Theta Coordinated Error-Driven Learning in the Hippocampus

Abstract

The learning mechanism in the hippocampus has almost universally been assumed to be Hebbian in nature, where individual neurons in an engram join together with synaptic weight increases to support facilitated recall of memories later. However, it is also widely known that Hebbian learning mechanisms impose significant capacity constraints, and are generally less computationally powerful than learning mechanisms that take advantage of error signals. We show that the differential phase relationships of hippocampal subfields within the overall theta rhythm enable a powerful form of error-driven learning, which results in significantly greater capacity, as shown in computer simulations. In one phase of the theta cycle, the bidirectional connectivity between CA1 and entorhinal cortex can be trained in an error-driven fashion to learn to effectively encode the cortical inputs in a compact and sparse form over CA1. In a subsequent portion of the theta cycle, the system attempts to recall an existing memory, via the pathway from entorhinal cortex to CA3 and CA1. Finally the full theta cycle completes when a strong target encoding representation of the current input is imposed onto the CA1 via direct projections from entorhinal cortex. The difference between this target encoding and the attempted recall of the same representation on CA1 constitutes an error signal that can drive the learning of CA3 to CA1 synapses. This CA3 to CA1 pathway is critical for enabling full reinstatement of recalled hippocampal memories out in cortex. Taken together, these new learning dynamics enable a much more robust, high-capacity model of hippocampal learning than was available previously under the classical Hebbian model.

Hippocampal Structure Predicts Statistical Learning and Associative Inference Abilities during Development

“…Prior fMRI work using a similar temporal association task has suggested a role for CA 3 and possibly subiculum in predicting upcoming items (Schapiro et al, 2012). One possible interpretation of the present result is that CA 3 reinstates upcoming items in the sequence via pattern completion (Leutgeb & Leutgeb, 2007; Norman & O’Reilly, 2003) and projects them to the subiculum; however, the role of subiculum in prediction and retrieval remains poorly understood (Ketz, Morkonda, & O’Reilly, 2013). …”

Efficacy of nonselective optogenetic control of the medial septum over hippocampal oscillations: the influence of speed and implications for cognitive enhancement

“…It is therefore possible that entorhinal activity can serve both as an error corrector (Ketz et al. ) and a path integrator (McNaughton et al. b) in the speed/distance calculation circuit during 6‐Hz MS stimulation and influence hippocampal theta oscillations to maintain a linear speed/theta relationship.…”

What Learning Systems do Intelligent Agents Need? Complementary Learning Systems Theory Updated

“…Work within the CLS framework [27,116,141] relies on the anatomical and physiological properties of MTL subregions and the computational insights of others [9,25,26] to characterize the computations performed within these structures.…”

“…Direct connections from ERC to CA3 are also strengthened, allowing the ERC input to directly activate the pattern in CA3 during retrieval without requiring DG involvement (Box 3). Pattern reinstatement in ERC and neocortex [116,141]: The connections from ERC to CA1 and back are thought to change relatively slowly to allow stable correspondence between patterns in CA1 and ERC. Strengthening of connections from the active CA3 neurons to the active CA1 neurons during memory encoding allows this CA1 pattern to be reactivated when the corresponding CA3 pattern is reactivated; the stable connections from CA1 to ERC then allow the appropriate pattern there to be reactivated, and stable connections between ERC and neocortical areas propagate the reactivated ERC pattern to the neocortex.…”