Competition on presynaptic resources enhances the discrimination of interfering memories

Evidence suggests that hippocampal adult neurogenesis is critical for discriminating considerably interfering memories. During adult neurogenesis, synaptic competition modifies the weights of synaptic connections nonlocally across neurons, thus providing a different form of unsupervised learning from Hebb’s local plasticity rule. However, how synaptic competition achieves separating similar memories largely remains unknown. Here, we aim to link synaptic competition with such pattern separation. In synaptic competition, adult-born neurons are integrated into the existing neuronal pool by competing with mature neurons for synaptic connections from the entorhinal cortex. We show that synaptic competition and neuronal maturation play distinct roles in separating interfering memory patterns. Furthermore, we demonstrate that a feedforward neural network trained by a competition-based learning rule can outperform a multilayer perceptron trained by the backpropagation algorithm when only a small number of samples are available. Our results unveil the functional implications and potential applications of synaptic competition in neural computation.journal articl

Hippocampal Neurogenesis Reduces the Dimensionality of Sparsely Coded Representations to Enhance Memory Encoding

Adult neurogenesis in the hippocampal dentate gyrus (DG) of mammals is known to contribute to memory encoding in many tasks. The DG also exhibits exceptionally sparse activity compared to other systems, however, whether sparseness and neurogenesis interact during memory encoding remains elusive. We implement a novel learning rule consistent with experimental findings of competition among adult-born neurons in a supervised multilayer feedforward network trained to discriminate between contexts. From this rule, the DG population partitions into neuronal ensembles each of which is biased to represent one of the contexts. This corresponds to a low dimensional representation of the contexts, whereby the fastest dimensionality reduction is achieved in sparse models. We then modify the rule, showing that equivalent representations and performance are achieved when neurons compete for synaptic stability rather than neuronal survival. Our results suggest that competition for stability in sparse models is well-suited to developing ensembles of what may be called memory engram cells

Zebrafish capable of generating future state prediction error show improved active avoidance behavior in virtual reality

Animals make decisions under the principle of reward value maximization and surprise minimization. It is still unclear how these principles are represented in the brain and are reflected in behavior. We addressed this question using a closed-loop virtual reality system to train adult zebrafish for active avoidance. Analysis of the neural activity of the dorsal pallium during training revealed neural ensembles assigning rules to the colors of the surrounding walls. Additionally, one third of fish generated another ensemble that becomes activated only when the real perceived scenery shows discrepancy from the predicted favorable scenery. The fish with the latter ensemble escape more efficiently than the fish with the former ensembles alone, even though both fish have successfully learned to escape, consistent with the hypothesis that the latter ensemble guides zebrafish to take action to minimize this prediction error. Our results suggest that zebrafish can use both principles of goal-directed behavior, but with different behavioral consequences depending on the repertoire of the adopted principles

Orchestrated ensemble activities constitute a hippocampal memory engram

The brain stores and recalls memories through a set of neurons, termed engram cells. However, it is unclear how these cells are organized to constitute a corresponding memory trace. We established a unique imaging system that combines Ca2+ imaging and engram identification to extract the characteristics of engram activity by visualizing and discriminating between engram and non-engram cells. Here, we show that engram cells detected in the hippocampus display higher repetitive activity than non-engram cells during novel context learning. The total activity pattern of the engram cells during learning is stable across post-learning memory processing. Within a single engram population, we detected several sub-ensembles composed of neurons collectively activated during learning. Some sub-ensembles preferentially reappear during post-learning sleep, and these replayed sub-ensembles are more likely to be reactivated during retrieval. These results indicate that sub-ensembles represent distinct pieces of information, which are then orchestrated to constitute an entire memory

Astrocyte GluN2C NMDA receptors control basal synaptic strengths of hippocampal CA1 pyramidal neurons in the stratum radiatum

Experience-dependent plasticity is a key feature of brain synapses for which neuronal N-Methyl-D-Aspartate receptors (NMDARs) play a major role, from developmental circuit refinement to learning and memory. Astrocytes also express NMDARs, although their exact function has remained controversial. Here, we identify in mouse hippocampus, a circuit function for GluN2C NMDAR, a subtype highly expressed in astrocytes, in layer-specific tuning of synaptic strengths in CA1 pyramidal neurons. Interfering with astrocyte NMDAR or GluN2C NMDAR activity reduces the range of presynaptic strength distribution specifically in the stratum radiatum inputs without an appreciable change in the mean presynaptic strength. Mathematical modeling shows that narrowing of the width of presynaptic release probability distribution compromises the expression of long-term synaptic plasticity. Our findings suggest a novel feedback signaling system that uses astrocyte GluN2C NMDARs to adjust basal synaptic weight distribution of Schaffer collateral inputs, which in turn impacts computations performed by the CA1 pyramidal neuron