Tim Machado: July 9th

The firing patterns of motor neurons represent the product of neural 
computation in the motor system. EMG recordings are often used as a 
proxy for this activity, given the direct relationship between motor 
neuron firing rate and muscle contraction. However, there are a 
variety of motor neuron subtypes with varied synaptic inputs and 
intrinsic properties, suggesting that this relationship is complex. 
Indeed, studies have shown that different compartments of individual 
muscles are activated asynchronously during some motor tasks—implying 
heterogeneity in firing across single motor pools. To measure the 
activity of many identified motor neurons simultaneously, we have 
combined population calcium imaging at cellular resolution with the 
use of a deconvolution algorithm that infers underlying spiking 
patterns from Ca++ transients. Using this approach we set out to 
examine the firing properties of neurons within an individual pool of 
motor neurons, and in particular, to compare the activity of 
individual neurons belonging to synergist (e.g. flexor-flexor) and 
antagonist (flexor-extensor) pools. 

We imaged motor neurons in the spinal cord of neonatal mice that were 
either loaded with synthetic calcium indicator or expressed GCaMP3. To 
identify the muscle targets of the loaded motor neurons we injected 
two fluorophore conjugated variants of the retrograde tracer cholera 
toxin B into specific antagonist or synergist muscles. To examine the 
correlated firing of motor neurons during network activity in our in 
vitro preparation, a current pulse train was delivered to a sacral 
dorsal root in order to evoke a locomotor-like state. The onset and 
evolution of this rhythmic state was measured with suction electrode 
recordings from multiple ventral roots. To calibrate optical 
measurements, and to determine the upper limit of correlated firing, 
motor neurons were antidromically activated via ventral root 
stimulation. The optical responses to the antidromic train were used 
to directly fit a model to our data that related the fluorescence 
measurements to an approximate spike train. Preliminary observations 
from datasets containing hundreds of identified motor neurons suggests 
heterogeneity in neuronal firing within individual pools, as well as 
alternation in the firing between antagonist pools. In the future, 
this approach will be used to examine the activity patterns of 
molecularly-defined interneuron populations as a function of firing of 
identified motor neurons.

Logan Grosenick : 3pm July 21

Logan Grosenick: Center for Mind, Brain, and Computation & Department of
Bioengineering, Stanford University.

TITLE: Fast classification, regression, and multivariate methods for
sparse but structured data with applications to whole-brain fMRI and
volumetric calcium imaging

ABSTRACT: Modern neuroimaging methods allow the rapid collection of
large (> 100,000 voxel) volumetric time-series. Consequently there has
been a growing interest in applying supervised (classification,
regression) and unsupervised (factor analytic) machine learning
methods to uncover interesting patterns in these rich data.

However, as classically formulated, such approaches are difficult to
interpret when fit to correlated, multivariate data in the presence of
noise. In such cases, these models may suffer from coefficient
instability and sensitivity to outliers, and typically return dense
rather than parsimonious solutions. Furthermore, on large data they
can take an unreasonably long time to compute.

I will discuss ongoing research in the area of sparse but structured
methods for classification, regression, and factor analysis that aim
to produce interpretable solutions and to incorporate realistic
physical priors in the face of large, spatially and temporally
correlated data. Two examples--whole-brain classification of
spatiotemporal fMRI data and nonnegative sparse PCA applied to 3D
calcium imaging--will be presented.