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Predicting explorative motor learning using decision-making and motor noise

April 24, 2017

Predicting explorative motor learning using decision-making and motor noise

 

Image credit: William Stitt/ unsplash.com

04/27/2017

research article

When do correlations increase with firing rates in recurrent networks?

A central question in neuroscience is how noisy firing patterns are used to transmit information. Barreiro and Ly study the relationship between pairwise correlations and firing rates in recurrently coupled excitatory-inhibitory spiking networks with conductance-based synapses. The authors find that with stronger excitatory coupling, a positive relationship emerged between pairwise correlations and firing rates.

Image credit: Barreiro, Ly.

When do correlations increase with firing rates in recurrent networks?

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Current Issue April 2017

04/27/2017

research article

Personalized Glucose Forecasting for Type 2 Diabetics Using Data Assimilation

Type 2 diabetes is a devastating disease that requires constant patient self-management of glucose, insulin, nutrition and exercise. Nevertheless, glucose and insulin dynamics are complicated, nonlinear, and individual-dependent, making self-management of diabetes a complex task. To help alleviate some of the difficulty for patients, Albers et al. develop a method for personalized glucose forecasting based on nutrition. 

Image credit: Nervión al día/ Flickr

Personalized Glucose Forecasting for Type 2 Diabetics Using Data Assimilation

04/24/2017

software paper

ESPRIT-Forest: Parallel clustering of massive amplicon sequence data in subquadratic time

It is currently computationally expensive to perform hierarchical clustering of very large sequence datasets due to its quadratic time and space complexities. In this paper Cai et al. develop ESPRIT-Forest, a new algorithm for parallel hierarchical clustering of sequences. The algorithm achieves subquadratic time and space complexity and maintains a high clustering accuracy comparable to the standard method.

Image credit: PLOS

ESPRIT-Forest: Parallel clustering of massive amplicon sequence data in subquadratic time

04/24/2017

research article

Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis

Recent studies use representational models to specify how different perceptions and cognitions are encoded in brain-activity patterns. Diedrichsen and Kriegeskorte provide a general mathematical framework for such models, which clarifies the relationships between the different methods currently used.

Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis

Image credit: Diedrichsen, Kriegeskorte.

04/17/2017

research article

Probabilistic fluorescence-based synapse detection

Synapses are fundamental not only to synaptic network function but also to network development, adaptation, and memory. Despite their obvious importance, mammalian synapse populations have so far resisted detailed quantitative study. Simhal et al. describe new probabilistic image analysis methods suitable for single-synapse analysis of synapse populations in both animal and human brains, in health and disorder.

Probabilistic fluorescence-based synapse detection

Image credit: Simhal et al.

04/26/2017

plos science wednesday

Reddit AMA


Why did we need a March for Science? The organizers of the recent March for Science events in DC and San Francisco discuss the motivations for the events.

Reddit AMA

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