Considering sex as a biological variable will require a global shift in science culture
http://feeds.nature.com/~r/neuro/rss/current/~3/zVCGJgqZlKk/s41593-021-00806-8

Nature Neuroscience, Published online: 01 March 2021; doi:10.1038/s41593-021-00806-8 (https://www.nature.com/articles/s41593-021-00806-8)Mandates to include both sexes are a critical step toward improving the translational value of preclinical research, but they will not succeed without intentional, large-scale shifts in scientific incentive structures and publishing standards.

Synaptic plasticity as Bayesian inference
http://feeds.nature.com/~r/neuro/rss/current/~3/T3ULWJgeCiE/s41593-021-00809-5

Nature Neuroscience, Published online: 11 March 2021; doi:10.1038/s41593-021-00809-5 (https://www.nature.com/articles/s41593-021-00809-5)We propose that synapses compute probability distributions over weights, not just point estimates. Using probabilistic inference, we derive a new set of synaptic learning rules and show that they speed up learning in neural networks.

Discovering governing equations from data by sparse identification of nonlinear dynamical systems:

Extracting governing equations from data is a central challenge in many diverse areas of science and engineering. Data are abundant whereas models often remain elusive, as in climate science, neurosci- ence, ecology, finance, and epidemiology, to name only a few examples. In this work, we combine sparsity-promoting techniques and machine learning with nonlinear dynamical systems to discover governing equations from noisy measurement data. The only as- sumption about the structure of the model is that there are only a few important terms that govern the dynamics, so that the equations are sparse in the space of possible functions; this assumption holds for many physical systems in an appropriate basis. In particular, we use sparse regression to determine the fewest terms in the dynamic governing equations required to accurately represent the data. This results in parsimonious models that balance accuracy with model complexity to avoid overfitting. We demonstrate the algorithm on a wide range of problems, from simple canonical systems, including linear and nonlinear oscillators and the chaotic Lorenz system, to the fluid vortex shedding behind an obstacle. The fluid example illustrates the ability of this method to discover the underlying dynamics of a system that took experts in the community nearly 30 years to resolve. We also show that this method generalizes to parameterized systems and systems that are time-varying or have external forcing.

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Virtual Nature Conference on Technologies for Neuroengineering
[ link ]

Frequency-Resolved Functional Connectivity: Role of Delay and the Strength of Connections

The brain functional network extracted from the BOLD signals reveals the correlated activity of the different brain regions, which is hypothesized to underlie the integration of the information across functionally specialized areas. Functional networks are not static and change over time and in different brain states, enabling the nervous system to engage and disengage different local areas in specific tasks on demand. Due to the low temporal resolution, however, BOLD signals do not allow the exploration of spectral properties of the brain dynamics over different frequency bands which are known to be important in cognitive processes. Recent studies using imaging tools with a high temporal resolution has made it possible to explore the correlation between the regions at multiple frequency bands. These studies introduce the frequency as a new dimension over which the functional networks change, enabling brain networks to transmit multiplex of information at any time. In this computational study, we explore the functional connectivity at different frequency ranges and highlight the role of the distance between the nodes in their correlation. We run the generalized Kuramoto model with delayed interactions on top of the brain's connectome and show that how the transmission delay and the strength of the connections, affect the correlation between the pair of nodes over different frequency bands.

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I’m planning to enrol for a Machine Learning course on Coursera by Andrew Ng. If anyone is interested in joining simultaneously to discuss and code and maybe collaborate for a project, please let me know. You can also ping me at <vivek.sharma1510@gmail.com>

Contact : @chaotic_neurons

This website provides one of the most lightweight introductions to #machine_learning I have seen.

If I were to start learning #ML all over again, the structure and concepts covered in this resource would provide a good start.

got from Omarsar0

NEST Desktop
An interactive desktop application for Nest Simulator

Link:
https://nest-desktop.github.io

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