Analysis and modelling of variability and covariability of population spike trains across multiple time scales

Lyamzin, Dmitry R.; Garcia-Lazaro, Jose A.; Lesica, Nicholas A.

doi:10.3109/0954898x.2012.679334

Cited by 3 publications

(2 citation statements)

References 42 publications

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“…It provides a theoretical basis for the optimal estimation of model parameters via classical statistical methods, and it is a reasonable approximation of the true distribution of activity in some brain areas. But activity in sub-cortical areas like the IC is typically underdispersed, i.e., it is much more reliable across trials than is expected for a Poisson process [22,23]. This phenomenon is illustrated in Fig 1b, which compares the peri-stimulus time histograms (PSTHs) of recorded and simulated neural responses for an example IC unit to 128 trials of a pure tone.…”

Section: Dnns Can Capture the Full Statistics Of Neural Spike Patternsmentioning

confidence: 97%

Modeling neural coding in the auditory midbrain with high resolution and accuracy

Drakopoulos,

Sabesan,

Xia

et al. 2024

Preprint

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Computational models of auditory processing can be valuable tools for research and technology development. Models of the cochlea are highly accurate and widely used, but models of the auditory brain lag far behind in both performance and penetration. Here, we present ICNet, a model that provides accurate simulation of neural coding in the inferior colliculus across a wide range of sounds, including near-perfect simulation of responses to speech. We developed ICNet using deep learning and large-scale intracranial recordings from gerbils, addressing three key modeling challenges that are common across all sensory systems: capturing the full statistical complexity of neuronal spike patterns; accounting for physiological and experimental non-stationarity; and extracting features of sensory processing that are common across different brains. ICNet can be used to simulate activity from thousands of neural units or to provide a compact representation of central auditory processing through its latent dynamics, facilitating a wide range of hearing and audio applications.

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Section: Dnns Can Capture the Full Statistics Of Neural Spike Patternsmentioning

confidence: 97%

Modeling neural coding in the auditory midbrain with high resolution and accuracy

Drakopoulos,

Sabesan,

Xia

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…potentially studying stochastic facilitation in setups that do not require any signals (input and output) to be defined; . development of new experimental methods for distinguishing what should be considered as signal and what should be considered as noise; new statistical methods might be useful to this end (Lyamzin et al 2012). …”

Section: Future Directions For Stochastic Facilitation Researchmentioning

confidence: 99%

A review of methods for identifying stochastic resonance in simulations of single neuron models

McDonnell

Iannella

et al. 2015

Network: Computation in Neural Systems

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Stochastic resonance (SR) is said to be observed when the presence of noise in a nonlinear system enables an output signal from the system to better represent some feature of an input signal than it does in the absence of noise. The effect has been observed in models of individual neurons, and in experiments performed on real neural systems. Despite the ubiquity of biophysical sources of stochastic noise in the nervous system, however, it has not yet been established whether neuronal computation mechanisms involved in performance of specific functions such as perception or learning might exploit such noise as an integral component, such that removal of the noise would diminish performance of these functions. In this paper we revisit the methods used to demonstrate stochastic resonance in models of single neurons. This includes a previously unreported observation in a multicompartmental model of a CA1-pyramidal cell. We also discuss, as a contrast to these classical studies, a form of 'stochastic facilitation', known as inverse stochastic resonance. We draw on the reviewed examples to argue why new approaches to studying 'stochastic facilitation' in neural systems need to be developed.

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Nonlinear Transfer of Signal and Noise Correlations in Cortical Networks

et al. 2015

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Signal and noise correlations, a prominent feature of cortical activity, reflect the structure and function of networks during sensory processing. However, in addition to reflecting network properties, correlations are also shaped by intrinsic neuronal mechanisms. Here we show that spike threshold transforms correlations by creating nonlinear interactions between signal and noise inputs; even when input noise correlation is constant, spiking noise correlation varies with both the strength and correlation of signal inputs. We characterize these effects systematically in vitro in mice and demonstrate their impact on sensory processing in vivo in gerbils. We also find that the effects of nonlinear correlation transfer on cortical responses are stronger in the synchronized state than in the desynchronized state, and show that they can be reproduced and understood in a model with a simple threshold nonlinearity. Since these effects arise from an intrinsic neuronal property, they are likely to be present across sensory systems and, thus, our results are a critical step toward a general understanding of how correlated spiking relates to the structure and function of cortical networks.

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Analysis and modelling of variability and covariability of population spike trains across multiple time scales

Cited by 3 publications

References 42 publications

Modeling neural coding in the auditory midbrain with high resolution and accuracy

Modeling neural coding in the auditory midbrain with high resolution and accuracy

A review of methods for identifying stochastic resonance in simulations of single neuron models

Nonlinear Transfer of Signal and Noise Correlations in Cortical Networks

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