Episodic Memories: How do the Hippocampus and the Entorhinal Ring Attractors Cooperate to Create Them?

Ring Attractors as the Basis of a Biomimetic Navigation System

The ability to navigate effectively in a rich and complex world is crucial for the survival of all animals. Specialist neural structures have evolved that are implicated in facilitating this ability, one such structure being the ring attractor network. In this study, we model a trio of Spiking Neural Network (SNN) ring attractors as part of a bio-inspired navigation system to maintain an internal estimate of planar translation of an artificial agent. This estimate is dynamically calibrated using a memory recall system of landmark-free allotheic multisensory experiences. We demonstrate that the SNN-based ring attractor system can accurately model motion through 2D space by integrating ideothetic velocity information and use recalled allothetic experiences as a positive corrective mechanism. This SNN based navigation system has potential for use in mobile robotics applications where power supply is limited and external sensory information is intermittent or unreliable.

Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network

Although it may appear infeasible and impractical, building artificial intelligence (AI) using a bottom-up approach based on the understanding of neuroscience is straightforward. The lack of a generalized governing principle for biological neural networks (BNNs) forces us to address this problem by converting piecemeal information on the diverse features of neurons, synapses, and neural circuits into AI. In this review, we described recent attempts to build a biologically plausible neural network by following neuroscientifically similar strategies of neural network optimization or by implanting the outcome of the optimization, such as the properties of single computational units and the characteristics of the network architecture. In addition, we proposed a formalism of the relationship between the set of objectives that neural networks attempt to achieve, and neural network classes categorized by how closely their architectural features resemble those of BNN. This formalism is expected to define the potential roles of top-down and bottom-up approaches for building a biologically plausible neural network and offer a map helping the navigation of the gap between neuroscience and AI engineering.

Fmr1-KO mice failure to detect object novelty associates with a post-test decrease of structural and synaptic plasticity upstream of the hippocampus

Mice with deletion of the FMR1 gene show episodic memory impairments and exhibit dendritic spines and synaptic plasticity defects prevalently identified in non-training conditions. Based on evidence that synaptic changes associated with normal or abnormal memory emerge when mice are cognitively challenged, here we examine whether, and how, fragile entorhinal and hippocampal synapses are remodeled when mice succeed or fail to learn. We trained Fmr1 knockout (KO) and wild-type C57BL/6J (WT) mice in the novel object recognition (NOR) paradigm with 1 h or 24 h training-to-test intervals and then assessed whether varying the time between the presentation of similar and different objects modulates NOR performance and plasticity along the entorhinal cortex-hippocampus axis. At the 1 h-interval, KO mice failed to discriminate the novel object, showed a collapse of spines in the lateral entorhinal cortex (LEC), and of long-term potentiation (LTP) in the lateral perforant path (LPP), but a normal increase in hippocampal spines. At the 24 h, they exhibited intact NOR performance, typical LEC and hippocampal spines, and exaggerated LPP-LTP. Our findings reveal that the inability of mice to detect object novelty primarily stands in their impediment to elaborate, and convey to the hippocampus, sensory/perceptive object representations.

Relevance of a Novel Circuit-Level Model of Episodic Memories to Alzheimer's Disease

The medial temporal lobe memory system has long been identified as the brain region showing the first histopathological changes in early Alzheimer's disease (AD), and the functional decline observed in patients also points to a loss of function in this brain area. Nonetheless, the exact identity of the neurons and networks that undergo deterioration has not been determined so far. A recent study has identified the entorhinal and hippocampal neural circuits responsible for encoding new episodic memories. Using this novel model we describe the elements of the episodic memory network that are especially vulnerable in early AD. We provide a hypothesis of how reduced reelin signaling within such a network can promote AD-related changes. Establishing novel associations and creating a temporal structure for new episodic memories are both affected in AD. Here, we furnish a reasonable explanation for both of these previous observations.