A computational model for characterizing visual information using both spikes and Local Field Potentials

Niknam, Kaiser; Akbarian, Amir; Noudoost, Behrad; Nategh, Neda

doi:10.1109/ner.2017.8008436

Cited by 8 publications

(6 citation statements)

References 10 publications

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“…Non-Poisson counts models such as the CMP and NB here could be used to explore the count variability and co-variability that have been linked to stimulus-onset (M. M. Churchland et al, 2010) or to attention and learning (Mitchell, Sundberg, & Reynolds, 2009). Since the CMP and NB models can be formulated as GLMs, other covariates, such as local field potentials (Niknam, Akbarian, Noudoost, & Nategh, 2017), plastic neural interactions (Ghanbari et al, 2018), or even latent variables (Chase, Schwartz, & Kass, 2010;Kulkarni & Paninski, 2007;Lawhern, Wu, Hatsopoulos, & Paninski, 2010), can easily be included in the models. Ultimately, the CMP and NB models provide a framework to describe the stimulus-dependence of both the mean and variance of neural responses.…”

Section: Discussionmentioning

confidence: 99%

Modeling stimulus-dependent variability improves decoding of population neural responses

et al. 2019

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Neural responses to repeated presentations of an identical stimulus often show substantial trial-to-trial variability. How the mean firing rate varies in response to different stimuli or during different movements (tuning curves) has been extensively modeled in a wide variety of neural systems. However, the variability of neural responses can also have clear tuning independent of the tuning in the mean firing rate. This suggests that the variability could contain information regarding the stimulus/movement beyond what is encoded in the mean firing rate. Here we demonstrate how taking variability into account can improve neural decoding. In a typical neural coding model spike counts are assumed to be Poisson with the mean response depending on an external variable, such as a stimulus or movement. Bayesian decoding methods then use the probabilities under these Poisson tuning models (the likelihood) to estimate the probability of each stimulus given the spikes on a given trial (the posterior). However, under the Poisson model, spike count variability is always exactly equal to the mean (Fano factor = 1). Here we use two alternative models -the Conway-Maxwell-Poisson (CMP) model and Negative Binomial (NB) model -to more flexibly characterize how neural variability depends on external stimuli. These models both contain the Poisson distribution as a special case but have an additional parameter that allows the variance to be greater than the mean (Fano factor >1) or, for the CMP model, less than the mean (Fano factor <1). We find that neural responses in primary motor (M1), visual (V1), and auditory (A1) cortices have diverse tuning in both their mean firing rates and response variability. Across cortical areas, we find that Bayesian decoders using the CMP or NB models improve stimulus/movement estimation accuracy by 4-12% compared to the Poisson model. Moreover, the uncertainty of the non-Poisson decoders more accurately reflects the magnitude of estimation errors. In addition to tuning curves that reflect average neural responses, stimulus-dependent response variability may be an important aspect of the neural code. Modeling this structure could, potentially, lead to improvements in brain machine interfaces.

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Section: Discussionmentioning

confidence: 99%

Modeling stimulus-dependent variability improves decoding of population neural responses

et al. 2019

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“…Building upon the development for time-varying GLM extensions, Niknam and colleagues extended the spiking NSG-GLM ( Figure 3D ) to incorporate the simultaneously recorded LFP as a model covariate with its own temporal filter ( Figures 4F , G ) ( Niknam et al, 2018 ). This approach enabled the characterization of the time-varying spatiotemporal sensitivity of extrastriate neurons during saccades and demonstrated increased prediction and decoding performance compared to the previously developed NSG-GLM ( Niknam et al, 2017a , b , 2018 ).…”

Section: Glm-based Approaches For Nonstationary Responsesmentioning

confidence: 99%

Time-varying generalized linear models: characterizing and decoding neuronal dynamics in higher visual areas

Weng,

Clark,

Akbarian

et al. 2024

Front. Comput. Neurosci.

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To create a behaviorally relevant representation of the visual world, neurons in higher visual areas exhibit dynamic response changes to account for the time-varying interactions between external (e.g., visual input) and internal (e.g., reward value) factors. The resulting high-dimensional representational space poses challenges for precisely quantifying individual factors’ contributions to the representation and readout of sensory information during a behavior. The widely used point process generalized linear model (GLM) approach provides a powerful framework for a quantitative description of neuronal processing as a function of various sensory and non-sensory inputs (encoding) as well as linking particular response components to particular behaviors (decoding), at the level of single trials and individual neurons. However, most existing variations of GLMs assume the neural systems to be time-invariant, making them inadequate for modeling nonstationary characteristics of neuronal sensitivity in higher visual areas. In this review, we summarize some of the existing GLM variations, with a focus on time-varying extensions. We highlight their applications to understanding neural representations in higher visual areas and decoding transient neuronal sensitivity as well as linking physiology to behavior through manipulation of model components. This time-varying class of statistical models provide valuable insights into the neural basis of various visual behaviors in higher visual areas and hold significant potential for uncovering the fundamental computational principles that govern neuronal processing underlying various behaviors in different regions of the brain.

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“…Dividing both sides by the constant slope reveals that the points on the tangent plane are normal to the weight vector and have a relative center of mass equal to zero. (15) As input firing patterns move away from the tangent point, , of the centroid plane and the NRS, two things happen: 1) their center of mass moves closer to the origin, and 2) they become more spread out in terms of their inter-spike intervals.…”

Section: B Center Of Mass (Centroid) and Fixed Delaymentioning

confidence: 99%

“…Although current spiking neuron models describe a biological neuron's behavior in some detail, analysis of such networks generally relies on differential equations [7][8][9][10][11][12], statistical approaches and averaging [13][14][15], or implicit timing information of spikes. Thus, irrespective of the fact that biological neural networks encode information through the detailed timing of spikes [16][17][18], our understanding of how they use detailed timing for information processing is still very limited.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Analyzing, Designing, and Visualizing Spiking Neural Networks—Part II: Nonlinear Response Surfaces

et al. 2020

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In this second part of a two-part study, we extend to nonlinear synaptic responses a new framework, called Response Surfaces (RSs), for analyzing, designing, and visualizing spiking neurons and networks. An RS is the transfer function between input and output firing times of a spiking neuron and shows all the patterns of input spike times that fire a neuron at a given time. Here in Part II, we present four mathematically tractable functions to model more realistic post-synaptic-potential waveforms, and we build on the linear RS framework of Part I to create a nonlinear RS framework. We then discuss the qualitative differences between the linear and nonlinear RSs frameworks and revisit problems of Part I using the nonlinear RSs framework: graphing the transfer function of a nonlinear spiking neuron, designing an efficient spiking-XOR gate, analyzing phase-tracking in recurrent SNNs, and developing transfer functions for calculating the output firing times of a spiking neuron given a set of input firing times. For the nonlinear RS framework, we show that the output firing time of a nonlinear spiking neuron is equivalent to the center-ofmass of input spike times (acting as positions) and weights (acting as masses) plus a fixed delay plus a secondorder correction. The second-order centroid correction is one of the main differences between the linear and nonlinear RS frameworks. For recurrent networks, the nonlinear RS framework also shows a more stable behavior compared to linear SNNs, owing to the smooth shapes of nonlinear post-synaptic-potential waveforms.

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A computational model for characterizing visual information using both spikes and Local Field Potentials

Cited by 8 publications

References 10 publications

Modeling stimulus-dependent variability improves decoding of population neural responses

Modeling stimulus-dependent variability improves decoding of population neural responses

Time-varying generalized linear models: characterizing and decoding neuronal dynamics in higher visual areas

A Framework for Analyzing, Designing, and Visualizing Spiking Neural Networks—Part II: Nonlinear Response Surfaces

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