Asymmetry of Neuronal Combinatorial Codes Arises from Minimizing Synaptic Weight Change

Distinct spatial maps and multiple object codes in the lateral entorhinal cortex

The lateral entorhinal cortex (LEC) is a major cortical input area to the hippocampus, and it is crucial for associative object-place-context memories. An unresolved question is whether these associations are performed exclusively in the hippocampus or also upstream of it. Anatomical evidence suggests that the LEC processes both object and spatial information. We describe here a gradient of spatial selectivity along the antero-posterior axis of the LEC. We demonstrate that the LEC generates distinct spatial maps for different contexts that are independent of object coding and vice versa, thus providing evidence for pure spatial and pure object codes upstream of the hippocampus. While space and object coding occur by and large separately in the LEC, we identified neurons that encode for space and objects conjunctively. Together, these findings point to a scenario in which the LEC sustains both distinct space and object coding and associative space-object coding.

Energy efficient synaptic plasticity

Many aspects of the brain’s design can be understood as the result of evolutionary drive toward metabolic efficiency. In addition to the energetic costs of neural computation and transmission, experimental evidence indicates that synaptic plasticity is metabolically demanding as well. As synaptic plasticity is crucial for learning, we examine how these metabolic costs enter in learning. We find that when synaptic plasticity rules are naively implemented, training neural networks requires extremely large amounts of energy when storing many patterns. We propose that this is avoided by precisely balancing labile forms of synaptic plasticity with more stable forms. This algorithm, termed synaptic caching, boosts energy efficiency manifold and can be used with any plasticity rule, including back-propagation. Our results yield a novel interpretation of the multiple forms of neural synaptic plasticity observed experimentally, including synaptic tagging and capture phenomena. Furthermore, our results are relevant for energy efficient neuromorphic designs.

The brain expends a lot of energy. While the organ accounts for only about 2% of a person’s bodyweight, it is responsible for about 20% of our energy use at rest. Neurons use some of this energy to communicate with each other and to process information, but much of the energy is likely used to support learning. A study in fruit flies showed that insects that learned to associate two stimuli and then had their food supply cut off, died 20% earlier than untrained flies. This is thought to be because learning used up the insects’ energy reserves.

If learning a single association requires so much energy, how does the brain manage to store vast amounts of data? Li and van Rossum offer an explanation based on a computer model of neural networks. The advantage of using such a model is that it is possible to control and measure conditions more precisely than in the living brain.

Analysing the model confirmed that learning many new associations requires large amounts of energy. This is particularly true if the memories must be stored with a high degree of accuracy, and if the neural network contains many stored memories already. The reason that learning consumes so much energy is that forming long-term memories requires neurons to produce new proteins. Using the computer model, Li and van Rossum show that neural networks can overcome this limitation by storing memories initially in a transient form that does not require protein synthesis. Doing so reduces energy requirements by as much as 10-fold.

Studies in living brains have shown that transient memories of this type do in fact exist. The current results hence offer a hypothesis as to how the brain can learn in a more energy efficient way. Energy consumption is thought to have placed constraints on brain evolution. It is also often a bottleneck in computers. By revealing how the brain encodes memories energy efficiently, the current findings could thus also inspire new engineering solutions.