Free recall scaling laws and short-term memory effects in a latching attractor network

Abstract

Despite the complexity of human memory, paradigms like free recall have revealed robust qualitative and quantitative characteristics, such as power laws governing recall capacity. Although abstract random matrix models could explain such laws, the possibility of their implementation in large networks of interacting neurons has so far remained unexplored. We study an attractor network model of long-term memory endowed with firing rate adaptation and global inhibition. Under appropriate conditions, the transitioning behaviour of the network from memory to memory is constrained by limit cycles that prevent the network from recalling all memories, with scaling similar to what has been found in experiments. When the model is supplemented with a heteroassociative learning rule, complementing the standard autoassociative learning rule, as well as short-term synaptic facilitation, our model reproduces other key findings in the free recall literature, namely serial position effects, contiguity and forward asymmetry effects, as well as the semantic effects found to guide memory recall. The model is consistent with a broad series of manipulations aimed at gaining a better understanding of the variables that affect recall, such as the role of rehearsal, presentation rates and (continuous/end-of-list) distractor conditions. We predict that recall capacity may be increased with the addition of small amounts of noise, for example in the form of weak random stimuli during recall. Moreover, we predict that although the statistics of the encoded memories has a strong effect on the recall capacity, the power laws governing recall capacity may still be expected to hold.

Path vectors: a neural code for sequential memory

“…An event or item sequence is mapped to a path through a network of cognitive states, whereupon the states’ neural representations are summed into a path vector from which the sequence can later be retrieved. Networks of hidden states have found success in accounting for animal behavior and neural activity (36, 37) and human free recall (6, 38, 39); however, it is unknown how sequences of states are stored (e.g. to repeat back a series of stimuli or actions), possibly retained in a unique compact representation, and retrieved.…”

“…Although a comprehensive picture has not emerged, models for sequence memory abound at many levels. Biophysically, sequences could be stored in synaptic changes (4–6) or graded intracellular states reactivated by oscillations (1, 7). From the viewpoint of distributed coding, models such as the temporal context model or vector symbolic architectures (8, 9) encode sequences by binding high-dimensional “item vectors” to positional or context vectors via e.g.…”