A Sociology for Other Animals: Analysis, advocacy, intervention

Sociology has come late to the field of Human Animal Studies (HAS), and such scholarship remains peripheral to the discipline. Early sociological interventions in the field were often informed by a critical perspective, in particular feminism but also Marxism and critical race studies. There have also been less critical routes taken, often using approaches such as actor-network theory and symbolic interactionism. These varied initiatives have made important contributions to the project of animalizing sociology and problematizing its legacies of human-exclusivity. As HAS expands and matures however, different kinds of study and different normative orientations have come increasingly into relations of tension in this eclectic field. This is particularly so when it comes to the ideological and ethical debates on appropriate human relations with other species, and on questions of whether and how scholarship might intervene to alter such relations. However, despite questioning contemporary social forms of human-animal relations and suggesting a need for change, the link between analysis and political strategy is uncertain. This paper maps the field of sociological animal studies through some examples of critical and mainstream approaches and considers their relation to advocacy. While those working in critical sociological traditions may appear to have a more certain political agenda, this article suggests that an analysis of 'how things are' does not always lead to a coherent position on 'what is to be done' in terms of social movement agendas or policy intervention. In addition, concepts deployed in advocacy such as rights, liberation and welfare are problematic when applied beyond the human. Even conceptions less entrenched in the liberal humanist tradition such as embodiment, care and vulnerability are difficult to operationalize. Despite complex and contested claims however, this paper suggests that there might also be possibilities for solidarity.

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.