Functional Microcircuit Recruited during Retrieval of Object Association Memory in Monkey Perirhinal Cortex

Abstract

The primate temporal cortex implements neural mechanisms for memory retrieval from visual long-term storage, and memory neurons have been identified at the single-neuron level whose activities following cue presentation encode the presented object ("cue-holding" neurons) or to-be-recalled target ("pair-recall" neurons). Although these two types of neurons can potentially interact during the target recall, little is known about information flow among these neurons. We conducted simultaneous recordings of multiple single units in macaque perirhinal cortex while they performed a pair-association memory task. Granger causality analysis revealed the emergence of directed couplings during the delay period predominantly from cue-holding neurons to pair-recall neurons. Moreover, these interactions coincided with unidirectional signal flow from the recipient recall neuron to another recall neuron, implying cascade-like signal propagation among the memory cell assembly. These results suggest that directed interactions among perirhinal memory neurons are dynamically modulated to implement functional microcircuitry for retrieval of object association memory.

Interactions between feedback and lateral connections in the primary visual cortex

“…Second, the noise contained in the data can bias the GC estimation. Previous work has shown that GC estimation of continuous time series can be strongly affected by noise, whereas spike trains are less affected (32,34). In addition, our simulations (Figs.…”

“…Fourth, the directional influence revealed by GC analysis is statistical in nature; thus, the observation of a Granger causal influence in the cortex does not necessarily imply the existence of direct anatomical connections between the corresponding neurons. Although GC analysis has proven informative in dissecting neuronal interactions (26,27,32,33), we note that it is difficult to exclude possible influences from hidden variables or unrecorded neurons. Although this problem has motivated several modeling studies (e.g., ref.…”

“…For two simultaneously measured time series, one is called causal to the other if the predictability of the second process at a given time point is improved by including measurements from the immediate past of the first. GC has been shown to be suitable for probing directionality in neuronal interactions for both continuous signals (26)(27)(28)(29) and spike trains (30)(31)(32). However, the pairwise approach to GC analysis may not clearly distinguish causal influences from different sources.…”

“…There are several important methodological issues when interpreting the results obtained by GC analysis of spike-train data. First, the data under analysis are assumed to be stationary, which can usually be reduced with a sliding window approach (32). Second, the noise contained in the data can bias the GC estimation.…”

Associative learning rapidly establishes neuronal representations of upcoming behavioral choices in crows

“…Learned visual associations in monkeys are reflected by association-selective representations in higher visual areas of the temporal cortex (21,22), with top-down activation by the PFC contributing to prospective representations of the choice picture to be recalled (19). Changes to stimulus-selective representations in higher sensory areas have also been reported in anesthetized starlings trained on auditory discriminations (23)(24)(25).…”