microinjectrode-system-for-combined-drug-infusion

Microinjectrode System for Combined Drug Infusion and Electrophysiology

This microinjectrode system is designed for drug infusion, electrophysiology, and delivery and retrieval of experimental probes, such as microelectrodes ...

University of Utah

This paper aims to develop a computational model that incorporates the functional effects of modulatory covariates (such as context, task, or behavior), which dynamically alter the relationship between the stimulus and the neural response.

Developing a Nonstationary Computational Framework With Application to Modeling Dynamic Modulations in Neural Spiking Responses.

The online maintenance and manipulation of information in working memory (WM) is essential for guiding behavior based on our goals. Understanding how WM alters sensory processing in pursuit of different behavioral objectives is therefore crucial to establish the neural basis of our goal-directed behavior. Here we show that, in the middle temporal (MT) area of rhesus monkeys, the power of the local field potentials in the αβ band (8-25 Hz) increases, reflecting the remembered location and the animal's performance. Moreover, the content of WM determines how coherently MT sites oscillate and how synchronized spikes are relative to these oscillations. These changes in spike timing are not only sufficient to carry sensory and memory information, they can also account for WM-induced sensory enhancement. These results provide a mechanistic-level understanding of how WM alters sensory processing by coordinating the timing of spikes across the neuronal population, enhancing the sensory representation of WM targets.

Working Memory Enhances Cortical Representations via Spatially Specific Coordination of Spike Times.

An ensemble of neurons can provide a dynamic representation of external stimuli, ongoing processes, or upcoming actions. This dynamic representation could be achieved by changes in the activity of individual neurons and/or their interactions. To investigate these possibilities, we simultaneously recorded from ensembles of prefrontal neurons in non-human primates during a memory-guided saccade task. Using both decoding and encoding methods, we examined changes in the information content of individual neurons and that of ensembles between visual encoding and saccadic target selection. We found that individual neurons maintained their limited spatial sensitivity between these cognitive states, whereas the ensemble selectively improved its encoding of spatial locations far from the neurons' preferred locations. This population-level "encoding expansion" was not due to the ceiling effect at the preferred locations and was accompanied by selective changes in noise correlations for non-preferred locations. Moreover, the encoding expansion was observed for ensembles of different types of neurons and could not be explained by shifts in the preferred location of individual neurons. Our results demonstrate that the representation of space by neuronal ensembles is dynamically enhanced prior to saccades, and this enhancement occurs alongside changes in noise correlations more than changes in the activity of individual neurons.

Selective Changes in Noise Correlations Contribute to an Enhanced Representation of Saccadic Targets in Prefrontal Neuronal Ensembles.

Correlations between neurons can profoundly impact the information encoding capacity of a neural population. We studied how maintenance of visuospatial information affects correlated activity in visual areas by recording the activity of neurons in visual area MT of rhesus macaques during a spatial working memory task. Correlations between MT neurons depended upon the spatial overlap between neurons' receptive fields. These correlations were influenced by the content of working memory, but the effect of a top-down memory signal differed in the presence or absence of bottom-up visual input. Neurons representing the same area of space showed increased correlations when remembering a location in their receptive fields in the absence of visual input, but decreased correlations in the presence of a visual stimulus. This set of results reveals the correlating nature of top-down signals influencing visual areas and uncovers how such a correlating signal, in interaction with bottom-up information, could enhance sensory representations.

Concurrent influence of top-down and bottom-up inputs on correlated activity of Macaque extrastriate neurons.

The processes of attention and working memory are conspicuously interlinked, suggesting that they may involve overlapping neural mechanisms. Working memory (WM) is the ability to maintain information in the absence of sensory input. Attention is the process by which a specific target is selected for further processing, and neural resources directed toward that target. The content of WM can be used to direct attention, and attention can in turn determine which information is encoded into WM. Here we discuss the similarities between attention and WM and the role prefrontal cortex (PFC) plays in each. First, at the theoretical level, we describe how attention and WM can both rely on models based on attractor states. Then we review the evidence for an overlap between the areas involved in both functions, especially the frontal eye field (FEF) portion of the prefrontal cortex. We also discuss similarities between the neural changes in visual areas observed during attention and WM. At the cellular level, we review the literature on the role of prefrontal DA in both attention and WM at the behavioral and neural levels. Finally, we summarize the anatomical evidence for an overlap between prefrontal mechanisms involved in attention and WM. Altogether, a summary of pharmacological, electrophysiological, behavioral, and anatomical evidence for a contribution of the FEF part of prefrontal cortex to attention and WM is provided.

Prefrontal Contributions to Attention and Working Memory.

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.

Article	Year
Developing a Nonstationary Computational Framework With Application to Modeling Dynamic Modulations in Neural Spiking Responses. IEEE transactions on bio-medical engineering\| PubMed ID: 29035203	2018
Working Memory Enhances Cortical Representations via Spatially Specific Coordination of Spike Times. Neuron\| PubMed ID: 29398360	2018
Selective Changes in Noise Correlations Contribute to an Enhanced Representation of Saccadic Targets in Prefrontal Neuronal Ensembles. Cerebral cortex (New York, N.Y. : 1991)\| PubMed ID: 29893800	2018
Concurrent influence of top-down and bottom-up inputs on correlated activity of Macaque extrastriate neurons. Nature communications\| PubMed ID: 30568166	2018
Prefrontal Contributions to Attention and Working Memory. Current topics in behavioral neurosciences\| PubMed ID: 30739308	2019
Characterizing and dissociating multiple time-varying modulatory computations influencing neuronal activity. PLoS computational biology\| PubMed ID: 31513570	2019

Behrad Noudoost

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