Chadderton, P. | bioRxiv Science

bioRxiv2022

Co-release of histamine and GABA in prefrontal cortex excites fast-spiking interneurons and causes divisive gain change in pyramidal cells; an effect that is enhanced in older mice

We studied how co-release of histamine/GABA from axons originating from the hypothalamic tuberomammillary nucleus (TMN) and projecting to the prefrontal cortex (PFC) influences circuit processing. We opto-stimulated histamine/GABA co-release from genetically defined TMN axons that express the histidine decarboxylase gene (TMNHDC axons). Whole-cell recordings were used to monitor excitability of visually identified PFC neurons in layer 2/3 of prelimbic (PL), anterior cingulate (AC) and infralimbic (IL) regions before and after opto-stimulated histamine/GABA release. We found that histamine-GABA co-release influences the PFC through actions on distinct neuronal types: histamine stimulates fast-spiking interneurons; and co-released GABA enhances tonic (extrasynaptic) inhibition on pyramidal cells (PyrNs). For fast spiking non-accommodating interneurons, opto-stimulation increased excitability, an effect blocked by histamine H1 and H2 receptor antagonists. The excitability of other interneuron types in the PFC was not altered. In contrast, the combined action of histamine and GABA co-release from TMNHDC axons produced predominantly divisive gain changes in PyrNs, increasing their resting input conductance, and decreasing the slope of the input-output relationship. The direct inhibitory effect of TMNHDC axon activation on PyrNs was not blocked by histamine receptor antagonists but was blocked by GABAA receptor antagonists. Across the adult lifespan (from 3 months to over 2 years of age), stimulation of TMNHDC axons in the PFC inhibited PyrN excitability significantly more in older mice. For individuals that maintain cognitive performance into later life, increases in TMNHDC modulation of PyrNs could enhance information processing and be an adaptive mechanism to buttress cognition.

neuroscience↗

bioRxiv2022

Cerebro-cerebellar networks facilitate learning through feedback decoupling

Behavioural feedback is critical for learning in the cerebral cortex. However, such feedback is often not readily available. How the cerebral cortex learns efficiently despite the sparse nature of feedback remains unclear. Inspired by recent deep learning algorithms, we introduce a systems-level computational model of cerebro-cerebellar interactions. In this model a cerebral recurrent network receives feedback predictions from a cerebellar network, thereby decoupling learning in cerebral networks from future feedback. When trained in a simple sensorimotor task the model shows faster learning and reduced dysmetria-like behaviours, in line with the widely observed functional impact of the cerebellum. Next, we demonstrate that these results generalise to more complex motor and cognitive tasks. Finally, the model makes several experimentally testable predictions regarding (1) cerebro-cerebellar task-specific representations over learning, (2) task-specific benefits of cerebellar predictions and (3) the differential impact of cerebellar and inferior olive lesions. Overall, our work offers a theoretical framework of cerebro-cerebellar networks as feedback decoupling machines.

neuroscience↗