Spiking neural network connectivity and its potential for temporal sensory processing and variable binding

The most biologically-inspired artificial neurons are those of the third generation, and are termed spiking neurons, as individual pulses or spikes are the means by which stimuli are communicated. In essence, a spike is a short-term change in electrical potential and is the basis of communication between biological neurons. Unlike previous generations of artificial neurons, spiking neurons operate in the temporal domain, and exploit time as a resource in their computation. In 1952, Alan Lloyd Hodgkin and Andrew Huxley produced the first model of a spiking neuron; their model describes the complex electro-chemical process that enables spikes to propagate through, and hence be communicated by, spiking neurons. Since this time, improvements in experimental procedures in neurobiology, particularly with in vivo experiments, have provided an increasingly more complex understanding of biological neurons. For example, it is now well-understood that the propagation of spikes between neurons requires neurotransmitter, which is typically of limited supply. When the supply is exhausted neurons become unresponsive. The morphology of neurons, number of receptor sites, amongst many other factors, means that neurons consume the supply of neurotransmitter at different rates. This in turn produces variations over time in the responsiveness of neurons, yielding various computational capabilities. Such improvements in the understanding of the biological neuron have culminated in a wide range of different neuron models, ranging from the computationally efficient to the biologically realistic. These models enable the modeling of neural circuits found in the brain.

Spiking Neural Network Connectivity and its Potential for Temporal Sensory Processing and Variable Binding

The most biologically-inspired artificial neurons are those of the third generation, and are termed spiking neurons, as individual pulses or spikes are the means by which stimuli are communicated. In essence, a spike is a short-term change in electrical potential and is the basis of communication between biological neurons. Unlike previous generations of artificial neurons, spiking neurons operate in the temporal domain, and exploit time as a resource in their computation. In 1952, Alan Lloyd Hodgkin and Andrew Huxley produced the first model of a spiking neuron; their model describes the complex electro-chemical process that enables spikes to propagate through, and hence be communicated by, spiking neurons. Since this time, improvements in experimental procedures in neurobiology, particularly with in vivo experiments, have provided an increasingly more complex understanding of biological neurons. For example, it is now well understood that the propagation of spikes between neurons requires neurotransmitter, which is typically of limited supply. When the supply is exhausted neurons become unresponsive. The morphology of neurons, number of receptor sites, amongst many other factors, means that neurons consume the supply of neurotransmitter at different rates. This in turn produces variations over time in the responsiveness of neurons, yielding various computational capabilities. Such improvements in the understanding of the biological neuron have culminated in a wide range of different neuron models, ranging from the computationally efficient to the biologically realistic. These models enable the modelling of neural circuits found in the brain. In recent years, much of the focus in neuron modelling has moved to the study of the connectivity of spiking neural networks. Spiking neural networks provide a vehicle to understand from a computational perspective, aspects of the brain’s neural circuitry. This understanding can then be used to tackle some of the historically intractable issues with artificial neurons, such as scalability and lack of variable binding. Current knowledge of feed-forward, lateral, and recurrent connectivity of spiking neurons, and the interplay between excitatory and inhibitory neurons is beginning to shed light on these issues, by improved understanding of the temporal processing capabilities and synchronous behaviour of biological neurons. This research topic aims to amalgamate current research aimed at tackling these phenomena.

The Relationship between Materialistic Aspirations and Distinct Aspects of Psychological Well-being in a UK sample

The purpose of this research is to gain a deeper understanding of how materialistic aspirations are related to distinct aspects of psychological well-being. Research has consistently found a negative relationship between materialistic goals and well-being, but a review of the literature identified that the measures of well- being used in the majority of studies were measures of what Keyes (2002) describes as “subjective well-being” or “hedonic happiness”. Criticisms of these types of measures are that they fixate too much on the momentary experience of pleasure and don’t take into account what is meaningful and or what contributes to long lasting fulfilment. Very little research was found investigating the impact of materialism on “eudaimonic” well-being, which is found through doing what is worthwhile and realising ones potential and has been found to have a longer lasting impact on overall well-being (Huta & Ryan, 2010). To address this gap in the literature, a convenience sample of 113 adult subjects in the UK were recruited through Facebook and asked to respond to the Aspiration Index and the Psychological wellbeing scale. The relative importance placed on extrinsic (materialistic) and intrinsic aspirations was compared to the six dimensions of psychological well-being. In line with previous research, higher importance placed on materialistic aspirations for wealth, status and image were found to be negatively correlated with all aspects of psychological well-being. However, the strongest and only statistically significant negative correlation was between extrinsic aspirations and positive relations with others (r = -.256, p< 0.01). Positive relationships with other people form a central component of many theories of well- being and so this negative relationship may help to explain why materialistic aspirations are so consistently found to be negatively correlated to a variety of measures of well-being. Further research is needed to explore this relationship as no causation could be inferred.