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.

Various Joyful Encounters with the Dystopias of Affective Capitalism

This article contends that what appear to be the dystopic conditions of affective capitalism are just as likely to be felt in various joyful encounters as they are in atmospheres of fear associated with post 9/11 securitization. Moreover, rather than grasping these joyful encounters with capitalism as an ideological trick working directly on cognitive systems of belief, they are approached here by way of a repressive affective relation a population establishes between politicized sensory environments and what Deleuze and Guattari (1994) call a brain-becoming-subject. This is a radical relationality (Protevi, 2010) understood in this context as a mostly nonconscious brain-somatic process of subjectification occurring in contagious sensory environments populations become politically situated in. The joyful encounter is not therefore merely an ideological manipulation of belief, but following Gabriel Tarde (as developed in Sampson, 2012), belief is always the object of desire. The discussion starts by comparing recent efforts by Facebook to manipulate mass emotional contagion to a Huxleyesque control through appeals to joy. Attention is then turned toward further manifestations of affective capitalism; beginning with the so-called emotional turn in the neurosciences, which has greatly influenced marketing strategies intended to unconsciously influence consumer mood (and choice), and ending with a further comparison between encounters with Nazi joy in the 1930s (Protevi, 2010) and the recent spreading of right wing populism similarly loaded with political affect. Indeed, the dystopian presence of a repressive political affect in all of these examples prompts an initial question concerning what can be done to a brain so that it involuntarily conforms to the joyful encounter. That is to say, what can affect theory say about an apparent brain-somatic vulnerability to affective suggestibility and a tendency toward mass repression? However, the paper goes on to frame a second (and perhaps more significant) question concerning what can a brain do. Through the work of John Protevi (in Hauptmann and Neidich (eds.), 2010: 168-183), Catherine Malabou (2009) and Christian Borch (2005), the article discusses how affect theory can conceive of a brain-somatic relation to sensory environments that might be freed from its coincidence with capitalism. This second question not only leads to a different kind of illusion to that understood as a product of an ideological trick, but also abnegates a model of the brain which limits subjectivity in the making to a phenomenological inner self or Being in the world.