103 research outputs found
The equivalence of information-theoretic and likelihood-based methods for neural dimensionality reduction
Stimulus dimensionality-reduction methods in neuroscience seek to identify a
low-dimensional space of stimulus features that affect a neuron's probability
of spiking. One popular method, known as maximally informative dimensions
(MID), uses an information-theoretic quantity known as "single-spike
information" to identify this space. Here we examine MID from a model-based
perspective. We show that MID is a maximum-likelihood estimator for the
parameters of a linear-nonlinear-Poisson (LNP) model, and that the empirical
single-spike information corresponds to the normalized log-likelihood under a
Poisson model. This equivalence implies that MID does not necessarily find
maximally informative stimulus dimensions when spiking is not well described as
Poisson. We provide several examples to illustrate this shortcoming, and derive
a lower bound on the information lost when spiking is Bernoulli in discrete
time bins. To overcome this limitation, we introduce model-based dimensionality
reduction methods for neurons with non-Poisson firing statistics, and show that
they can be framed equivalently in likelihood-based or information-theoretic
terms. Finally, we show how to overcome practical limitations on the number of
stimulus dimensions that MID can estimate by constraining the form of the
non-parametric nonlinearity in an LNP model. We illustrate these methods with
simulations and data from primate visual cortex
The 8th annual computational and systems neuroscience (Cosyne) meeting
1 Department of Neurobiology, Harvard Medical School, Boston, USA -- 2 Departments of Psychology and Neurobiology, Center for Perceptual Systems, The University of Texas at Austin, Austin USAThe 8th annual Computational and Systems Neuroscience meeting (Cosyne) was held February 24-27, 2011 in Salt Lake City, Utah (abstracts are freely available online: http://www.cosyne.org/c/index.php?title=Cosyne2011_Program webcite). Cosyne brings together experimental and theoretical approaches to systems neuroscience, with the goal of understanding neurons, neural assemblies, and the perceptual, cognitive and behavioral functions they mediate.
The range of questions available to systems and computational neuroscience has grown substantially in recent years, with both theoretical and experimental approaches driven by the increasing availability of data about neural circuits and systems. The Cosyne meeting has reflected this growth, nearly doubling in size since the first meeting in 2004, to a new record of nearly 600 attendees this year. It remains single-track, which allows discussions of presentations to drive scientific interaction between attendees with diverse backgrounds. Poster sessions take place each evening, which provide a forum for intense scientific conversations that frequently spill out into more informal settings late at night. The meeting is followed by two days of workshops, held at the Snowbird ski resort, which feature more specialized talks and interactive discussions on a wide collection of topics, this year ranging from consciousness and compressed sensing to dynamics, learning, and [email protected]
Olfactory learning alters navigation strategies and behavioral variability in C. elegans
Animals adjust their behavioral response to sensory input adaptively
depending on past experiences. The flexible brain computation is crucial for
survival and is of great interest in neuroscience. The nematode C. elegans
modulates its navigation behavior depending on the association of odor butanone
with food (appetitive training) or starvation (aversive training), and will
then climb up the butanone gradient or ignore it, respectively. However, the
exact change in navigation strategy in response to learning is still unknown.
Here we study the learned odor navigation in worms by combining precise
experimental measurement and a novel descriptive model of navigation. Our model
consists of two known navigation strategies in worms: biased random walk and
weathervaning. We infer weights on these strategies by applying the model to
worm navigation trajectories and the exact odor concentration it experiences.
Compared to naive worms, appetitive trained worms up-regulate the biased random
walk strategy, and aversive trained worms down-regulate the weathervaning
strategy. The statistical model provides prediction with accuracy of
the past training condition given navigation data, which outperforms the
classical chemotaxis metric. We find that the behavioral variability is altered
by learning, such that worms are less variable after training compared to naive
ones. The model further predicts the learning-dependent response and
variability under optogenetic perturbation of the olfactory neuron
AWC. Lastly, we investigate neural circuits downstream from
AWC that are differentially recruited for learned odor-guided
navigation. Together, we provide a new paradigm to quantify flexible navigation
algorithms and pinpoint the underlying neural substrates
Dimensionality reduction in neural models: an information-theoretic generalization of spike-triggered average and covariance analysis.
We describe an information-theoretic framework for fitting neural spike responses with a Linear-Nonlinear-Poisson cascade model. This framework unifies the spike-triggered average (STA) and spike-triggered covariance (STC) approaches to neural characterization and recovers a set of linear filters that maximize mean and variance-dependent information between stimuli and spike responses. The resulting approach has several useful properties, namely, (1) it recovers a set of linear filters sorted according to their informativeness about the neural response; (2) it is both computationally efficient and robust, allowing recovery of multiple linear filters from a data set of relatively modest size; (3) it provides an explicit ''default'' model of the nonlinear stage mapping the filter responses to spike rate, in the form of a ratio of Gaussians; (4) it is equivalent to maximum likelihood estimation of this default model but also converges to the correct filter estimates whenever the conditions for the consistency of STA or STC analysis are met; an
Correcting motion induced fluorescence artifacts in two-channel neural imaging
Imaging neural activity in a behaving animal presents unique challenges in
part because motion from an animal's movement creates artifacts in fluorescence
intensity time-series that are difficult to distinguish from neural signals of
interest. One approach to mitigating these artifacts is to image two channels;
one that captures an activity-dependent fluorophore, such as GCaMP, and another
that captures an activity-independent fluorophore such as RFP. Because the
activity-independent channel contains the same motion artifacts as the
activity-dependent channel, but no neural signals, the two together can be used
to remove the artifacts. Existing approaches for this correction, such as
taking the ratio of the two channels, do not account for channel independent
noise in the measured fluorescence. Moreover, no systematic comparison has been
made of existing approaches that use two-channel signals. Here, we present
Two-channel Motion Artifact Correction (TMAC), a method which seeks to remove
artifacts by specifying a generative model of the fluorescence of the two
channels as a function of motion artifact, neural activity, and noise. We
further present a novel method for evaluating ground-truth performance of
motion correction algorithms by comparing the decodability of behavior from two
types of neural recordings; a recording that had both an activity-dependent
fluorophore (GCaMP and RFP) and a recording where both fluorophores were
activity-independent (GFP and RFP). A successful motion-correction method
should decode behavior from the first type of recording, but not the second. We
use this metric to systematically compare five methods for removing motion
artifacts from fluorescent time traces. We decode locomotion from a GCaMP
expressing animal 15x more accurately on average than from control when using
TMAC inferred activity and outperform all other methods of motion correction
tested.Comment: 11 pages, 3 figure
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