Background | Hebb’s rule [45]) or infrequent reward signals [8, 46, 47]. |
Background | This type of plasticity-controlling neuromodulation has been successfully applied when evolving neural networks that solve reinforcement learning problems [25, 46], and a comparison found that evolution was able to solve more complex tasks with neuromodu-lated Hebbian learning than with Hebbian learning alone [25]. |
Discussion | However, even in the non-neuromodulatory (pure Hebbian ) experiments, P&CC is more modular (0.33 [95% Cl: 0.33, 0.33] vs PA 0.26 [0.22, 0.31 ], p = 1.16 x 10—12) and performs significantly better (0.72 [95% Cl: 0.71, 0.72] vs. PA 0.70 [0.69, 0.71], p = 0.003). |
Learning Model | The ai'aj component is a regular Hebbian learning term that is high when the activity of the pre and post-synaptic neurons of a connection are correlated [45]. |
Learning Model | The result is a Hebbian learning rule that is regulated by the inputs from neuromodulatory neurons, allowing the learning rate of specific connections to be increased or decreased in specific circumstances. |
The Importance of Neuromodulation | When we evolve Without neuromodulation, the Hebbian learning dynamics of each connection are constant throughout the lifetime of the organism: this is 0.75 — Normal Learning Forced Forgetting |
The Importance of Neuromodulation | Comparing the performance of networks evolved with and without neuromodulation demonstrates that with purely Hebbian learning (i.e. |
The Importance of Neuromodulation | This finding is in line with previous work demonstrating that neuromodulation allows evolution to solve more compleX reinforcement learning problems than purely Hebbian learning [25]. |
Discussion | Hebbian STDP shaped the lateral structure to improve signal detection performance. |
Excitatory and inhibitory STDP cooperatively shape structured lateral connections | We first introduced Hebbian STDP for both E-to-I and I-to-E connections. |
Excitatory and inhibitory STDP cooperatively shape structured lateral connections | Hebbian inhibitory STDP at lateral connections is not always beneficial for learning. |
Excitatory and inhibitory STDP cooperatively shape structured lateral connections | For eXample, in minor source detection, if we use Hebbian inhibitory STDP, a slightly minor source is not detectable, whereas for anti-Hebbian STDP, a small number of neurons still detect the minor source because reciprocal connections from strong-source responsive inhibitory neurons to strong-source responsive output neurons inhibit synaptic weight development for the stronger source (Fig 6C). |
If we assume WY 2 < > , and gZZ = 0, then the synaptic weight change follows | We have restricted our consideration to Hebbian STDP, but the properties of STDP on E-to-I and I-to-E connections are still debatable [58,59]. |
STDP in E-to-I and I-to-E connections | We showed that in a feedback circuit, Hebbian inhibitory STDP preferred winner-take-all while anti-Hebbian inhibitory STDP tended to cause winner-share-all (see Fukai and Tanaka 1997 for winner-share-all) at eXcitatory neurons (Fig 6D). |
STDP in E-to-I and I-to-E connections | In our model, although inhibitory neurons are not directly projected from input sources, as excitatory neurons learn a specific input source (Fig 5D, left panel), inhibitory neurons acquire feature selectivity through Hebbian STDP at synaptic connections from those excitatory neurons (Fig 5D, middle panel). |
average synaptic weight dynamics satisfy | The first two terms are Hebbian terms that depend on correlation by FX1 and FXZ, Whereas the remainders are homeostatic terms. |
Author Summary | Such basic geometric requirement, which was explicitly recognized in Donald Hebb’s original formulation of synaptic plasticity, is not usually accounted for in neural network learning rules. |
BIG Learning in Small-World Graphs: Ability to Differentiate Real from Spurious Associations | Hebbian models form both associations, relying on later experience to reinforce those that reoccur and eliminating the others [12] , e.g. |
Discussion | Such basic geometric requirement was explicitly recognized in Hebb’s original formulation of synaptic plasticity, yet is not usually accounted for in neural network learning rules. |
Introduction | In order to establish a synapse, according to Hebbian theory, the axon and dendrites of the two co-activated neurons must be juxtaposed [7]. |
Neural Network Model and the BIG ADO Learning Rule | Activity-dependent plasticity is traditionally framed in terms of the Hebbian rule: “When an axon of cell a is near enough to excite cell (9 and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that a’s efficiency, as one of the cells f1ring b, is increased” [7]. |
Neural Network Model and the BIG ADO Learning Rule | Many variants of Hebbian synaptic modification exist [12], often summarized as ‘neurons that fire together wire together’. |
Neural Network Model and the BIG ADO Learning Rule | This popular quip, however, misses the essential requirement, clearly stressed in Hebb’s original formulation, that the axon of the pre-synaptic neuron must be sufficiently close to its post-synaptic target for plasticity to take place. |
Robustness Analysis and Optimal Conditions | This is the key parameter distinguishing BIG ADO from traditional Hebbian learning: a new synapse is formed between two neurons when they fire together and only a potential synapse is already present. |