1,296 research outputs found
State Dependence of Stimulus-Induced Variability Tuning in Macaque MT
Behavioral states marked by varying levels of arousal and attention modulate
some properties of cortical responses (e.g. average firing rates or pairwise
correlations), yet it is not fully understood what drives these response
changes and how they might affect downstream stimulus decoding. Here we show
that changes in state modulate the tuning of response variance-to-mean ratios
(Fano factors) in a fashion that is neither predicted by a Poisson spiking
model nor changes in the mean firing rate, with a substantial effect on
stimulus discriminability. We recorded motion-sensitive neurons in middle
temporal cortex (MT) in two states: alert fixation and light, opioid
anesthesia. Anesthesia tended to lower average spike counts, without decreasing
trial-to-trial variability compared to the alert state. Under anesthesia,
within-trial fluctuations in excitability were correlated over longer time
scales compared to the alert state, creating supra-Poisson Fano factors. In
contrast, alert-state MT neurons have higher mean firing rates and largely
sub-Poisson variability that is stimulus-dependent and cannot be explained by
firing rate differences alone. The absence of such stimulus-induced variability
tuning in the anesthetized state suggests different sources of variability
between states. A simple model explains state-dependent shifts in the
distribution of observed Fano factors via a suppression in the variance of gain
fluctuations in the alert state. A population model with stimulus-induced
variability tuning and behaviorally constrained information-limiting
correlations explores the potential enhancement in stimulus discriminability by
the cortical population in the alert state.Comment: 36 pages, 18 figure
Multiplicative Auditory Spatial Receptive Fields Created by a Hierarchy of Population Codes
A multiplicative combination of tuning to interaural time difference (ITD) and interaural level difference (ILD) contributes to the generation of spatially selective auditory neurons in the owl's midbrain. Previous analyses of multiplicative responses in the owl have not taken into consideration the frequency-dependence of ITD and ILD cues that occur under natural listening conditions. Here, we present a model for the responses of ITD- and ILD-sensitive neurons in the barn owl's inferior colliculus which satisfies constraints raised by experimental data on frequency convergence, multiplicative interaction of ITD and ILD, and response properties of afferent neurons. We propose that multiplication between ITD- and ILD-dependent signals occurs only within frequency channels and that frequency integration occurs using a linear-threshold mechanism. The model reproduces the experimentally observed nonlinear responses to ITD and ILD in the inferior colliculus, with greater accuracy than previous models. We show that linear-threshold frequency integration allows the system to represent multiple sound sources with natural sound localization cues, whereas multiplicative frequency integration does not. Nonlinear responses in the owl's inferior colliculus can thus be generated using a combination of cellular and network mechanisms, showing that multiple elements of previous theories can be combined in a single system
Regulation of Irregular Neuronal Firing by Autaptic Transmission
The importance of self-feedback autaptic transmission in modulating
spike-time irregularity is still poorly understood. By using a biophysical
model that incorporates autaptic coupling, we here show that self-innervation
of neurons participates in the modulation of irregular neuronal firing,
primarily by regulating the occurrence frequency of burst firing. In
particular, we find that both excitatory and electrical autapses increase the
occurrence of burst firing, thus reducing neuronal firing regularity. In
contrast, inhibitory autapses suppress burst firing and therefore tend to
improve the regularity of neuronal firing. Importantly, we show that these
findings are independent of the firing properties of individual neurons, and as
such can be observed for neurons operating in different modes. Our results
provide an insightful mechanistic understanding of how different types of
autapses shape irregular firing at the single-neuron level, and they highlight
the functional importance of autaptic self-innervation in taming and modulating
neurodynamics.Comment: 27 pages, 8 figure
Particle-filtering approaches for nonlinear Bayesian decoding of neuronal spike trains
The number of neurons that can be simultaneously recorded doubles every seven
years. This ever increasing number of recorded neurons opens up the possibility
to address new questions and extract higher dimensional stimuli from the
recordings. Modeling neural spike trains as point processes, this task of
extracting dynamical signals from spike trains is commonly set in the context
of nonlinear filtering theory. Particle filter methods relying on importance
weights are generic algorithms that solve the filtering task numerically, but
exhibit a serious drawback when the problem dimensionality is high: they are
known to suffer from the 'curse of dimensionality' (COD), i.e. the number of
particles required for a certain performance scales exponentially with the
observable dimensions. Here, we first briefly review the theory on filtering
with point process observations in continuous time. Based on this theory, we
investigate both analytically and numerically the reason for the COD of
weighted particle filtering approaches: Similarly to particle filtering with
continuous-time observations, the COD with point-process observations is due to
the decay of effective number of particles, an effect that is stronger when the
number of observable dimensions increases. Given the success of unweighted
particle filtering approaches in overcoming the COD for continuous- time
observations, we introduce an unweighted particle filter for point-process
observations, the spike-based Neural Particle Filter (sNPF), and show that it
exhibits a similar favorable scaling as the number of dimensions grows.
Further, we derive rules for the parameters of the sNPF from a maximum
likelihood approach learning. We finally employ a simple decoding task to
illustrate the capabilities of the sNPF and to highlight one possible future
application of our inference and learning algorithm
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