Have you ever wondered how models of resting state fMRI really perform? Then this is the thread.

GitHub

Computational Neuroscience Symposium

This is the annual symposium of NYU's Training Program in Computational Neuroscience, but with a twist: we will have a joint event with the three other training programs funded by the same NIH grant: Brandeis University, Carnegie Mellon University, and University of Washington. There will be five student talks per site. Keynote lectures will be given by Joshua Gordon (NIMH) and Adrienne Fairhall (University of Washington). We hope you will join us!

Monday Jun. 7
12:00pm - 6:00pm

[ link ]-[ registration ]

Follow:@theTuringMachine

My first post on #Medium on solving ill-conditioned system of equations using Multi-precision computations. 🙃

Link

A Gaussian process can be thought of as an extension of the multivariate normal distribution to an infinite number of random variables covering each point on the input domain. The covariance between function values at any two points is given by the evaluation of the kernel of the Gaussian process.

[ link ]

Follow: @theTuringMachine

Here, we bridge these different levels of denoscription by showing how computational models parametrically map classic neuromodulatory processes onto systems-level models of neural activity. The ensuing critical balance of systems-level activity supports perception and action, although our knowledge of this mapping remains incomplete. In this way, quantitative models that link microscale neuronal neuromodulation to systems-level brain function highlight gaps in knowledge and suggest new directions for integrating theoretical and experimental work.

[ link ]
Follow: @theTuringMachine

From the neuron doctrine to neural networks
---
Abstract | For over a century, the neuron doctrine — which states that the neuron is the structural and functional unit of the nervous system — has provided a conceptual foundation for neuroscience. This viewpoint reflects its origins in a time when the use of single-neuron anatomical and physiological techniques was prominent. However, newer multineuronal recording methods have revealed that ensembles of neurons, rather than individual cells, can form physiological units and generate emergent functional properties and states. As a new paradigm for neuroscience, neural network models have the potential to incorporate knowledge acquired with single-neuron approaches to help us understand how emergent functional states generate behaviour, cognition and mental disease.

[ link ]

Follow: @theTuringMachine

We are hiring! Please get in touch if you are looking for an interdisciplinary comp neuro #PhD or #Postdoc. We are looking to fill two positions 1) focusing on dendritic dynamics and synaptic plasticity and 2) on neural network analysis in health and disease.

https://twitter.com/TTchumatchenko/status/1401987678707531783?s=19

NLTools is a Python package for analyzing neuroimaging data. It is the analysis engine powering neuro-learn There are tools to perform data manipulation and analyses such as univariate GLMs, predictive multivariate modeling, and representational similarity analyses. It is based loosely off of Tor Wager’s object-oriented Matlab toolbox and leverages much code from nilearn and scikit-learn

[ link ]

Follow: @theTuringMachine

Listening through the noise
We are all familiar with the difficulty of trying to pay attention to a person speaking in a noisy environment, something often known as the ‘cocktail party problem’. This can be especially…

[ link ]

#spare_time

Follow: @theTuringMachine

Many people ask me what (NMA) was about, how it worked, how we got there, what we learned etc. And every time I try to provide a concise answer, it feels like I’m not doing it justice. Where to start? What to say? How can one summarize what has been the biggest interactive online neuroscience training event in the history of neuroscience in a few sentences?...

[ link ]

Follow: @theTuringMachine

By the end of this tutorial, you will:
• Be able to provide an example of how linear algebra is used in computational neuroscience
• Be able to describe vectors, their properties (dimensionality/length), and their operations (scalar multiplication, vector addition, dot product) geometrically
• Be able to determine and explain the number of basis vectors necessary for a given vector space

[ link ]

#NMA_2021

Follow: @theTuringMachine