Figure - PMC

Skip to main content

An official website of the United States government

Here's how you know

Here's how you know

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

View full-text article in PMC

. 2012 Jan 26;8(1):e1002334. doi: 10.1371/journal.pcbi.1002334

Search in PMC
Search in PubMed
View in NLM Catalog
Add to search

Luz, Shamir.

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.

PMC Copyright notice

The probability density of the synaptic weight, is shown in color code as a function of time. The range of values of , , was divided into one hundred equally sized bins, and the probability of having a value in a corresponding bin of size of 1/100 was estimated numerically. The color scale is shown in terms of . The stochastic learning dynamics of a single inhibitory synapse was simulated using an integrate and fire model (see Methods). The probability density was estimated from the simulations by averaging over 1999 repeats with different realizations for the noise (stochasticity of the presynaptic neurons' firing) and with initial conditions that were uniformly spaced in the interval (0, 1). Here we used , , and .

Inline graphic — The probability density of the synaptic weight, is shown in color code as a function of time. The range of values of , , was divided into one hundred equally sized bins, and the probability of having a value in a corresponding bin of size of 1/100 was estimated numerically. The color scale is shown in terms of . The stochastic learning dynamics of a single inhibitory synapse was simulated using an integrate and fire model (see Methods). The probability density was estimated from the simulations by averaging over 1999 repeats with different realizations for the noise (stochasticity of the presynaptic neurons' firing) and with initial conditions that were uniformly spaced in the interval (0, 1). Here we used , , and .