Sensing and processing whisker deflections in rodents

Temporal activity patterns of layer II and IV rat barrel cortex neurons in healthy and injured conditions

Neurons are known to encode information not just by how frequently they fire, but also at what times they fire. However, characterizations of temporal encoding in sensory cortices under conditions of health and injury are limited. Here we characterized and compared the stimulus-evoked activity of 1210 online-sorted units in layers II and IV of rat barrel cortex under healthy and diffuse traumatic brain injury (TBI) (caused by a weight-drop model) conditions across three timepoints post-injury: four days, two weeks, and eight weeks. Temporal activity patterns in the first 50 ms post-stimulus recording showed four categories of responses: no response or 1, 2, or 3 temporally-distinct response components, that is, periods of high unit activity separated by silence. The relative proportions of unit response categories were similar between layers II and IV in healthy conditions but not in early post-TBI conditions. For units with multiple response components, inter-component timings were reliable in healthy and late post-TBI conditions but disrupted by injury. Response component times typically shifted earlier with increasing stimulus intensity and this was more pronounced in layer IV than layer II. Surprisingly, injury caused a reversal of this trend and in the late post-TBI condition no stimulus intensity-dependence differences were observed between layers II and IV. We speculate this indicates a potential compensatory mechanism in response to injury. These results demonstrate how temporal encoding features maladapt or functionally recover differently in sensory cortex after TBI. Such maladaptation or functional recovery is layer-dependent, perhaps due to differences in thalamic input or local inhibitory neuronal makeup.

Classic Hebbian learning endows feed-forward networks with sufficient adaptability in challenging reinforcement learning tasks

A common pitfall of current reinforcement learning agents implemented in computational models is in their inadaptability postoptimization. Najarro and Risi [Najarro E, Risi S. Proc 33rd Conf Neural Inf Process Systems (NeurIPS 2020). 2020: 20719-20731, 2020] demonstrate how such adaptability may be salvaged in artificial feed-forward networks by optimizing coefficients of classic Hebbian rules to dynamically control the networks' weights instead of optimizing the weights directly. Although such models fail to capture many important neurophysiological details, allying the fields of neuroscience and artificial intelligence in this way bears many fruits for both fields, especially when computational models engage with topics with a rich history in neuroscience such as Hebbian plasticity.