Stability and Competition in Multi-spike Models of Spike-Timing Dependent Plasticity

Spike-timing dependent plasticity (STDP) is a widespread plasticity mechanism in the nervous system. The simplest description of STDP only takes into account pairs of pre- and postsynaptic spikes, with potentiation of the synapse when a presynaptic spike precedes a postsynaptic spike and depression otherwise. In light of experiments that explored a variety of spike patterns, the pair-based STDP model has been augmented to account for multiple pre- and postsynaptic spike interactions. As a result, a number of different “multi-spike” STDP models have been proposed based on different experimental observations. The behavior of these models at the population level is crucial for understanding mechanisms of learning and memory. The challenging balance between the stability of a population of synapses and their competitive modification is well studied for pair-based models, but it has not yet been fully analyzed for multi-spike models. Here, we address this issue through numerical simulations of an integrate-and-fire model neuron with excitatory synapses subject to STDP described by three different proposed multi-spike models. We also analytically calculate average synaptic changes and fluctuations about these averages. Our results indicate that the different multi-spike models behave quite differently at the population level. Although each model can produce synaptic competition in certain parameter regions, none of them induces synaptic competition with its originally fitted parameters. The dichotomy between synaptic stability and Hebbian competition, which is well characterized for pair-based STDP models, persists in multi-spike models. However, anti-Hebbian competition can coexist with synaptic stability in some models. We propose that the collective behavior of synaptic plasticity models at the population level should be used as an additional guideline in applying phenomenological models based on observations of single synapses

Pairwise Analysis Can Account for Network Structures Arising from Spike-Timing Dependent Plasticity

Spike timing-dependent plasticity (STDP) modifies synaptic strengths based on timing information available locally at each synapse. Despite this, it induces global structures within a recurrently connected network. We study such structures both through simulations and by analyzing the effects of STDP on pair-wise interactions of neurons. We show how conventional STDP acts as a loop-eliminating mechanism and organizes neurons into in- and out-hubs. Loop-elimination increases when depression dominates and turns into loop-generation when potentiation dominates. STDP with a shifted temporal window such that coincident spikes cause depression enhances recurrent connections and functions as a strict buffering mechanism that maintains a roughly constant average firing rate. STDP with the opposite temporal shift functions as a loop eliminator at low rates and as a potent loop generator at higher rates. In general, studying pairwise interactions of neurons provides important insights about the structures that STDP can produce in large networks

Network Model of Spontaneous Activity Exhibiting Synchronous Transitions Between Up and Down States

Both in vivo and in vitro recordings indicate that neuronal membrane potentials can make spontaneous transitions between distinct up and down states. At the network level, populations of neurons have been observed to make these transitions synchronously. Although synaptic activity and intrinsic neuron properties play an important role, the precise nature of the processes responsible for these phenomena is not known. Using a computational model, we explore the interplay between intrinsic neuronal properties and synaptic fluctuations. Model neurons of the integrate-and-fire type were extended by adding a nonlinear membrane current. Networks of these neurons exhibit large amplitude synchronous spontaneous fluctuations that make the neurons jump between up and down states, thereby producing bimodal membrane potential distributions. The effect of sensory stimulation on network responses depends on whether the stimulus is applied during an up state or deeply inside a down state. External noise can be varied to modulate the network continuously between two extreme regimes in which it remains permanently in either the up or the down state

Avionics for a Small Satellite

This paper discusses a small. seven and a half (7.5) inch diameter. satellite that NASA-JSC is developing as a technology demonstrator for an astronaut assistant free flyer. The Free Flyer is designed to off load flight crew work load by performing inspections of the exterior of Space Shuttle or International Space Station. The Free Flyer is designed to be operated by the flight crew thereby reducing the number of Extra Vehicle Activities (EVA) or by an astronaut on the ground further reducing crew work load. The paper focuses on the design constraint of a small satellite and the technology approach used to achieve the set of high performance requirements specified for the Free Flyer. Particular attention is paid to the processor card as it is the heart and system integration point of the Free Flyer

Invited talk: The role of spontaneous activity in sensory processing

Spontaneous, background activity in sensory areas is often similar in both magnitude and form to evoked responses. Embedding responses evoked by sensory stimuli in such strong and complex background activity seems like a confusing way to represent information about the outside world. However, modeling studies indicate that, contrary to intuition, information about sensory stimuli may be better conveyed by a network displaying chaotic background activity than in a network without spontaneous activity

Avionics for a Small Robotic Inspection Spacecraft

A report describes the tentative design of the avionics of the Mini-AERCam -- a proposed 7.5-in. (approximately 19-cm)-diameter spacecraft that would contain three digital video cameras to be used in visual inspection of the exterior of a larger spacecraft (a space shuttle or the International Space Station). The Mini-AERCam would maneuver by use of its own miniature thrusters under radio control by astronauts inside the larger spacecraft. The design of the Mini-AERCam avionics is subject to a number of constraints, most of which can be summarized as severely competing requirements to maximize radiation hardness and maneuvering, image-acquisition, and data-communication capabilities while minimizing cost, size, and power consumption. The report discusses the design constraints, the engineering approach to satisfying the constraints, and the resulting iterations of the design. The report places special emphasis on the design of a flight computer that would (1) acquire position and orientation data from a Global Positioning System receiver and a microelectromechanical gyroscope, respectively; (2) perform all flight-control (including thruster-control) computations in real time; and (3) control video, tracking, power, and illumination systems

A Complex-Valued Firing-Rate Model That Approximates the Dynamics of Spiking Networks

Firing-rate models provide an attractive approach for studying large neural networks because they can be simulated rapidly and are amenable to mathematical analysis. Traditional firing-rate models assume a simple form in which the dynamics are governed by a single time constant. These models fail to replicate certain dynamic features of populations of spiking neurons, especially those involving synchronization. We present a complex-valued firing-rate model derived from an eigenfunction expansion of the Fokker-Planck equation and apply it to the linear, quadratic and exponential integrate-and-fire models. Despite being almost as simple as a traditional firing-rate description, this model can reproduce firing-rate dynamics due to partial synchronization of the action potentials in a spiking model, and it successfully predicts the transition to spike synchronization in networks of coupled excitatory and inhibitory neurons

Adapting PC104Plus for Space

This article addresses the issues associated with adapting the commercial PC104Plus standard and its associated architecture to the requirements of space applications. In general, space applications exhibit extreme constraints on power, weight, and volume. EMI and EMC are also issues of significant concern. Additionally, space applications have to survive high radiation environment. Finally, NASA is always concerned about achieving cost effective solutions that are compatible with safety and launch constraints. Weight and volume constraints are directly related to high launch cost. Power on the other hand is not only related to the high launch costs, but are related to the problem of dissipating the resulting heat once in space. The article addresses why PC104Plus is an appropriate solution for the weight and volume issues. The article also addresses what NASA did electrically to reduce power consumption and mechanically dissipate the associated heat in a microgravity and vacuum environment, and how these solutions allow NASA to integrate various sizes of ruggedized custom PC104 boards with COTS, PC104 complaint boards for space applications. In addition to the mechanical changes to deal with thermal dissipation NASA also made changes to minimize EMI. Finally, radiation issues are addressed as well as the architectural and testing solutions and the implications for use of COTS PC104Plus boards

Meta-learning Hebbian plasticity for continual familiarity detection

Memories are stored and recalled throughout the lifetime of an animal, but many models of memory, including previous models of familiarity detection, do not operate in a continuous manner. We consider a family of models that recognize previously experienced stimuli and, importantly, operate and learn continuously. Specifically, we investigate a learning paradigm in which stimuli are presented in a streaming fashion with repetitions at various intervals, and the subject/model must report whether the current stimulus has previously appeared in the stream. We propose a feedforward network architecture with ongoing plasticity in the synaptic weight matrix. Parameters governing plasticity and static network parameters are meta-learned using gradient descent to optimize the continual familiarity detection process. This architecture, unlike recurrent networks without ongoing plasticity, generalizes easily over a range of repeat intervals even if trained with a single interval. We show that an anti-Hebbian plasticity rule (co-activated neurons cause synaptic depression) enables repeat detection over much longer intervals than a Hebbian one, and this is the solution most readily found by meta-learning. This rule leads to experimentally observed features such as repeat suppression in the hidden layer neurons. In contrast to previous theoretical work, the capacity of these networks remains constant across their lifetimes, meaning that pairs of stimuli with a given temporal separation are stored and recognized as familiar independent of the network's input history. We also consider learning rules that use an external gating circuit to control plasticity. Collectively, these models demonstrate a range of different psychometric curves that we compare to human performance. Keywords: learning, memory, recognition, familiarity, novelty detection, meta-learning, Hebbian, synaptic plasticit

Airborne Dust Cloud Measurements at the INL National Security Test Range

On July 11, 2007, a surface, high-explosive test (<20,000 lb TNT-equivalent) was carried out at the National Security Test Range (NSTR) on the Idaho National Laboratory (INL) Site. Aircraft-mounted rapid response (1-sec) particulate monitors were used to measure airborne PM-10 concentrations directly in the dust cloud and to develop a PM-10 emission factor that could be used for subsequent tests at the NSTR. The blast produced a mushroom-like dust cloud that rose approximately 2,500–3,000 ft above ground level, which quickly dissipated (within 5 miles of the source). In general, the cloud was smaller and less persistence than expected, or that might occur in other areas, likely due to the coarse sand and subsurface conditions that characterize the immediate NSTR area. Maximum short time-averaged (1-sec) PM-10 concentrations at the center of the cloud immediately after the event reached 421 µg m-3 but were rapidly reduced (by atmospheric dispersion and fallout) to near background levels (~10 µg m-3) after about 15 minutes. This occurred well within the INL Site boundary, about 8 km (5 miles) from the NSTR source. These findings demonstrate that maximum concentrations in ambient air beyond the INL Site boundary (closest is 11.2 km from NSTR) from these types of tests would be well within the 150 µg m-3 24-hour National Ambient Air Quality Standards for PM-10. Aircraft measurements and geostatistical techniques were used to successfully quantify the initial volume (1.64E+9 m3 or 1.64 km3) and mass (250 kg) of the PM-10 dust cloud, and a PM-10 emission factor (20 kg m-3 crater soil volume) was developed for this specific type of event at NSTR. The 250 kg of PM-10 mass estimated from this experiment is almost seven-times higher than the 36 kg estimated for the environmental assessment (DOE-ID 2007) using available Environmental Protection Agency (EPA 1995) emission factors. This experiment demonstrated that advanced aircraft-mounted instruments operated by experienced atmospheric research groups, such as the INL and Airborne Research Consultants LLC team, can safely and effectively assess difficult air pollutant questions at the INL Site and elsewhere that cannot be otherwise answered. This site-specific, measurement-based assessment provides valuable input to stakeholders in judging the risks associated with these types of events and NSTR project staff in the development of future experimental design and environmental impact assessments