Comparing representations and computations in single neurons versus neural networks

Mixed recurrent connectivity in primate prefrontal cortex

The functional properties of a network depend on its connectivity, which includes the strength of its inputs and the strength of the connections between its units, or recurrent connectivity. Because we lack a detailed description of the recurrent connectivity in the lateral prefrontal cortex of primates, we developed an indirect method to estimate it. This method leverages the elevated noise correlation of mutually-connected units. To estimate the connectivity of prefrontal regions, we trained recurrent neural network models with varying percentages of bump attractor connectivity and noise levels to match the noise correlation properties observed in two specific prefrontal regions: the dorsolateral prefrontal cortex and the frontal eye field. We found that models initialized with approximately 20% and 7.5% bump attractor connectivity closely matched the noise correlation properties of the frontal eye field and dorsolateral prefrontal cortex, respectively. These findings suggest that the different percentages of bump attractor connectivity may reflect distinct functional roles of these brain regions. Specifically, lower percentages of bump attractor units, associated with higher-dimensional representations, likely support more abstract neural representations in more anterior regions.

Cellular computation and cognition

Contemporary neural network models often overlook a central biological fact about neural processing: that single neurons are themselves complex, semi-autonomous computing systems. Both the information processing and information storage abilities of actual biological neurons vastly exceed the simple weighted sum of synaptic inputs computed by the "units" in standard neural network models. Neurons are eukaryotic cells that store information not only in synapses, but also in their dendritic structure and connectivity, as well as genetic "marking" in the epigenome of each individual cell. Each neuron computes a complex nonlinear function of its inputs, roughly equivalent in processing capacity to an entire 1990s-era neural network model. Furthermore, individual cells provide the biological interface between gene expression, ongoing neural processing, and stored long-term memory traces. Neurons in all organisms have these properties, which are thus relevant to all of neuroscience and cognitive biology. Single-cell computation may also play a particular role in explaining some unusual features of human cognition. The recognition of the centrality of cellular computation to "natural computation" in brains, and of the constraints it imposes upon brain evolution, thus has important implications for the evolution of cognition, and how we study it.

Cortical Organoid-on-a-Chip with Physiological Hypoxia for Investigating Tanshinone IIA-Induced Neural Differentiation

Cortical organoids represent cutting-edge models for mimic human brain development during the early and even middle stage of pregnancy, while they often fail to recreate the complex microenvironmental factors, such as physiological hypoxia. Herein, to recapitulate fetal brain development, we propose a novel cortical organoid-on-a-chip with physiological hypoxia and further explore the effects of tanshinone IIA (Tan IIA) in neural differentiation. The microfluidic chip was designed with a micropillar array for the controlled and efficient generation of cortical organoids. With low oxygen, the generated cortical organoids could recapitulate key aspects of early-gestational human brain development. Compared to organoids in normoxic culturing condition, the promoted neurogenesis, synaptogenesis and neuronal maturation were observed in the present microsystem, suggesting the significance of physiological hypoxia in cortical development. Based on this model, we have found that Chinese herbal drug Tan IIA could promote neural differentiation and maturation, indicating its potential therapeutic effects on neurodevelopmental disorders as well as congenital neuropsychiatric diseases. These results indicate that the proposed biomimetic cortical organoid-on-a-chip model with physiological hypoxia can offer a promising platform to simulate prenatal environment, explore brain development, and screen natural neuroactive components.