Effects of Random External Background Stimulation on Network Synaptic Stability After Tetanization: A Modeling Study

We constructed a simulated spiking neural network model to investigate the effects of random background stimulation on the dynamics of network activity patterns and tetanus induced network plasticity. The simulated model was a “leaky integrate-and-fire” (LIF) neural model with spike-timing-dependent plasticity (STDP) and frequency-dependent synaptic depression. Spontaneous and evoked activity patterns were compared with those of living neuronal networks cultured on multielectrode arrays. To help visualize activity patterns and plasticity in our simulated model, we introduced new population measures called Center of Activity (CA) and Center of Weights (CW) to describe the spatio-temporal dynamics of network-wide firing activity and network-wide synaptic strength, respectively. Without random background stimulation, the network synaptic weights were unstable and often drifted after tetanization. In contrast, with random background stimulation, the network synaptic weights remained close to their values immediately after tetanization. The simulation suggests that the effects of tetanization on network synaptic weights were difficult to control because of ongoing synchronized spontaneous bursts of action potentials, or “barrages.” Random background stimulation helped maintain network synaptic stability after tetanization by reducing the number and thus the influence of spontaneous barrages. We used our simulated network to model the interaction between ongoing neural activity, external stimulation and plasticity, and to guide our choice of sensory-motor mappings for adaptive behavior in hybrid neural-robotic systems or “hybrots.

Basic design and simulation of a SPECT microscope for in vivo stem cell imaging

The need to understand the behavior of individual stem cells at the various stages of their differentiation and to assess the resulting reparative action in pre-clinical model systems, which typically involves laboratory animals, provides the motivation for imaging of stem cells in vivo at high resolution. Our initial focus is to image cells and cellular events at single cell resolution in vivo in shallow tissues (few mm of intervening tissue) in laboratory mice and rates. In order to accomplish this goal we are building a SPECT-based microscope. We based our design on earlier theoretical work with near-field coded apertures and have adjusted the components of the system to meet the real-world demands of instrument construction and of animal imaging. Our instrumental design possesses a reasonable trade-off between field-of-view, sensitivity, and contrast performance (photon penetration). A layered gold aperture containing 100 pinholes and intended for use in coded aperture imaging application has been designed and constructed. A silicon detector connected to a TimePix readout from the CERN collaborative group was selected for use in our prototype microscope because of its ultra-high spatial and energy resolution capabilities. The combination of the source, aperture, and detector has been modeled and the coded aperture reconstruction of simulated sources is presented in this work

Shaping Embodied Neural Networks for Adaptive Goal-directed Behavior

The acts of learning and memory are thought to emerge from the modifications of synaptic connections between neurons, as guided by sensory feedback during behavior. However, much is unknown about how such synaptic processes can sculpt and are sculpted by neuronal population dynamics and an interaction with the environment. Here, we embodied a simulated network, inspired by dissociated cortical neuronal cultures, with an artificial animal (an animat) through a sensory-motor loop consisting of structured stimuli, detailed activity metrics incorporating spatial information, and an adaptive training algorithm that takes advantage of spike timing dependent plasticity. By using our design, we demonstrated that the network was capable of learning associations between multiple sensory inputs and motor outputs, and the animat was able to adapt to a new sensory mapping to restore its goal behavior: move toward and stay within a user-defined area. We further showed that successful learning required proper selections of stimuli to encode sensory inputs and a variety of training stimuli with adaptive selection contingent on the animat's behavior. We also found that an individual network had the flexibility to achieve different multi-task goals, and the same goal behavior could be exhibited with different sets of network synaptic strengths. While lacking the characteristic layered structure of in vivo cortical tissue, the biologically inspired simulated networks could tune their activity in behaviorally relevant manners, demonstrating that leaky integrate-and-fire neural networks have an innate ability to process information. This closed-loop hybrid system is a useful tool to study the network properties intermediating synaptic plasticity and behavioral adaptation. The training algorithm provides a stepping stone towards designing future control systems, whether with artificial neural networks or biological animats themselves

Long-Term Activity-Dependent Plasticity of Action Potential Propagation Delay and Amplitude in Cortical Networks

Background: The precise temporal control of neuronal action potentials is essential for regulating many brain functions. From the viewpoint of a neuron, the specific timings of afferent input from the action potentials of its synaptic partners determines whether or not and when that neuron will fire its own action potential. Tuning such input would provide a powerful mechanism to adjust neuron function and in turn, that of the brain. However, axonal plasticity of action potential timing is counter to conventional notions of stable propagation and to the dominant theories of activity-dependent plasticity focusing on synaptic efficacies. Methodology/Principal Findings: Here we show the occurrence of activity-dependent plasticity of action potentia

Basic design and simulation of a SPECT microscope for in vivo stem cell imaging

The need to understand the behavior of individual stem cells at the various stages of their differentiation and to assess the resulting reparative action in pre-clinical model systems, which typically involves laboratory animals, provides the motivation for imaging of stem cells in vivo at high resolution. Our initial focus is to image cells and cellular events at single cell resolution in vivo in shallow tissues (few mm of intervening tissue) in laboratory mice and rates. In order to accomplish this goal we are building a SPECT-based microscope. We based our design on earlier theoretical work with near-field coded apertures and have adjusted the components of the system to meet the real-world demands of instrument construction and of animal imaging. Our instrumental design possesses a reasonable trade-off between field-of-view, sensitivity, and contrast performance (photon penetration). A layered gold aperture containing 100 pinholes and intended for use in coded aperture imaging application has been designed and constructed. A silicon detector connected to a TimePix readout from the CERN collaborative group was selected for use in our prototype microscope because of its ultra-high spatial and energy resolution capabilities. The combination of the source, aperture, and detector has been modeled and the coded aperture reconstruction of simulated sources is presented in this work

Leptospirosis among Returned Travelers: A GeoSentinel Site Survey and Multicenter Analysis-1997-2016.

Leptospirosis is a potentially fatal emerging zoonosis with worldwide distribution and a broad range of clinical presentations and exposure risks. It typically affects vulnerable populations in (sub)tropical countries but is increasingly reported in travelers as well. Diagnostic methods are cumbersome and require further improvement. Here, we describe leptospirosis among travelers presenting to the GeoSentinel Global Surveillance Network. We performed a descriptive analysis of leptospirosis cases reported in GeoSentinel from January 1997 through December 2016. We included 180 travelers with leptospirosis (mostly male; 74%; mostly tourists; 81%). The most frequent region of infection was Southeast Asia (52%); the most common source countries were Thailand (N = 52), Costa Rica (N = 13), Indonesia, and Laos (N = 11 each). Fifty-nine percent were hospitalized; one fatality was reported. We also distributed a supplemental survey to GeoSentinel sites to assess clinical and diagnostic practices. Of 56 GeoSentinel sites, three-quarters responded to the survey. Leptospirosis was reported to have been most frequently considered in febrile travelers with hepatic and renal abnormalities and a history of freshwater exposure. Serology was the most commonly used diagnostic method, although convalescent samples were reported to have been collected infrequently. Within GeoSentinel, leptospirosis was diagnosed mostly among international tourists and caused serious illness. Clinical suspicion and diagnostic workup among surveyed GeoSentinel clinicians were mainly triggered by a classical presentation and exposure history, possibly resulting in underdiagnosis. Suboptimal usage of available diagnostic methods may have resulted in additional missed, or misdiagnosed, cases

Similarity, precedent and argument from analogy

In this paper, it is shown (1) that there are two schemes for argument from analogy that seem to be competitors but are not, (2) how one of them is based on a distinctive type of similarity premise, (3) how to analyze the notion of similarity using story schemes illustrated by some cases, (4) how arguments from precedent are based on arguments from analogy, and in many instances arguments from classification, and (5) that when similarity is defined by means of episode schemes, we can get a clearer idea of how it integrates with the use of argument from classification and argument from precedent in case-based reasoning by using a dialogue structure

Innate Synchronous Oscillations in Freely-Organized Small Neuronal Circuits

BACKGROUND: Information processing in neuronal networks relies on the network's ability to generate temporal patterns of action potentials. Although the nature of neuronal network activity has been intensively investigated in the past several decades at the individual neuron level, the underlying principles of the collective network activity, such as the synchronization and coordination between neurons, are largely unknown. Here we focus on isolated neuronal clusters in culture and address the following simple, yet fundamental questions: What is the minimal number of cells needed to exhibit collective dynamics? What are the internal temporal characteristics of such dynamics and how do the temporal features of network activity alternate upon crossover from minimal networks to large networks? METHODOLOGY/PRINCIPAL FINDINGS: We used network engineering techniques to induce self-organization of cultured networks into neuronal clusters of different sizes. We found that small clusters made of as few as 40 cells already exhibit spontaneous collective events characterized by innate synchronous network oscillations in the range of 25 to 100 Hz. The oscillation frequency of each network appeared to be independent of cluster size. The duration and rate of the network events scale with cluster size but converge to that of large uniform networks. Finally, the investigation of two coupled clusters revealed clear activity propagation with master/slave asymmetry. CONCLUSIONS/SIGNIFICANCE: The nature of the activity patterns observed in small networks, namely the consistent emergence of similar activity across networks of different size and morphology, suggests that neuronal clusters self-regulate their activity to sustain network bursts with internal oscillatory features. We therefore suggest that clusters of as few as tens of cells can serve as a minimal but sufficient functional network, capable of sustaining oscillatory activity. Interestingly, the frequencies of these oscillations are similar those observed in vivo