Jan Homann

Fast, scalable, Bayesian spike identification for multi-electrode arrays.

We present an algorithm to identify individual neural spikes observed on high-density multi-electrode arrays (MEAs). Our method can distinguish large numbers of distinct neural units, even when spikes overlap, and accounts for intrinsic variability of spikes from each unit. As MEAs grow larger, it is important to find spike-identification methods that are scalable, that is, the computational cost of spike fitting should scale well with the number of units observed. Our algorithm accomplishes this goal, and is fast, because it exploits the spatial locality of each unit and the basic biophysics of extracellular signal propagation. Human interaction plays a key role in our method; but effort is minimized and streamlined via a graphical interface. We illustrate our method on data from guinea pig retinal ganglion cells and document its performance on simulated data consisting of spikes added to experimentally measured background noise. We present several tests demonstrating that the algorithm is highly accurate: it exhibits low error rates on fits to synthetic data, low refractory violation rates, good receptive field coverage, and consistency across users.

Transformation of Stimulus Correlations by the Retina

Redundancies and correlations in the responses of sensory neurons may seem to waste neural resources, but they can also carry cues about structured stimuli and may help the brain to correct for response errors. To investigate the effect of stimulus structure on redundancy in retina, we measured simultaneous responses from populations of retinal ganglion cells presented with natural and artificial stimuli that varied greatly in correlation structure; these stimuli and recordings are publicly available online. Responding to spatio-temporally structured stimuli such as natural movies, pairs of ganglion cells were modestly more correlated than in response to white noise checkerboards, but they were much less correlated than predicted by a non-adapting functional model of retinal response. Meanwhile, responding to stimuli with purely spatial correlations, pairs of ganglion cells showed increased correlations consistent with a static, non-adapting receptive field and nonlinearity. We found that in response to spatio-temporally correlated stimuli, ganglion cells had faster temporal kernels and tended to have stronger surrounds. These properties of individual cells, along with gain changes that opposed changes in effective contrast at the ganglion cell input, largely explained the pattern of pairwise correlations across stimuli where receptive field measurements were possible.

Scalable, Bayesian, multi-electrode spike sorting

Multi-electrode array technology provides an efficient means of recording from many neurons. However, as arrays become larger, a greater computational burden falls on the spike-sorting algorithm. We have developed a new method, that scales linearly with array size, for sorting multi-electrode signals from retinal ganglion cells. We believe that our techniques represent progress toward solving many of the source separation problems that will become ubiquitous as large multi-electrode arrays become commonplace. The broad outline of our method is to identify spikes in the raw data, cluster a subset, generate template waveforms, then fit the templates to all the data using an iterative Bayesian algorithm. Spikes are identified as spatiotemporally connected patches of threshold-crossing voltage samples. The spike waveform is taken from a fixed neighborhood centered on the electrode having the peak voltage within each patch. This approach allows for segmentation of simultaneous, yet spatially separated, events and prevents the waveform dimensionality from increasing with array size. Next we cluster a small subset of spikes. We use an existing algorithm, OPTICS, which orders the waveforms so that similar spikes are placed together. This linear ordering makes cluster boundaries easily distinguishable by the user. We have built a GUI in which the manual cluster cutting can be performed efficiently. For each cluster, we align the waveforms and take the median to get templates. The primary obstacle in fitting templates to the data is the presence of overlapping spikes, which distort the observed waveforms. One established approach to the problem is to simply fit single templates, then subtract the best fit and iterate. However, the amplitude of the observed spike can differ substantially from the template, producing errors upon subtraction. We avoid this problem by allowing the amplitude of the template to vary; this is most naturally incorporated into a Bayesian framework. We model each waveform as a linear superposition of templates with Gaussian-distributed amplitudes, plus correlated Gaussian noise. We then seek the most probable template, spike time, and amplitude given the data. The spatial localization of spikes narrows down the list of candidate templates, speeding up the algorithm. The Gaussian amplitude prior allows the amplitudes to be marginalized analytically, avoiding an explicit sum. We have tested the method on many data sets recorded with a dense 30-electrode array, under a variety of stimulus conditions. It always produces very low error rates. Tests with larger arrays, different species, and synthetic data are ongoing.