Posts about neural

2017-03-29 testing COMPs-fastPcum_scripted

In this notebook, we will study how homeostasis (cooperation) may be an essential ingredient to this algorithm working on a winner-take-all basis (competition). This extension has been published as Perrinet, Neural Computation (2010) (see http://invibe.net/LaurentPerrinet/Publications/Perrinet10shl ). Compared to the previous post, we integrated the faster code to https://github.com/bicv/SHL_scripts.

See also the other posts on unsupervised learning,

This is joint work with Victor Boutin.

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2017-03-29 testing COMPs-fastPcum

In this notebook, we will study how homeostasis (cooperation) may be an essential ingredient to this algorithm working on a winner-take-all basis (competition). This extension has been published as Perrinet, Neural Computation (2010) (see http://invibe.net/LaurentPerrinet/Publications/Perrinet10shl ). Compared to the previous post, we optimize the code to be faster.

See also the other posts on unsupervised learning,

This is joint work with Victor Boutin.

Read more…

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2017-03-29 testing COMPs-Pcum

In this notebook, we will study how homeostasis (cooperation) may be an essential ingredient to this algorithm working on a winner-take-all basis (competition). This extension has been published as Perrinet, Neural Computation (2010) (see http://invibe.net/LaurentPerrinet/Publications/Perrinet10shl ). In particular, we will show how one can build the non-linear functions based on the activity of each filter and which implement homeostasis.

See also the other posts on unsupervised learning,

This is joint work with Victor Boutin.

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2016-07-16 Predictive coding of motion in an aperture

After reading the paper http://www.jneurosci.org/content/34/37/12601.full by Helena X. Wang, Elisha P. Merriam, Jeremy Freeman, and David J. Heeger (The Journal of Neuroscience, 10 September 2014, 34(37): 12601-12615; doi: 10.1523/JNEUROSCI.1034-14.2014), I was interested to test the hypothesis they raise in the discussion section :

The aperture-inward bias in V1–V3 may reflect spatial interactions between visual motion signals along the path of motion (Raemaekers et al., 2009; Schellekens et al., 2013). Neural responses might have been suppressed when the stimulus could be predicted from the responses of neighboring neurons nearer the location of motion origin, a form of predictive coding (Rao and Ballard, 1999; Lee and Mumford, 2003). Under this hypothesis, spatial interactions between neurons depend on both stimulus motion direction and the neuron's relative RF locations, but the neurons themselves need not be direction selective. Perhaps consistent with this hypothesis, psychophysical sensitivity is enhanced at locations further along the path of motion than at motion origin (van Doorn and Koenderink, 1984; Verghese et al., 1999).

Concerning the origins of aperture-inward bias, I want to test an alternative possibility. In some recent modeling work:

Laurent Perrinet, Guillaume S. Masson. Motion-based prediction is sufficient to solve the aperture problem. Neural Computation, 24(10):2726--50, 2012 http://invibe.net/LaurentPerrinet/Publications/Perrinet12pred

I was surprised to observe a similar behavior: the trailing edge was exhibiting a stronger activation (i. e. higher precision revealed by a lower variance in this probabilistic model) while I would have thought intuitively the leading edge would be more informative. In retrospect, it made sense in a motion-based prediction algorithm as information from the leading edge may propagate in more directions (135° for a 45° bar) than in the trailing edge (45°, that is a factor of 3 here). While we made this prediction we did not have any evidence for it.

In this script the predictive coding is done using the MotionParticles package and for a http://motionclouds.invibe.net/ within a disk aperture.

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2016-06-25 compiling notebooks into a report

For a master's project in computational neuroscience, we adopted a quite novel workflow to go all the steps from the learning of the small steps to the wrtiting of the final thesis. Though we were flexible in our method during the 6 months of this work, a simple workflow emerged that I describe here.

Compiling a set of notebook to a LaTeX document.

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2016-06-01 Compiling and using pyNN + NEST + python3

PyNN is a neural simulation language which works well with the NEST simulator. Here I show my progress in using both with python 3 and how to show results in a notebook.

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2016-02-19 Compiling and using pyNN + NEST + python3

PyNN is a neural simulation language which works well with the NEST simulator. Here I show my progress in using both with python 3 and how to show results in a notebook.

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