Dataset Information

Neural Computations in a Dynamical System with Multiple Time Scales.

ABSTRACT: Neural systems display rich short-term dynamics at various levels, e.g., spike-frequency adaptation (SFA) at the single-neuron level, and short-term facilitation (STF) and depression (STD) at the synapse level. These dynamical features typically cover a broad range of time scales and exhibit large diversity in different brain regions. It remains unclear what is the computational benefit for the brain to have such variability in short-term dynamics. In this study, we propose that the brain can exploit such dynamical features to implement multiple seemingly contradictory computations in a single neural circuit. To demonstrate this idea, we use continuous attractor neural network (CANN) as a working model and include STF, SFA and STD with increasing time constants in its dynamics. Three computational tasks are considered, which are persistent activity, adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, and hence cannot be implemented by a single dynamical feature or any combination with similar time constants. However, with properly coordinated STF, SFA and STD, we show that the network is able to implement the three computational tasks concurrently. We hope this study will shed light on the understanding of how the brain orchestrates its rich dynamics at various levels to realize diverse cognitive functions.

SUBMITTER: Mi Y

PROVIDER: S-EPMC5020071 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Neural Computations in a Dynamical System with Multiple Time Scales.

Mi Yuanyuan Y Lin Xiaohan X Wu Si S

Frontiers in computational neuroscience 20160913

Neural systems display rich short-term dynamics at various levels, e.g., spike-frequency adaptation (SFA) at the single-neuron level, and short-term facilitation (STF) and depression (STD) at the synapse level. These dynamical features typically cover a broad range of time scales and exhibit large diversity in different brain regions. It remains unclear what is the computational benefit for the brain to have such variability in short-term dynamics. In this study, we propose that the brain can ex ...[more]

PMID: 27679569

Similar Datasets

Project description:The ventriloquism aftereffect (VAE) refers to a shift in auditory spatial perception following exposure to a spatial disparity between auditory and visual stimuli. The VAE has been previously measured on two distinct time scales. Hundreds or thousands of exposures to a an audio-visual spatial disparity produces enduring VAE that persists after exposure ceases. Exposure to a single audio-visual spatial disparity produces immediate VAE that decays over seconds. To determine if these phenomena are two extremes of a continuum or represent distinct processes, we conducted an experiment with normal hearing listeners that measured VAE in response to a repeated, constant audio-visual disparity sequence, both immediately after exposure to each audio-visual disparity and after the end of the sequence. In each experimental session, subjects were exposed to sequences of auditory and visual targets that were constantly offset by +8° or -8° in azimuth from one another, then localized auditory targets presented in isolation following each sequence. Eye position was controlled throughout the experiment, to avoid the effects of gaze on auditory localization. In contrast to other studies that did not control eye position, we found both a large shift in auditory perception that decayed rapidly after each AV disparity exposure, along with a gradual shift in auditory perception that grew over time and persisted after exposure to the AV disparity ceased. We modeled the temporal and spatial properties of the measured auditory shifts using grey box nonlinear system identification, and found that two models could explain the data equally well. In the power model, the temporal decay of the ventriloquism aftereffect was modeled with a power law relationship. This causes an initial rapid drop in auditory shift, followed by a long tail which accumulates with repeated exposure to audio-visual disparity. In the double exponential model, two separate processes were required to explain the data, one which accumulated and decayed exponentially and the other which slowly integrated over time. Both models fit the data best when the spatial spread of the ventriloquism aftereffect was limited to a window around the location of the audio-visual disparity. We directly compare the predictions made by each model, and suggest additional measurements that could help distinguish which model best describes the mechanisms underlying the VAE.

Project description: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.

Dataset Information

Neural Computations in a Dynamical System with Multiple Time Scales.

Publications

Neural Computations in a Dynamical System with Multiple Time Scales.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets