Kenneth Latimer

Google Scholar Profile


  • Kenneth W. Latimer & Adrienne L. Fairhall (2020). Capturing multiple timescales of adaptation to second-order statistics with generalized linear models: gain scaling and fractional differentiation. Frontiers in Systems Neuroscience, 14.   [abstract | link]

    Single neurons can dynamically change the gain of their spiking responses to take into account shifts in stimulus variance. Moreover, gain adaptation can occur across multiple timescales. Here, we examine the ability of a simple statistical model of spike trains, the generalized linear model (GLM), to account for these adaptive effects. The GLM describes spiking as a Poisson process whose rate depends on a linear combination of the stimulus and recent spike history. The GLM successfully replicates gain scaling observed in Hodgkin-Huxley simulations of cortical neurons that occurs when the ratio of spike-generating potassium and sodium conductances approaches one. Gain scaling in the GLM depends on the length and shape of the spike history filter. Additionally, the GLM captures adaptation that occurs over multiple timescales as a fractional derivative of the stimulus envelope, which has been observed in neurons that include long timescale afterhyperpolarization conductances. Fractional differentiation in GLMs requires long spike history that span several seconds. Together, these results demonstrate that the GLM provides a tractable statistical approach for examining single-neuron adaptive computations in response to changes in stimulus variance.

  • Kenneth W. Latimer (2019). Nonlinear demixed component analysis for neural population data as a low-rank kernel regression problem. Neurons, Behavior, Data Analysis, & Theory.   [abstract | link]

    Many studies of neural activity in behaving animals aim to discover interpretable low-dimensional structure in large-scale neural population recordings. One approach to this problem is demixed principal component analysis (dPCA), a supervised linear dimensionality reduction technique to find components that depend on particular experimental parameters. Here, I introduce kernel dPCA (kdPCA) as a nonlinear extension of dPCA by applying kernel least-squares regression to the demixing problem. I consider simulated examples of neural populations with low-dimensional activity to compare the components recovered from dPCA and kdPCA. These simulations demonstrate that neurally relevant nonlinearities, such as stimulus-dependent gain and rotation, interfere with linear demixing of neural activity into components that represent to individual experimental parameters. However, kdPCA can still recover interpretable components in these examples. Finally, I demonstrate kdPCA using two examples of neural populations recorded during perceptual decision-making tasks.

  • Kenneth W. Latimer, Fred Rieke, & Jonathan W. Pillow (2019). Inferring synaptic inputs from spikes with a conductance-based neural encoding model. eLife.   [abstract | link]

    Descriptive statistical models of neural responses generally aim to characterize the mapping from stimuli to spike responses while ignoring biophysical details of the encoding process. Here, we introduce an alternative approach, the conductance-based encoding model (CBEM), which describes amapping from stimuli to excitatory and inhibitory synaptic conductances governing the dynamics of sub-threshold membrane potential. Remarkably, we show that the CBEM can be fit to extracellular spike train data and then used to predict excitatory and inhibitory synaptic currents. We validate these predictions with intracellular recordings from macaque retinal ganglion cells. Moreover, we offer a novel quasi-biophysical interpretation of the Poisson generalized linear model (GLM) as a special case of the CBEM in which excitation and inhibition are perfectly balanced. This work forges a new link between statistical and biophysical models of neural encoding and sheds new light on the biophysical variables that underlie spiking in the early visual pathway.

  • Kenneth W. Latimer*, Dylan Barbera*, Michael Sokoletsky, Bshara Awwad, Yonaton Katz, Israel Nelkin, Ilan Lampl, Adrienne L. Fairhall, & Nicholas J. Priebe (2019). Multiple timescales account for adaptive responses across sensory cortices. J. Neurosci, 39(50):10019-10033.  [abstract | link]

    Sensory systems encounter remarkably diverse stimuli in the external environment. Natural stimuli exhibit timescales and amplitudes of variation that span a wide range. Mechanisms of adaptation, ubiquitous feature of sensory systems, allow for the accommodation of this range of scales. Are there common rules of adaptation across different sensory modalities? We measured the membrane potential responses of individual neurons in the visual, somatosensory and auditory cortices to discrete, punctate stimuli delivered at a wide range of fixed and nonfixed frequencies. We find that the adaptive profile of the response is largely preserved across these three areas, exhibiting attenuation and responses to the cessation of stimulation which are signatures of response to changes in stimulus statistics. We demonstrate that these adaptive responses can emerge from a simple model based on the integration of fixed filters operating over multiple time scales.

  • David Zoltowski, Kenneth W. Latimer, Jacob L. Yates, Alexander C. Huk, & Jonathan W. Pillow (2019). Discrete stepping and nonlinear ramping dynamics underlie spiking responses of LIP neurons during decision-making. Neuron, 102(6):1249-1258.   [abstract | link]

    Neurons in LIP exhibit ramping trial-averaged responses during decision-making. Recent work sparked debate over whether single-trial LIP spike trains are better described by discrete "stepping" or continuous "ramping"dynamics. We extended latent dynamical spike train models and used Bayesian model comparison to address this controversy. First, we incorporated non-Poisson spiking into both models and found that more neurons were better described by stepping than ramping, even when conditioned on evidence or choice. Second, we extended the ramping model to include a non-zero baseline and compressive output nonlinearity. This model accounted for roughly as many neurons as the stepping model. However, latent dynamics inferred under this model exhibited high diffusion variance for many neurons, softening the distinction between continuous and discrete dynamics. Results generalized to additional datasets, demonstrating that substantial fractions of neurons are well described by either stepping or nonlinear ramping, which may be less categorically distinct than the original labels implied.

  • Kenneth W. Latimer, Jacob L. Yates, Miriam L. R. Meister, Alexander C. Huk, & Jonathan W. Pillow (2015). Single-trial spike trains in parietal cortex reveal discrete steps during decision-making. Science, 349(6244):184-187.   [abstract | link]
    Related: Technical Comment by Shadlen and colleagues and Our Reply followed by Response to Reply to Comment by Zylberberg & Shadlen and Our Rebuttal to Response to Reply to Comment

    Neurons in the macaque lateral intraparietal (LIP) area exhibit firing rates that appear to ramp upward or downward during decision-making. These ramps are commonly assumed to reflect the gradual accumulation of evidence toward a decision threshold. However, the ramping in trial-averaged responses could instead arise from instantaneous jumps at different times on different trials. We examined single-trial responses in LIP using statistical methods for fitting and comparing latent dynamical spike-train models. We compared models with latent spike rates governed by either continuous diffusion-to-bound dynamics or discrete “stepping” dynamics. Roughly three-quarters of the choice-selective neurons we recorded were better described by the stepping model. Moreover, the inferred steps carried more information about the animal’s choice than spike counts.

  • Kenneth W. Latimer, Alexander C. Huk, & Jonathan W. Pillow (2015). Bayesian inference for latent stepping and ramping models of spike train data. Chapter in Advanced State Space Methods for Neural and Clinical Data, ed. Zhe Chen, Cambridge University Press.

  • Kenneth W. Latimer, E.J. Chichilnisky, Fred Rieke, & Jonathan W. Pillow (2014). Inferring synaptic conductances from spike trains under a biophysically inspired point process model. Advances in Neural Information Processing Systems, 27:954-962.   [abstract | link | poster]

    A popular approach to neural characterization describes neural responses in terms of a cascade of linear and nonlinear stages: a linear filter to describe stimulus integration, followed by a nonlinear function to convert the filter output to spike rate. However, real neurons respond to stimuli in a manner that depends on the nonlinear integration of excitatory and inhibitory synaptic inputs. Here we introduce a biophysically inspired point process model that explicitly incorporates stimulus-induced changes in synaptic conductance in a dynamical model of neuronal membrane potential. Our work makes two important contributions. First, on a theoretical level, it offers a novel interpretation of the popular generalized linear model (GLM) for neural spike trains. We show that the classic GLM is a special case of our conductance-based model in which the stimulus linearly modulates excitatory and inhibitory conductances in an equal and opposite "push-pull" fashion. Our model can therefore be viewed as a direct extension of the GLM in which we relax these constraints; the resulting model can exhibit shunting as well as hyperpolarizing inhibition, and time-varying changes in both gain and membrane time constant. Second, on a practical level, we show that our model provides a tractable model of spike responses in early sensory neurons that is both more accurate and more interpretable than the GLM. Most importantly, we show that we can accurately infer intracellular synaptic conductances from extracellularly recorded spike trains. We validate these estimates using direct intracellular measurements of excitatory and inhibitory conductances in parasol retinal ganglion cells. The stimulus-dependence of both excitatory and inhibitory conductances can be well described by a linear-nonlinear cascade, with the filter driving inhibition exhibiting opposite sign and a slight delay relative to the filter driving excitation. We show that the model fit to extracellular spike trains can predict excitatory and inhibitory conductances elicited by novel stimuli with nearly the same accuracy as a model trained directly with intracellular conductances.

  • Il Memming Park, Evan Archer, Kenneth W. Latimer, & Jonathan W. Pillow (2013). Universal models for binary spike patterns using centered Dirichlet processes. Advances in Neural Information Processing Systems, 26:2463-2471.   [abstract | link]

    Probabilistic models for binary spike patterns provide a powerful tool for understanding the statistical dependencies in large-scale neural recordings. Maximum entropy (or "maxent") models, which seek to explain dependencies in terms of low-order interactions between neurons, have enjoyed remarkable success in modeling such patterns, particularly for small groups of neurons. However, these models are computationally intractable for large populations, and low-order maxent models have been shown to be inadequate for some datasets. To overcome these limitations, we propose a family of "universal" models for binary spike patterns, where universality refers to the ability to model arbitrary distributions over all 2^m binary patterns. We construct universal models using a Dirichlet process centered on a well-behaved parametric base measure, which naturally combines the flexibility of a histogram and the parsimony of a parametric model. We derive computationally efficient inference methods using Bernoulli and cascaded logistic base measures, which scale tractably to large populations. We also establish a condition for equivalence between the cascaded logistic and the 2nd-order maxent or "Ising" model, making cascaded logistic a reasonable choice for base measure in a universal model. We illustrate the performance of these models using neural data.

  • Benjamin Scholl, Kenneth W. Latimer, & Nicholas J. Priebe (2012). A retinal source of spatial contrast gain control. J. Neurosci, 32(29):9824-9830.   [abstract | link]

    Sensory cortex is able to encode a broad range of stimulus features despite a great variation in signal strength. In cat primary visual cortex (V1), for example, neurons are able to extract stimulus features like orientation or spatial configuration over a wide range of stimulus contrasts. The contrast-invariant spatial tuning found in V1 neuron responses has been modeled as a gain control mechanism, but at which stage of the visual pathway it emerges has remained unclear. Here we describe our findings that contrast-invariant spatial tuning occurs not only in the responses of lateral geniculate nucleus (LGN) relay cells but also in their afferent retinal input. Our evidence suggests that a similar contrast-invariant mechanism is found throughout the stages of the early visual pathway, and that the contrast-invariant spatial selectivity is evident in both retinal ganglion cell and LGN cell responses.


*, † denote equal contribution.