A Spiking Self-Organising Map Combining STDP, Oscillations and Continuous Learning

Open Access article EPSRC EP/C010841/1, EP/J004561/

ConversationPiece II: Displaced and Rehacked

Abstract: Conversations are amazing! Although we usually find the experience enjoyable and even relaxing, when one considers the difficulties of simultaneously generating sig- nals that convey an intended message while at the same time trying to understand the messages of another, then the pleasures of conversation may seem rather surprising. We manage to communicate with each other without knowing quite what will happen next. We quickly manufacture precisely timed sounds and gestures on the fly, which we exchange with each other without clashing—even managing to slip in some imita- tions as we go along! Yet usually meaning is all we really notice. In the Conversa- tionPiece project, we aim to transform conversations into musical sounds using neuro-inspired technology to expose the amazing world of sounds people create when talking with others. Sounds from a microphone are separated into different fre- quency bands by a computer-simulated “ear” (more precisely “basilar membrane”) and analyzed for tone onsets using a lateral-inhibition network, similar to some cor- tical neural networks. The detected events are used to generate musical notes played on a synthesizer either instantaneously or delayed. The first option allows for ex- changing timed sound events between two speakers with a speech-like structure, but without conveying (much) meaning. Delayed feedback further allows self-exploration of one’s own speech. We discuss the current setup (ConversationPiece version II), in- sights from first experiments, and options for future applications

Spatio-temporal pattern recognizers using spiking neurons and spike-timing-dependent plasticity.

It has previously been shown that by using spike-timing-dependent plasticity (STDP), neurons can adapt to the beginning of a repeating spatio-temporal firing pattern in their input. In the present work, we demonstrate that this mechanism can be extended to train recognizers for longer spatio-temporal input signals. Using a number of neurons that are mutually connected by plastic synapses and subject to a global winner-takes-all mechanism, chains of neurons can form where each neuron is selective to a different segment of a repeating input pattern, and the neurons are feed-forwardly connected in such a way that both the correct input segment and the firing of the previous neurons are required in order to activate the next neuron in the chain. This is akin to a simple class of finite state automata. We show that nearest-neighbor STDP (where only the pre-synaptic spike most recent to a post-synaptic one is considered) leads to "nearest-neighbor" chains where connections only form between subsequent states in a chain (similar to classic "synfire chains"). In contrast, "all-to-all spike-timing-dependent plasticity" (where all pre- and post-synaptic spike pairs matter) leads to multiple connections that can span several temporal stages in the chain; these connections respect the temporal order of the neurons. It is also demonstrated that previously learnt individual chains can be "stitched together" by repeatedly presenting them in a fixed order. This way longer sequence recognizers can be formed, and potentially also nested structures. Robustness of recognition with respect to speed variations in the input patterns is shown to depend on rise-times of post-synaptic potentials and the membrane noise. It is argued that the memory capacity of the model is high, but could theoretically be increased using sparse codes

Felix - A Simulation-Tool for Neural Networks (and Dynamical Systems). User Guide.

User Guide for the Felix Simulation Too

Recruitment of visual cortex for language processing in blind individuals: A neurobiological model

After sensory deprivation, the visual cortex is functionally recruited into non-visual cognitive language and semantic processing. Why this functional organization takes place and how its underlying mechanisms work at the neuronal circuit level is still unclear. Here, we use a biologically constrained network model implementing anatomical structure, neurophysiological function and connectivity of the fronto-tempo-occipital cortex to simulate word-meaning acquisition in visually deprived and undeprived (‘healthy control’) brains. Whereas in the ‘undeprived’ simulations only words denoting visual entities grew into the visual domain, the ‘blind’ models unexpectedly produced word-related neuronal circuits extending into visual cortex for all semantic categories (and especially for those carrying action-related meaning). Additionally, during word recognition, the blind model showed long-lasting spiking neural activity compared to the sighted model, a sign for enhanced verbal working memory due to the additional neural recruitment. Three factors are crucial for explaining this deprivation-related growth: (i) changes in the network’s activity balance brought about by the absence of uncorrelated sensory input, (ii) the connectivity structure of the network, and (iii) Hebbian correlation learning. By offering a neurobiological account for neural changes of language processing due to visual deprivation, our model bridges the gap between cellular-level mechanisms and system-level language function in blind humans

A robust sound perception model suitable for neuromorphic implementation

Coath M, Sheik S, Chicca E, Indiveri G, Denham S, Wennekers T. A robust sound perception model suitable for neuromorphic implementation. Neuromorphic Engineering. 2014;7(278):1-10.We have recently demonstrated the emergence of dynamic feature sensitivity through exposure to formative stimuli in a real-time neuromorphic system implementing a hybrid analog/digital network of spiking neurons. This network, inspired by models of auditory processing in mammals, includes several mutually connected layers with distance-dependent transmission delays and learning in the form of spike timing dependent plasticity, which effects stimulus-driven changes in the network connectivity. Here we present results that demonstrate that the network is robust to a range of variations in the stimulus pattern, such as are found in naturalistic stimuli and neural responses. This robustness is a property critical to the development of realistic, electronic neuromorphic systems. We analyze the variability of the response of the network to “noisy” stimuli which allows us to characterize the acuity in information-theoretic terms. This provides an objective basis for the quantitative comparison of networks, their connectivity patterns, and learning strategies, which can inform future design decisions. We also show, using stimuli derived from speech samples, that the principles are robust to other challenges, such as variable presentation rate, that would have to be met by systems deployed in the real world. Finally we demonstrate the potential applicability of the approach to real sounds

Thinking in circuits: toward neurobiological explanation in cognitive neuroscience

Cognitive theory has decomposed human mental abilities into cognitive (sub) systems, and cognitive neuroscience succeeded in disclosing a host of relationships between cognitive systems and specific structures of the human brain. However, an explanation of why specific functions are located in specific brain loci had still been missing, along with a neurobiological model that makes concrete the neuronal circuits that carry thoughts and meaning. Brain theory, in particular the Hebb-inspired neurocybernetic proposals by Braitenberg, now offers an avenue toward explaining brain–mind relationships and to spell out cognition in terms of neuron circuits in a neuromechanistic sense. Central to this endeavor is the theoretical construct of an elementary functional neuronal unit above the level of individual neurons and below that of whole brain areas and systems: the distributed neuronal assembly (DNA) or thought circuit (TC). It is shown that DNA/TC theory of cognition offers an integrated explanatory perspective on brain mechanisms of perception, action, language, attention, memory, decision and conceptual thought. We argue that DNAs carry all of these functions and that their inner structure (e.g., core and halo subcomponents), and their functional activation dynamics (e.g., ignition and reverberation processes) answer crucial localist questions, such as why memory and decisions draw on prefrontal areas although memory formation is normally driven by information in the senses and in the motor system. We suggest that the ability of building DNAs/TCs spread out over different cortical areas is the key mechanism for a range of specifically human sensorimotor, linguistic and conceptual capacities and that the cell assembly mechanism of overlap reduction is crucial for differentiating a vocabulary of actions, symbols and concepts

Opening the Rome-Southampton window for operator mixing matrices

We show that the running of operators which mix under renormalization can be computed fully non-perturbatively as a product of continuum step scaling matrices. These step scaling matrices are obtained by taking the "ratio" of Z matrices computed at different energies in an RI-MOM type scheme for which twisted boundary conditions are an essential ingredient. Our method allows us to relax the bounds of the Rome-Southampton window. We also explain why such a method is important in view of the light quark physics program of the RBC-UKQCD collaborations. To illustrate our method, using n_f=2+1 domain-wall fermions, we compute the non-perturbative running matrix of four-quark operators needed in K->pipi decay and neutral kaon mixing. Our results are then compared to perturbation theory.Comment: 8 pages, 7 figures. v2: PRD version, minor changes and few references adde

Visual cortex recruitment during language processing in blind individuals is explained by Hebbian learning

In blind people, the visual cortex takes on higher cognitive functions, including language. Why this functional organisation mechanistically emerges at the neuronal circuit level is still unclear. Here, we use a biologically constrained network model implementing features of anatomical structure, neurophysiological function and connectivity of fronto-temporal-occipital areas to simulate word-meaning acquisition in visually deprived and undeprived brains. We observed that, only under visual deprivation, distributed word-related neural circuits ‘grew into’ the deprived visual areas, which therefore adopted a linguistic-semantic role. Three factors are crucial for explaining this deprivation-related growth: changes in the network’s activity balance brought about by the absence of uncorrelated sensory input, the connectivity structure of the network, and Hebbian correlation learning. In addition, the blind model revealed long-lasting spiking neural activity compared to the sighted model during word recognition, which is a neural correlate of enhanced verbal working memory. The present neurocomputational model offers a neurobiological account for neural changes followed by sensory deprivation, thus closing the gap between cellular-level mechanisms, system-level linguistic and semantic function