SIMNETS: a computationally efficient and scalable framework for identifying sub-networks of functionally similar neurons

Abstract

AbstractRecent technological advances have made it possible to simultaneously record the activity of thousands of individual neurons in the cortex of awake behaving animals. However, the comparatively slower development of analytical tools capable of handling the scale and complexity of large-scale recordings is a growing problem for the field of neuroscience. We present the Similarity Networks (SIMNETS) algorithm: a computationally efficient and scalable method for identifying and visualizing sub-networks of functionally similar neurons within larger simultaneously recorded ensembles. While traditional approaches tend to group neurons according to the statistical similarities of inter-neuron spike patterns, our approach begins by mathematically capturing the intrinsic relationship between the spike train outputs of each neuron across experimental conditions, before any comparisons are made between neurons. This strategy estimates the intrinsic geometry of each neuron’s output space, allowing us to capture the information processing properties of each neuron in a common format that is easily compared between neurons. Dimensionality reduction tools are then used to map high-dimensional neuron similarity vectors into a low-dimensional space where functional groupings are identified using clustering and statistical techniques. SIMNETS makes minimal assumptions about single neuron encoding properties; is efficient enough to run on consumer-grade hardware (100 neurons < 4s run-time); and has a computational complexity that scales near-linearly with neuron number. These properties make SIMNETS well-suited for examining large networks of neurons during complex behaviors. We validate the ability of our approach for detecting statistically and physiologically meaningful functional groupings in a population of synthetic neurons with known ground-truth, as well three publicly available datasets of ensemble recordings from primate primary visual and motor cortex and the rat hippocampal CA1 region.