Fig 4. Different priors are learnt by the network.

During self-organization, the sequences “ABCD” and “EFGH” were randomly interleaved with frequencies of 67% and 33%, respectively. This is reflected in the relative occurrence of (a) each letter and (b) each word in the spontaneous activity. For different priors during self-organization, this results in the frequencies in (c) for each letter and in (d) for each word. Both show overlearning effects in that their frequencies are biased in favour of the word that was shown more often. The reversing trend and high variance for the extreme priors (0.1 and 0.9) can be accounted for by pathological network dynamics for some simulations with these priors. The letter frequency is the observed frequency in the evoked activity while the word frequency was normalized over the total number of observed words (“ABCD”, “EFGH”, “DCBA”, and “HGFE”) to yield better comparison over different realizations. Error bars represent SEM over 20 independent realizations.