Biology subjects

Liu, Y. H.

Publications and source records attributed to Liu, Y. H..

3 recordsLinked to original sources

bioRxiv2022

Ultrastructure of synaptic connectivity within sub-regions of the SCN revealed by genetically encoded EM tag and SBEM

The suprachiasmatic nucleus (SCN) in the hypothalamus of the vertebrate brain is the central pacemaker regulating circadian rhythmicity throughout the body. The SCN receives photic information through melanopsin-expressing retinal ganglion cells (mRGC) to synchronize the body with environmental light cycles. Determining how these inputs fit into the network of synaptic connections on and between SCN neurons is key to impelling our understanding of the regulation of the circadian clock by light and unraveling the relevant local circuits within the SCN. To map these connections, we used a newly-developed Cre-dependant electron microscopy reporter, APEX2, to label mitochondria of mRGC axons, and serial blockface scanning electron microscopy to resolve the fine structure of mRGC in 3D volumes of the SCN. The maps thus created provide a first draft of the patterns of connectomic organization of SCN in the core and the shell, composed of different neuronal subtypes, and here shown to differ with regard to the patterning of their mRGC input as the shell receives denser mRGCs synaptic inputs compared to the core. This challenges the presently held view that photic information coming directly from the retina is mainly integrated by the core region of the SCN.

neuroscience↗

bioRxiv2020

A solution to temporal credit assignment using cell-type-specific modulatory signals

Animals learn and form memories by jointly adjusting the efficacy of their synapses. How they efficiently solve the underlying temporal credit assignment problem remains elusive. Here, we re-analyze the mathematical basis of gradient descent learning in recurrent spiking neural networks (RSNNs) in light of the recent single-cell transcriptomic evidence for cell-type-specific local neuropeptide signaling in the cortex. Our normative theory posits an important role for the notion of neuronal cell types and local diffusive communication by enabling biologically plausible and efficient weight update. While obeying fundamental biological constraints, including separating excitatory vs inhibitory cell types and observing connection sparsity, we trained RSNNs for temporal credit assignment tasks spanning seconds and observed that the inclusion of local modulatory signaling improved learning efficiency. Our learning rule puts forth a novel form of interaction between modulatory signals and synaptic transmission. Moreover, it suggests a computationally efficient learning method for bio-inspired artificial intelligence.

neuroscience↗

bioRxiv2020

Emergence of prefrontal neuron maturation properties by training recurrent neural networks in cognitive tasks

Working memory and response inhibition are functions that mature relatively late in life, after adolescence, paralleling the maturation of the prefrontal cortex. The link between behavioral and neural maturation is not obvious, however, making it challenging to understand how neural activity underlies the maturation of cognitive function. To gain insights into the nature of observed changes in prefrontal activity between adolescence and adulthood, we investigated the progressive changes in unit activity of Recurrent Neural Networks (RNNs) as they were trained to perform working memory and response inhibition tasks. These included increased delay period activity during working memory tasks, and increased activation in antisaccade tasks. These findings reveal universal properties underlying the neuronal computations behind cognitive tasks and explicate the nature of changes that occur as the result of developmental maturation.

neuroscience↗