It is widely accepted that human cognition is the product of spiking neurons. Yet even for basic cognitive functions, such as the ability to make decisions or prepare and execute a voluntary movement, the gap between spikes and computation is vast. Only for very simple circuits and reflexes can one explain computations neuron-by-neuron and spike-by-spike. This approach becomes infeasible when neurons are numerous the flow of information is recurrent. To understand computation, one thus requires appropriate abstractions. An increasingly common abstraction is the neural ‘factor’. Factors are central to many explanations in systems neuroscience. Factors provide a framework for describing computational mechanism, and offer a bridge between data and concrete models. Yet there remains some discomfort with this abstraction, and with any attempt to provide mechanistic explanations above that of spikes, neurons, cell-types, and other comfortingly concrete entities. I will explain why, for many networks of spiking neurons, factors are not only a well-defined abstraction, but are critical to understanding computation mechanistically. Indeed, factors are as real as other abstractions we now accept: pressure, temperature, conductance, and even the action potential itself. I use recent empirical results to illustrate how factor-based hypotheses have become essential to the forming and testing of scientific hypotheses. I will also show how embracing factor-level descriptions affords remarkable power when decoding neural activity for neural engineering purposes.

My group pioneered the development of a novel intracortical brain computer interface (iBCI) that decodes muscle activity (EMG) from signals recorded in the motor cortex of animals. We use these synthetic EMG signals to control Functional Electrical Stimulation (FES), which causes the muscles to contract and thereby restores rudimentary voluntary control of the paralyzed limb. In the past few years, there has been much interest in the fact that information from the millions of neurons active during movement can be reduced to a small number of “latent” signals in a low-dimensional manifold computed from the multiple neuron recordings. These signals can be used to provide a stable prediction of the animal’s behavior over many months-long periods, and they may also provide the means to implement methods of transfer learning across individuals, an application that could be of particular importance for paralyzed human users. We have begun to examine the representation within this latent space, of a broad range of behaviors, including well-learned, stereotyped movements in the lab, and more natural movements in the animal’s home cage, meant to better represent a person’s daily activities. We intend to develop an FES-based iBCI that will restore voluntary movement across a broad range of motor tasks without need for intermittent recalibration. However, the nonlinearities and context dependence within this low-dimensional manifold present significant challenges.

Corticospinal neurons (CSN) are the cortical projection neurons that innervate the spinal cord and some brainstem targets with segmental precision to control voluntary movement of specific functional motor groups, limb sections, or individual digits, yet molecular regulation over CSN segmental target specificity is essentially unknown. CSN subpopulations exhibit striking axon targeting specificity from development into maturity: Evolutionarily newer rostrolateral CSN exclusively innervate bulbar-cervical targets (CSNBC-lat), while evolutionarily older caudomedial CSN (CSNmed) are more heterogeneous, with distinct subpopulations extending axons to either bulbar-cervical or thoraco-lumbar segments. The cervical cord, with its evolutionarily enhanced precision of forelimb movement, is innervated by multiple CSN subpopulations, suggesting inter-neuronal interactions in establishing corticospinal connectivity. I identify that Lumican, previously unrecognized in axon development, controls the specificity of cervical spinal cord innervation by CSN. Remarkably, Lumican, an extracellular matrix protein expressed by CSNBC-lat, non-cell-autonomously suppresses axon collateralization in the cervical cord by CSNmed. Intersectional viral labeling and mouse genetics further identify that Lumican controls axon collateralization by multiple subpopulations in caudomedial sensorimotor cortex. These results identify inter-axonal molecular crosstalk between CSN subpopulations as a novel mechanism controlling corticospinal connectivity and competitive specificity. Further, this mechanism has potential implications for evolutionary diversification of corticospinal circuitry with finer scale precision. "" Complementing this work, to comprehensively elucidate related axon projection mechanisms functioning at tips of growing CSN axons in vivo, I am currently applying experimental and analytic approaches recently developed in my postdoc lab (Poulopoulos*, Murphy*, Nature, 2019) to quantitatively and subcellularly “map” RNA and protein molecular machinery of subtype-specific growth cones, in parallel to their parent somata, isolated directly in vivo from developing subcerebral projection neurons (SCPN; the broader cortical output neuron population targeting both brainstem and spinal cord; includes CSN). I am investigating both normal development and GC-soma dysregulation with mutation of central CSN-SCPN transcriptional regulator Ctip2/Bcl11b.