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.

Motor delays in MDMA (ecstasy) exposed infants persist to 2years

Background Recreational use of 3,4 methylenedioxymethamphetamine (ecstasy, MDMA) is increasing worldwide. Its use by pregnant women causes concern due to potentially harmful effects on the developing fetus. MDMA, an indirect monoaminergic agonist and reuptake inhibitor, affects the serotonin and dopamine systems. Preclinical studies of fetal exposure demonstrate effects on learning, motor behavior, and memory. In the first human studies, we found prenatal MDMA exposure related to poorer motor development in the first year of life. In the present study we assessed the effects of prenatal exposure to MDMA on the trajectory of child development through 2 years of age. We hypothesized that exposure would be associated with poorer mental and motor outcomes. Materials and Methods The DAISY (Drugs and Infancy Study, 2003–2008) employed a prospective longitudinal cohort design to assess recreational drug use during pregnancy and child outcomes in the United Kingdom. Examiners masked to drug exposures followed infants from birth to 4, 12, 18, and 24 months of age. MDMA, cocaine, alcohol, tobacco, cannabis, and other drugs were quantified through a standardized clinical interview. The Bayley Scales (III) of Mental (MDI) and Motor (PDI) Development and the Behavior Rating Scales (BRS) were primary outcome measures. Statistical analyses included a repeated measures mixed model approach controlling for multiple confounders. Results Participants were pregnant women volunteers, primarily white, of middle class socioeconomic status, average IQ, with some college education, in stable partner relationships. Of 96 women enrolled, children of 93 had at least one follow-up assessment and 81 (87%) had ≥ two assessments. Heavier MDMA exposure (M = 1.3 ± 1.4 tablets per week) predicted lower PDI (p < .002), and poorer BRS motor quality from 4 to 24 months of age, but did not affect MDI, orientation, or emotional regulation. Children with heavier exposure were twice as likely to demonstrate poorer motor quality as lighter and non-exposed children (O.R. = 2.2, 95%, CI = 1.02–4.70, p < .05). Discussion Infants whose mothers reported heavier MDMA use during pregnancy had motor delays from 4 months to two years of age that were not attributable to other drug or lifestyle factors. Women of child bearing age should be cautioned about the use of MDMA and MDMA-exposed infants should be screened for motor delays and possible intervention.