Bridging large-scale neuronal recordings and large-scale network models using dimensionality reduction

Abstract

A long-standing goal in neuroscience has been to bring together neuronal recordings and neural network modeling to understand brain function. Neuronal recordings can inform the development of network models, and network models can in turn provide predictions for subsequent experiments. Traditionally, neuronal recordings and network models have been related using single-neuron and pairwise spike train statistics. We review here recent studies that have begun to relate neuronal recordings and network models based on the multi-dimensional structure of neuronal population activity, as identified using dimensionality reduction. This approach has been used to study working memory, decision making, motor control, and more. Dimensionality reduction has provided common ground for incisive comparisons and tight interplay between neuronal recordings and network models.

Sequential and efficient neural-population coding of complex task information

“…Our analysis of neural data is based on conceptualizing the collective activity of neurons as a point in a high-dimensional neural state space, where each coordinate is the activity level of one neuron. It has been observed that in many scenarios, the neural state seemed confined to a lower-dimensional region of the neural state space, termed the "neural manifold" [14][15][16][17][18][19][20][21][22][23][24][25] . We analyzed the geometrical structure of the neural manifold by examining two types of state-space directions, defined by the neural encoding and decoding of the above-mentioned task variables as explained below.…”