well-developed for small finite state Markov decision processes (MDPs). Up to now, extracting the right state representations out of bare observations, that is, reducing the general agent setup to the MDP framework, is an art that involves significant effort by designers. The primary goal of this work is to automate the reduction process and thereby significantly expand the scope of many existing reinforcement learning algorithms and the agents that employ them. Before we can think of mechanizing this search for suitable MDPs, we need a formal objective criterion. The main contribution of this article is to develop such a criterion. I also integrate the various parts into one learning algorithm. Extensions to more realistic dynamic Bayesian networks are developed in Part II. The role of POMDPs is also considered there.Comment: 24 LaTeX pages, 5 diagram

Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor

Neuromorphic computing is a new paradigm for design of both the computing hardware and algorithms inspired by biological neural networks. The event-based nature and the inherent parallelism make neuromorphic computing a promising paradigm for building efficient neural network based architectures for control of fast and agile robots. In this paper, we present a spiking neural network architecture that uses sensory feedback to control rotational velocity of a robotic vehicle. When the velocity reaches the target value, the mapping from the target velocity of the vehicle to the correct motor command, both represented in the spiking neural network on the neuromorphic device, is autonomously stored on the device using on-chip plastic synaptic weights. We validate the controller using a wheel motor of a miniature mobile vehicle and inertia measurement unit as the sensory feedback and demonstrate online learning of a simple 'inverse model' in a two-layer spiking neural network on the neuromorphic chip. The prototype neuromorphic device that features 256 spiking neurons allows us to realise a simple proof of concept architecture for the purely neuromorphic motor control and learning. The architecture can be easily scaled-up if a larger neuromorphic device is available.Comment: 6+1 pages, 4 figures, will appear in one of the Robotics conference

Associative memory of phase-coded spatiotemporal patterns in leaky Integrate and Fire networks

We study the collective dynamics of a Leaky Integrate and Fire network in which precise relative phase relationship of spikes among neurons are stored, as attractors of the dynamics, and selectively replayed at differentctime scales. Using an STDP-based learning process, we store in the connectivity several phase-coded spike patterns, and we find that, depending on the excitability of the network, different working regimes are possible, with transient or persistent replay activity induced by a brief signal. We introduce an order parameter to evaluate the similarity between stored and recalled phase-coded pattern, and measure the storage capacity. Modulation of spiking thresholds during replay changes the frequency of the collective oscillation or the number of spikes per cycle, keeping preserved the phases relationship. This allows a coding scheme in which phase, rate and frequency are dissociable. Robustness with respect to noise and heterogeneity of neurons parameters is studied, showing that, since dynamics is a retrieval process, neurons preserve stablecprecise phase relationship among units, keeping a unique frequency of oscillation, even in noisy conditions and with heterogeneity of internal parameters of the units