Figure 2.

Schematic diagrams comparing the standard model and the Darwinian model of learning-induced map plasticity. A) Highly specific map plasticity is associated with learning, but is not necessarily maintained. This schematic shows that discrimination of low frequency (blue) tones increases the proportion of neurons that respond to these sounds. Recent studies show that map plasticity usually renormalizes after learning without a decrease in performance^{12, 61-65}. Thus, it is not clear where the memory is stored. B) In the proposed Darwinian model of learning, map plasticity increases the diversity of neural circuits that could accomplish the task. Each symbol represents a neural circuit that responds differently. Although the circuits may be tuned to the same tone frequency, many other stimulus features influence the responses of individual circuits. Map plasticity is a form of replication with variation (neural exploration). If the best circuit could be selected and stabilized, maps could be returned to normal while new skills and memories are maintained. In this schematic, the black circle denotes the new circuit that persists and supports the memory. These circuits involve neurons from many brain regions. C) A schematic diagram in which the amount of information provided by neural circuits that respond to the task stimuli (e.g. the blue low frequency neurons in A and B) is plotted. For a novel task (1), judgments would be based on the average of many circuits (wisdom of crowds). Initial behavioral performance is indicated by the dotted line. With feedback (2), the brain would rapidly select the most effective circuit and improve behavioral performance (black arrow). Map expansion would increase the number of responsive circuits (3) and likely result in the selection of a new, more effective circuit and better behavioral performance (4). If that circuit were stabilized (pink), the rest of the map could return to the initial state (5) in order to support future learning¹¹⁴. If necessary, the process could be repeated. The presence of stabilized circuits would influence the set of diverse response characteristics generated by the next round of map plasticity, which could enhance learning by biasing the exploration of the neural solution space based on past learning (see Figure S2 in the Supplementary material online). In this schematic, circuit effectiveness is represented as the percentage of task information provided by each circuit, where the left edge is zero bits. The pink circuit corresponds to the black circled circuit in B).