Despite intense efforts to understand perception, learning and memory, there are still huge gaps in our knowledge. We have yet to pinpoint the neuronal mechanisms that underlie perception. Millions of neurons in structurally diverse networks activate for us to perceive even the simplest of objects, and teasing apart the neural circuits that are responsible is no small achievement.
Perception and memory are intimately interlinked — perceiving an object would be meaningless without the ability to recall and link it to corresponding memories. Although perception, memory formation and recall are likely to rely on interlinked mechanisms and substrates, we have yet to understand them fully, or to decipher the effects of sleep, attention and other ill-understood processes on learning and memory.
We do know that memory is a spatially and temporally dynamic process. As memories are stored and consolidated, they are shifted from one part of the brain to another in a process that can take weeks and appears to be dependent on brain activity during certain phases of sleep1. Memory-related proteins, synapses, neurons and neural networks are also dynamic. Neurons die off as part of normal ageing, yet for the most part we notice no change. Proteins are continually recycled and replaced, and new proteins are required for learning and memory to occur2. So, how can some memories remain stable when so many of the underlying components are constantly changing?
The study of perception, learning and memory offers many challenges and research opportunities. Our technical toolbox means it is now possible to catalogue and describe the constituents of the brain and its neural circuits — an essential step towards understanding the brain. All that is needed is the time and optimized methods to help handle huge data sets.
The development of high-resolution serial electron microscopy, super-resolution light microscopy3,4 and multicoloured genetic tools for neuronal labelling5, and the increased affordability of immense computing power, make it possible to imagine a day when the connection matrix of a small-to-medium-sized brain (perhaps that of a fly or a mouse) will be known with a reasonable degree of accuracy. This endeavour requires the ability to handle enormous data sets, and a multi-disciplinary approach incorporating molecular biology, genetics, electrophysiology, imaging, electronics, nanotechnology, mathematics, computer science and nonlinear dynamics. It will breed a new type of cooperation between areas of science that have often worked separately.