The state of MIIND

MIIND (Multiple Interacting Instantiations of Neural Dynamics) is a highly modular multi-level C++ framework, that aims to shorten the development time for models in Cognitive Neuroscience (CNS). It offers reusable code modules (libraries of classes and functions) aimed at solving problems that occur repeatedly in modelling, but tries not to impose a specific modelling philosophy or methodology. At the lowest level, it offers support for the implementation of sparse networks. For example, the library SparseImplementationLib supports sparse random networks and the library LayerMappingLib can be used for sparse regular networks of filter-like operators. The library DynamicLib, which builds on top of the library SparseImplementationLib, offers a generic framework for simulating network processes. Presently, several specific network process implementations are provided in MIIND: the Wilson–Cowan and Ornstein–Uhlenbeck type, and population density techniques for leaky-integrate-and-fire neurons driven by Poisson input. A design principle of MIIND is to support detailing: the refinement of an originally simple model into a form where more biological detail is included. Another design principle is extensibility: the reuse of an existing model in a larger, more extended one. One of the main uses of MIIND so far has been the instantiation of neural models of visual attention. Recently, we have added a library for implementing biologically-inspired models of artificial vision, such as HMAX and recent successors. In the long run we hope to be able to apply suitably adapted neuronal mechanisms of attention to these artificial models

“Making meaning”: Communication between sign language users without a shared language

In a small group of deaf sign language users from different countries and with no shared language, the signers’ initial conversational interactions are investigated as they meet in pairs for the very first time. This case study allows for a unique insight into the initial stages of pidginisation and the conceptual processes involved. The participants use a wide range of linguistic and communicative resources, and it can be argued that they construct shared multilingual-multimodal cognitive spaces for the purpose of these conversations. This research explores the nature of these shared multilingual-multimodal spaces, how they are shaped by the signers in interaction, and how they can be understood in terms of conceptual blending. The research also focuses on the meta-linguistic skills that signers use in these multilingual-multimodal interactions to “make meaning”

Efficient Numerical Population Density Techniques with an Application in Spinal Cord Modelling

MIIND is a neural simulator which uses an innovative numerical population density technique to simulate the behaviour of multiple interacting populations of neurons under the influence of noise. Recent efforts have produced similar techniques but they are often limited to a single neuron model or type of behaviour. Extensions to these require a great deal of further work and specialist knowledge. The technique used in MIIND overcomes this limitation by being agnostic to the underlying neuron model of each population. However, earlier versions of MIIND still required a high level of technical knowledge to set up the software and involved an often time-consuming manual pre-simulation process. It was also limited to only two-dimensional neuron models. This thesis presents the development of an alternative population density technique, based on that already in MIIND, which reduces the pre-simulation step to an automated process. The new technique is much more flexible and has no limit on the number of time-dependent variables in the underlying neuron model. For the first time, the population density over the state space of the Hodgkin-Huxley neuron model can be observed in an efficient manner on a single PC. The technique allows simulation time to be significantly reduced by gracefully degrading the accuracy without losing important behavioural features. The MIIND software itself has also been simplified, reducing technical barriers to entry, so that it can now be run from a Python script and installed as a Python module. With the improved usability, a model of neural populations in the spinal cord was simulated in MIIND. It showed how afferent signals can be integrated into common reflex circuits to produce observed patterns of muscle activation during an isometric knee extension task. The influence of proprioception in motor control is not fully understood as it can be both task and subject-specific. The results of this study show that afferent signals have a significant effect on sub-maximal muscle contractions even when the limb remains static. Such signals should be considered when developing methods to improve motor control in activities of daily living via therapeutic or mechanical means

A computational dynamical model of human visual cortex for visual search and feature-based attention

Visual attention can be deployed to locations within the visual array (spatial attention), to individual features such as colour and form (feature-based attention), or to entire objects (object-based attention). Objects are composed of features to form a perceived ‘whole’. This compositional object representation reduces the storage demands by avoiding the need to store every type of object experienced. However, this approach exposes a problem of binding these constituent features (e.g. form and colour) into objects. The problem is made explicit in the higher areas of the ventral stream as information about a feature’s location is absent. For feature-based attention and search, activations flow from the inferotemporal cortex to primary visual cortex without spatial cues from the dorsal stream, therefore the neural effect is applied to all locations across the visual field [79, 60, 7, 52]. My research hypothesis is that biased competition occurs independently for each cued feature, and is implemented by lateral inhibition between a feedforward and a feedback network through a cortical micro-circuit architecture. The local competition for each feature can be combined in the dorsal stream via spatial congruence to implement a secondary spatial attention mechanism, and in early visual areas to bind together the distributed featural representation of a target object

Building for Joy in the Digital World

User experience of digital platforms and technologies tends to be quite ‘thin’, characterized by low-quality engagements such as addictive tendencies or browsing on autopilot. In this paper, we take an interdisciplinary approach to identifying more cognitive and active dimensions of ‘thick’ user experience, introducing a new design and User Experience framework which is centred around the notion of joy. We consider existing frameworks which focus on dimensions such as delight, happiness, satisfaction, pleasure, adoption, and retention. We argue that many harmful kinds of engagement either do not impact negatively on these assessments, or even result in higher scores. We explain how we are working at developing measurements that operationalize different dimensions of joy, drawing from a series of case studies of different digital platforms and applications

pMIIND-an MPI-based population density simulation framework

MIIND [1] is the first publicly available implementation of population density algorithms. Like neural mass models, they model at the population level, rather than that of individual neurons, but unlike neural mass models, they consider the full neuronal state space. The central concept is a population density, a probability distribution function that represents the probability of a neuron being in a certain part of state space. Neurons will move through state space by their own intrinsic dynamics or driven by synaptic input. When individual spikes do not matter but only population averaged quantities are considered, these methods outperform direct simulations using neuron point models by a factor 10 or more, whilst (at the population level) producing identical results to simulations of spiking neurons. This is in general not true for neural mass models. Population density methods also relate closely to analytic evaluations of population dynamics. The evolution of the density function is given by a partial differential equation (PDE). In [3] a generic method was presented for solving this equation efficiently, both for small synaptic efficacies (diffusion limit; the PDE becomes a Fokker-Planck equation) and for large ones (finite jumps). We demonstrated that for leaky-integrate-and-fire (LIF) neurons this method reproduces analytic results [1] and uses of the order of 0.2 s to model 1s simulation time of infinitely large population of spiking LIF neurons. We now have developed this method to apply to any 1D neuron point model [3], not just LIF neurons and demonstrated the technique on quadratic-integrate-and-fire neurons. We are therefore in the position to model large heterogeneous networks of spiking neurons very efficiently. A potential bottleneck is MIIND's serial simulation loop. We developed an MPI implementation of MIIND's central simulation loop starting from a fresh code base, and addressed serialization, which is now done at the level of individual cores. Central assumption in the set up is that firing rates are communicated, not individual spikes, so bandwidth requirements are low. Latency is potentially a problem, but with the use of latency hiding techniques good scalability for up to 64 cores has been achieved ondedicated clusters. The scalability was verified with a simple model of cortical waves in a hexagonal network of populations with balanced excitation-inhibition. pMIIND is available on Sourceforge, through its git repository: git://http://miind.sourceforge.net A CMake-based install procedure is provided. Since pMIIND is set up as a C++ framework, it is possible to define one's own algorithms and still take advantage of the MPI-based simulation loop

Draculab: A Python Simulator for Firing Rate Neural Networks With Delayed Adaptive Connections

Draculab is a neural simulator with a particular use scenario: firing rate units with delayed connections, using custom-made unit and synapse models, possibly controlling simulated physical systems. Draculab also has a particular design philosophy. It aims to blur the line between users and developers. Three factors help to achieve this: a simple design using Python's data structures, extensive use of standard libraries, and profusely commented source code. This paper is an introduction to Draculab's architecture and philosophy. After presenting some example networks it explains basic algorithms and data structures that constitute the essence of this approach. The relation with other simulators is discussed, as well as the reasons why connection delays and interaction with simulated physical systems are emphasized

Locating the Embodied Sense of Self and Examining its Relationship with Psychological Well-Being

Westerners tend to localize their sense of self in the head, and, to a lesser degree, in the chest. However, single-point, localization studies of the self omit direct exploration of the size and shape of the embodied self. This study explored a) beliefs about the location and spatial distribution of the embodied sense of self, and b) whether individual differences in how the embodied self was represented were associated with psychological and subjective well-being. Results from a sample of 206 American adults confirm extant reports, indicating that the embodied sense of self is most often located in the head and chest. However, results from this study extend previous findings by suggesting that the majority of respondents (70%) located their embodied sense of self in multiple body regions, and individuals that reported a more widely distributed sense of self reported greater well-being. Specifically, a more widely distributed sense of self in the torso was most strongly associated with psychological well-being. No relationship emerged between the distribution of the sense of self in the head and psychological well-being. Results from this study indicate that the sense of self may be located throughout the body, and that locating the sense of self in the torso may have psychological benefit. As such, exploring methods of shifting the sense of self out of the head and into the body may have therapeutic value