# Take away lessons

**Back-propagation**: A prominent algorithm for training ANNs in which a network’s weights are modulated following a gradient of an error function.

**Hebbian learning**: Activity-dependent synaptic plasticity where correlated activation of pre- and postsynaptic neurons leads to the strengthening of the connection between the two neurons.

**Spike Timing Dependent Plasticity**: Spike-tailored Hebbian- based learning in which the relative timing of pre- and postsynaptic spikes are used to modulate synaptic connection strength. With STDP, a positive increase in a synaptic weight occurs when the presynaptic spike precedes the postsynaptic spike (LTP, colored red), and vice versa (LTD, colored blue).

![](https://1487620879-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MiS7NytljlKs-2Yccqw%2F-Mj46oZgWv3yw6edv3tf%2F-Mj496pRN7M0o0f54Skj%2FScreen%20Shot%202021-09-08%20at%2014.26.06.png?alt=media\&token=4f646f98-586d-4c83-ab19-9561e57e6d16)

**Long Term Depression**: Activity-dependent reduction in the efficacy of neuronal synapses.

**Long Term Potentiation**: Activity-dependent increase in the efficacy of neuronal synapses.

**BCM learning**: Hebbian learning-based rule according to which a neuron will undergo LTP if it is in a high-activity state or LTD if it is in a lower-activity state. With the BCM, synaptic modification is characterized by two thresholds separating non-modifying, positive and negative activity lev- els. LTP is colored in red and LTD is colored in blue.

![](https://1487620879-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MiS7NytljlKs-2Yccqw%2F-Mj46oZgWv3yw6edv3tf%2F-Mj48pimjlE5A-uTKFi7%2FScreen%20Shot%202021-09-08%20at%2014.24.44.png?alt=media\&token=1b55d43f-d4e3-47b6-b9cd-43213a504e47)

**Oja’s learning:** Multiplicative normalized Hebbian learning.

