Characterizing and dissociating multiple time-varying modulatory computations influencing neuronal activity

Abstract

In many brain areas, sensory responses are heavily modulated by factors including attentional state, context, reward history, motor preparation, learned associations, and other cognitive variables. Modelling the effect of these modulatory factors on sensory responses has proven challenging, mostly due to the time-varying and nonlinear nature of the underlying computations. Here we present a computational model capable of capturing and dissociating multiple time-varying modulatory effects on neuronal responses on the order of milliseconds. The model’s performance is tested on extrastriate perisaccadic visual responses in nonhuman primates. Visual neurons respond to stimuli presented around the time of saccades differently than during fixation. These perisaccadic changes include sensitivity to the stimuli presented at locations outside the neuron’s receptive field, which suggests a contribution of multiple sources to perisaccadic response generation. Current computational approaches cannot quantitatively characterize the contribution of each modulatory source in response generation, mainly due to the very short timescale on which the saccade takes place. In this study, we use a high spatiotemporal resolution experimental paradigm along with a novel extension of the generalized linear model framework (GLM), termed the sparse-variable GLM, to allow for time-varying model parameters representing the temporal evolution of the system with a resolution on the order of milliseconds. We used this model framework to precisely map the temporal evolution of the spatiotemporal receptive field of visual neurons in the middle temporal area during the execution of a saccade. Moreover, an extended model based on a factorization of the sparse-variable GLM allowed us to disassociate and quantify the contribution of individual sources to the perisaccadic response. Our results show that our novel framework can precisely capture the changes in sensitivity of neurons around the time of saccades, and provide a general framework to quantitatively track the role of multiple modulatory sources over time.

A Sensory Memory to Preserve Visual Representations Across Eye Movements

“…This representation reduces the dimensionality of the spatiotemporal sensitivity map by about two orders of magnitude, however, it is still far beyond the practical dimensionality for a computationally robust estimation of the sensitivity values (Niknam et al, 2019) using an experimentally tractable amount of data. The short duration of saccade execution makes it infeasible to acquire a large number of data points from all spatial locations and times relative to saccade onset.…”

“…In order to estimate the weight of each STU, we develop a new variation of classical GLMs, termed the sparse-variable GLM (S-model (Niknam et al, 2019), Figure S3), which enables us to represent a high-dimensional model using a sparse set of variables selected through a dimensionality reduction process and estimate those variables. Using this encoding model, we can capture the neuron’s high-resolution spatiotemporal sensitivity using limited perisaccadic data.…”

“…The sigmoidal nonlinearity in the S-model’s CIF (Eq. 5) makes the LL function not convex, meaning it may not give a unique optimal solution (more details in (Akbarian et al, 2017; Niknam et al, 2019)). Also, considering the number of data points (spiking events) relative to the number of model parameters to be estimated, this optimization may be subject to the overfitting problem.…”

“…These strategies included early stopping, representation of model kernels using smooth basis functions, data-informed initialization, an iterative, robust gradient-based search algorithm, and cross validation, in addition to those strategies described in the dimensionality reduction section for the stimulus kernels specifically. For more details of these optimization strategies refer to (Niknam et al, 2019).…”

“…Note that the combination of STU weights across delays for a certain time and location is a representation of the neuron’s sensitivity, classically known as its “kernel” (middle panel). Overall, the model performed well in capturing the dynamics of neuronal responses, as well as providing high temporal resolution sensitivity maps of neurons (Niknam et al, 2019) (see STAR Methods; Figure S3-4).…”