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.

Conspicuous by their abstinence: the limited engagement of heroin users in English and Welsh Drug Recovery Wings

Background In recent years, an abstinence-focused, ‘recovery’ agenda has emerged in UK drug policy, largely in response to the perception that many opioid users had been ‘parked indefinitely’ on Opioid Substitution Therapy (OST). The introduction of ten pilot ‘Drug Recovery Wings’ (DRWs) in 2011 represents the application of this recovery agenda to prisons. This paper describes the DRWs’ operational models, the place of opiate dependent prisoners within them, and the challenges of delivering ‘recovery’ in prison. Methods In 2013, the implementation and operational models of all ten pilot DRWs were rapidly assessed. Up to three days were spent in each DRW, undertaking semi-structured interviews with a sample of 94 DRW staff and 102 DRW residents. Interviews were fully transcribed, and coded using grounded theory. Findings from the nine adult prisons are presented here. Results Four types of DRW were identified, distinguished by their size and selection criteria. Strikingly, no mid- or large-sized units regularly supported OST recipients through detoxification. Type A were large units whose residents were mostly on OST with long criminal records and few social or personal resources. Detoxification was rare, and medication reduction slow. Type B's mid-sized DRW was developed as a psychosocial support service for OST clients seeking detoxification. However, staff struggled to find such prisoners, and detoxification again proved rare. Type C DRWs focused on abstinence from all drugs, including OST. Though OST clients were not intentionally excluded, very few applied to these wings. Only Type D DRWs, offering intensive treatment on very small wings, regularly recruited OST recipients into abstinence-focused interventions. Conclusion Prison units wishing to support OST recipients in making greater progress towards abstinence may need to be small, intensive and take a stepped approach based on preparatory motivational work and extensive preparation for release. However, concerns about post-release deaths will remain.