Modular Deconstruction Reveals the Dynamical and Physical Building Blocks of a Locomotion Motor Program

Abstract

The neural substrates of motor programs are only well understood for small, dedicated circuits. Here we investigate how a motor program is constructed within a large network. We imaged populations of neurons in the Aplysia pedal ganglion during execution of a locomotion motor program. We found that the program was built from a very small number of dynamical building blocks, including both neural ensembles and low-dimensional rotational dynamics. These map onto physically discrete regions of the ganglion, so that the motor program has a corresponding modular organization in both dynamical and physical space. Using this dynamic map, we identify the population potentially implementing the rhythmic pattern generator and find that its activity physically traces a looped trajectory, recapitulating its low-dimensional rotational dynamics. Our results suggest that, even in simple invertebrates, neural motor programs are implemented by large, distributed networks containing multiple dynamical systems.

Lognormal firing rate distribution reveals prominent fluctuation–driven regime in spinal motor networks

“…The $V_m$ of individual neurons is often normally distributed in cortical neurons when considering either the up– or down–state (Destexhe et al, 2003; Stern et al, 1997) and the spiking is irregular with a $CV$ clustered around 1 (Softky and Koch, 1993; Stevens and Zador, 1998). Similar irregularity is observed in invertebrates (Bruno et al, 2015). The distribution of mean $C{V}_2$ values in our experiments was clustered around 0.6 when ignoring the inter–burst intervals (Figure 10C).…”

“…The fluctuation–driven neuron spent most of the time below threshold (Figure 6A) and had more irregular spiking as quantified by a local measure of irregularity, the $C{V}_2$ (green line). $C{V}_2$ is the difference of two adjacent ISIs divided by their mean (Holt et al, 1996; Bruno et al, 2015). In contrast, the mean–driven neuron spent most time above threshold and had more regular spiking, i.e.…”

The modular and integrative functional architecture of the human brain

“…A modular structural wiring network (i.e., white matter tracts), with dense connectivity within modules and weak connectivity between modules, has also been consistently found in human brain imaging data (5,6,13,14). Finally, the brain's functional architecturehow the brain's modules interact to produce cognition-appears modular (7,(15)(16)(17).…”

Neural Manifolds for the Control of Movement

“…The concept of the neural manifold and its associated latent variables has been used in a series of recent studies that replace the search for movement representation by single neurons to consider instead movement planning and execution based on the activation of a few neural modes (Ahrens et al, 2012; Bruno et al, 2015; Churchland et al, 2012, 2010a, 2010b; Churchland and Shenoy, 2007; Elsayed et al, 2016; Kaufman et al, 2016, 2014; Michaels et al, 2016; Overduin et al, 2015; Sadtler et al, 2014; Santhanam et al, 2009; Sussillo et al, 2015). …”

“…A number of studies have shown that the largely heterogeneous activity patterns of individual neurons in monkey (Kobak et al, 2016; Machens et al, 2010; Mante et al, 2013; Markowitz et al, 2015) and rat (Durstewitz et al, 2010) prefrontal cortex, monkey (Churchland et al, 2010b) and rat (Forsberg et al, 2016) V1, rat olfactory cortex (Kobak et al, 2016), rat thalamus (Chapin and Nicolelis, 1999), rat parietal cortex (Raposo et al, 2014), locust olfactory system (Stopfer et al, 2003), aplysia pedal ganglion (Bruno et al, 2015), and perhaps the entire zebrafish brain (Ahrens et al, 2012) can be explained as generated by a small set of latent variables associated with neural modes. In all these studies, neural modes and their time-varying activation helped describe previously unexplained mechanisms of neural function.…”