Characterizing Unobserved Factors Driving Local Field Potential Dynamics Underlying a Time-Varying Spike Generation

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

“…Although the output of these models depends on the population state changes, their stimulus sensitivity parameters are time-invariant and thus are not able to characterize the time-varying characteristics of the neuron’s RF. 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 ).…”

“…Alternatively, information-theoretic methods based on mutual information or entropy have been used to determine the dimensions along which stimulus information reflected in the response is maximized (Paninski, 2003a;Sharpee et al, 2004;Shlens et al, 2006;Tang et al, 2008). The maximum mutual information methods can (Keat et al, 2001) Sensorimotor cortex (Paninski, 2004) M1, dorsal premotor cortex (Stevenson et al, 2009;Perich et al, 2018) Retina (Gerwinn et al, 2010) LGN (Babadi et al, 2010) Auditory midbrain (Calabrese et al, 2011) M1 (Kim et al, 2011) Neocortex (Pozzorini et al, 2013) LIP (Park et al, 2014) A1 and prefrontal cortex (Sheikhattar et al, 2018) MT (Yates et al, 2020) Nonlinear LGN (Butts et al, 2007) Barrel cortex V1 (Kass et al, 2011) A1 Schinkel-Bielefeld et al, 2012;Willmore et al, 2016) Retina Retina (Pillow et al, 2008(Pillow et al, , 2011Vidne et al, 2012) MT, LIP (Yates et al, 2017) Prefrontal cortex, mediodorsal thalamus (Rikhye et al, 2018) Thalamus, visual cortex, hippocampus, striatum, motor cortex LGN (Stanley, 2002) Inferior colliculus, A1 (Meyer et al, 2014) A1 (Sheikhattar et al, 2016) Cortical assembly (Mukherjee and Babadi, 2021) Nonstationary Gain GLM (Niknam et al, 2017a(Niknam et al, ,b, 2018(Niknam et al, , 2019 MT, V4 (Akbarian et al, 2021;Weng et al, 2023) State space GLM Hidden states Milliseconds to seconds Often requires population responses, large sample size, or the estimation of posterior densities…”