Hebb’s rule [45]) or infrequent reward signals [8, 46, 47].

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].

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).

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].

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.

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

Comparing the performance of networks evolved with and without neuromodulation demonstrates that with purely Hebbian learning (i.e.

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].

Hebbian STDP shaped the lateral structure to improve signal detection performance.

We first introduced Hebbian STDP for both E-to-I and I-to-E connections.

Hebbian inhibitory STDP at lateral connections is not always beneficial for learning.

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).

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].

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).

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).

The first two terms are Hebbian terms that depend on correlation by FX1 and FXZ, Whereas the remainders are homeostatic terms.