NEURAL REPRESENTATION OF SENSORY STIMULI AND SENSORY INTERPRETATION OF NEURAL ACTIVITY

1981 ◽  
pp. 103-125 ◽  
Author(s):  
Peter Johannesma
Keyword(s):  
Neural Activity ◽  
Neural Representation ◽  
Sensory Stimuli

scholarly journals The Hemo-Neural Hypothesis: On The Role of Blood Flow in Information Processing

2008 ◽  
Vol 99 (5) ◽  
pp. 2035-2047 ◽  
Author(s):  
Christopher I. Moore ◽  
Rosa Cao
Keyword(s):  
Information Processing ◽  
Neural Activity ◽  
Neural Representation ◽  
Regulatory Control ◽  
Activity Levels ◽  
Functional Hyperemia ◽  
Sensory Stimuli ◽  
Brain Vasculature ◽  
Medical Benefits

Brain vasculature is a complex and interconnected network under tight regulatory control that exists in intimate communication with neurons and glia. Typically, hemodynamics are considered to exclusively serve as a metabolic support system. In contrast to this canonical view, we propose that hemodynamics also play a role in information processing through modulation of neural activity. Functional hyperemia, the basis of the functional MRI (fMRI) BOLD signal, is a localized influx of blood correlated with neural activity levels. Functional hyperemia is considered by many to be excessive from a metabolic standpoint, but may be appropriate if interpreted as having an activity-dependent neuro-modulatory function. Hemodynamics may impact neural activity through direct and indirect mechanisms. Direct mechanisms include delivery of diffusible blood-borne messengers and mechanical and thermal modulation of neural activity. Indirect mechanisms are proposed to act through hemodynamic modulation of astrocytes, which can in turn regulate neural activity. These hemo-neural mechanisms should alter the information processing capacity of active local neural networks. Here, we focus on analysis of neocortical sensory processing. We predict that hemodynamics alter the gain of local cortical circuits, modulating the detection and discrimination of sensory stimuli. This novel view of information processing—that includes hemodynamics as an active and significant participant—has implications for understanding neural representation and the construction of accurate brain models. There are also potential medical benefits of an improved understanding of the role of hemodynamics in neural processing, as it directly bears on interpretation of and potential treatment for stroke, dementia, and epilepsy.

scholarly journals Mirror neurons are modulated by grip force and reward expectation in the sensorimotor cortices (S1, M1, PMd, PMv)

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Md Moin Uddin Atique ◽  
Joseph Thachil Francis
Keyword(s):  
Neural Activity ◽  
Visual Information ◽  
Mirror Neurons ◽  
Grip Force ◽  
Neural Representation ◽  
Virtual Object ◽  
Time Lags ◽  
Subjective Value ◽  
Machine Interface ◽  
Definition Of

AbstractMirror Neurons (MNs) respond similarly when primates make or observe grasping movements. Recent work indicates that reward expectation influences rostral M1 (rM1) during manual, observational, and Brain Machine Interface (BMI) reaching movements. Previous work showed MNs are modulated by subjective value. Here we expand on the above work utilizing two non-human primates (NHPs), one male Macaca Radiata (NHP S) and one female Macaca Mulatta (NHP P), that were trained to perform a cued reward level isometric grip-force task, where the NHPs had to apply visually cued grip-force to move and transport a virtual object. We found a population of (S1 area 1–2, rM1, PMd, PMv) units that significantly represented grip-force during manual and observational trials. We found the neural representation of visually cued force was similar during observational trials and manual trials for the same units; however, the representation was weaker during observational trials. Comparing changes in neural time lags between manual and observational tasks indicated that a subpopulation fit the standard MN definition of observational neural activity lagging the visual information. Neural activity in (S1 areas 1–2, rM1, PMd, PMv) significantly represented force and reward expectation. In summary, we present results indicating that sensorimotor cortices have MNs for visually cued force and value.

scholarly journals Statistical assessment of the stability of neural movement representations

2011 ◽  
Vol 106 (2) ◽  
pp. 764-774 ◽  
Author(s):  
Ian H. Stevenson ◽  
Anil Cherian ◽  
Brian M. London ◽  
Nicholas A. Sachs ◽  
Eric Lindberg ◽  
...  
Keyword(s):  
Primary Motor Cortex ◽  
Tuning Curve ◽  
Modulation Depth ◽  
Measurement Noise ◽  
Neural Representation ◽  
Experimental Conditions ◽  
Sensory Stimuli ◽  
Tuning Curves ◽  
Dynamic Tuning ◽  
Preferred Direction

In systems neuroscience, neural activity that represents movements or sensory stimuli is often characterized by spatial tuning curves that may change in response to training, attention, altered mechanics, or the passage of time. A vital step in determining whether tuning curves change is accounting for estimation uncertainty due to measurement noise. In this study, we address the issue of tuning curve stability using methods that take uncertainty directly into account. We analyze data recorded from neurons in primary motor cortex using chronically implanted, multielectrode arrays in four monkeys performing center-out reaching. With the use of simulations, we demonstrate that under typical experimental conditions, the effect of neuronal noise on estimated preferred direction can be quite large and is affected by both the amount of data and the modulation depth of the neurons. In experimental data, we find that after taking uncertainty into account using bootstrapping techniques, the majority of neurons appears to be very stable on a timescale of minutes to hours. Lastly, we introduce adaptive filtering methods to explicitly model dynamic tuning curves. In contrast to several previous findings suggesting that tuning curves may be in constant flux, we conclude that the neural representation of limb movement is, on average, quite stable and that impressions to the contrary may be largely the result of measurement noise.

scholarly journals Robust and distributed neural representation of action values

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Eun Ju Shin ◽  
Yunsil Jang ◽  
Soyoun Kim ◽  
Hoseok Kim ◽  
Xinying Cai ◽  
...  
Keyword(s):  
Neural Activity ◽  
Statistical Tests ◽  
Brain Structures ◽  
Surrogate Data ◽  
Neural Representation ◽  
Decision Variables ◽  
Multiple Regions ◽  
Serial Correlations ◽  
Action Value ◽  
The Brain

Studies in rats, monkeys, and humans have found action-value signals in multiple regions of the brain. These findings suggest that action-value signals encoded in these brain structures bias choices toward higher expected rewards. However, previous estimates of action-value signals might have been inflated by serial correlations in neural activity and also by activity related to other decision variables. Here, we applied several statistical tests based on permutation and surrogate data to analyze neural activity recorded from the striatum, frontal cortex, and hippocampus. The results show that previously identified action-value signals in these brain areas cannot be entirely accounted for by concurrent serial correlations in neural activity and action value. We also found that neural activity related to action value is intermixed with signals related to other decision variables. Our findings provide strong evidence for broadly distributed neural signals related to action value throughout the brain.

scholarly journals Neuronal pattern separation of motion-relevant input in LIP activity

2017 ◽  
Vol 117 (2) ◽  
pp. 738-755 ◽  
Author(s):  
Nareg Berberian ◽  
Amanda MacPherson ◽  
Eloïse Giraud ◽  
Lydia Richardson ◽  
J.-P. Thivierge
Keyword(s):  
Neuronal Activity ◽  
Neural Activity ◽  
Population Model ◽  
Visual Motion ◽  
Temporal Dynamics ◽  
Strong Predictor ◽  
Pattern Separation ◽  
Sensory Stimuli ◽  
Sensory Discrimination

In various regions of the brain, neurons discriminate sensory stimuli by decreasing the similarity between ambiguous input patterns. Here, we examine whether this process of pattern separation may drive the rapid discrimination of visual motion stimuli in the lateral intraparietal area (LIP). Starting with a simple mean-rate population model that captures neuronal activity in LIP, we show that overlapping input patterns can be reformatted dynamically to give rise to separated patterns of neuronal activity. The population model predicts that a key ingredient of pattern separation is the presence of heterogeneity in the response of individual units. Furthermore, the model proposes that pattern separation relies on heterogeneity in the temporal dynamics of neural activity and not merely in the mean firing rates of individual neurons over time. We confirm these predictions in recordings of macaque LIP neurons and show that the accuracy of pattern separation is a strong predictor of behavioral performance. Overall, results propose that LIP relies on neuronal pattern separation to facilitate decision-relevant discrimination of sensory stimuli. NEW & NOTEWORTHY A new hypothesis is proposed on the role of the lateral intraparietal (LIP) region of cortex during rapid decision making. This hypothesis suggests that LIP alters the representation of ambiguous inputs to reduce their overlap, thus improving sensory discrimination. A combination of computational modeling, theoretical analysis, and electrophysiological data shows that the pattern separation hypothesis links neural activity to behavior and offers novel predictions on the role of LIP during sensory discrimination.

scholarly journals A novel device for real-time measurement and manipulation of licking behavior in head-fixed mice

2018 ◽  
Vol 120 (6) ◽  
pp. 2975-2987 ◽  
Author(s):  
Brice Williams ◽  
Anderson Speed ◽  
Bilal Haider
Keyword(s):  
Real Time ◽  
Neural Activity ◽  
Closed Loop ◽  
Neural Circuits ◽  
Neural Circuit ◽  
Visual Behavior ◽  
Sensory Stimuli ◽  
Neural Recordings ◽  
Control Signals ◽  
Sensory Motor

The mouse has become an influential model system for investigating the mammalian nervous system. Technologies in mice enable recording and manipulation of neural circuits during tasks where they respond to sensory stimuli by licking for liquid rewards. Precise monitoring of licking during these tasks provides an accessible metric of sensory-motor processing, particularly when combined with simultaneous neural recordings. There are several challenges in designing and implementing lick detectors during head-fixed neurophysiological experiments in mice. First, mice are small, and licking behaviors are easily perturbed or biased by large sensors. Second, neural recordings during licking are highly sensitive to electrical contact artifacts. Third, submillisecond lick detection latencies are required to generate control signals that manipulate neural activity at appropriate time scales. Here we designed, characterized, and implemented a contactless dual-port device that precisely measures directional licking in head-fixed mice performing visual behavior. We first determined the optimal characteristics of our detector through design iteration and then quantified device performance under ideal conditions. We then tested performance during head-fixed mouse behavior with simultaneous neural recordings in vivo. We finally demonstrate our device’s ability to detect directional licks and generate appropriate control signals in real time to rapidly suppress licking behavior via closed-loop inhibition of neural activity. Our dual-port detector is cost effective and easily replicable, and it should enable a wide variety of applications probing the neural circuit basis of sensory perception, motor action, and learning in normal and transgenic mouse models. NEW & NOTEWORTHY Mice readily learn tasks in which they respond to sensory cues by licking for liquid rewards; tasks that involve multiple licking responses allow study of neural circuits underlying decision making and sensory-motor integration. Here we design, characterize, and implement a novel dual-port lick detector that precisely measures directional licking in head-fixed mice performing visual behavior, enabling simultaneous neural recording and closed-loop manipulation of licking.

scholarly journals A neural link between affective understanding and interpersonal attraction

2016 ◽  
Vol 113 (16) ◽  
pp. E2248-E2257 ◽  
Author(s):  
Silke Anders ◽  
Roos de Jong ◽  
Christian Beck ◽  
John-Dylan Haynes ◽  
Thomas Ethofer
Keyword(s):  
Neural Activity ◽  
Interpersonal Attraction ◽  
Affective State ◽  
Neural Mechanism ◽  
Reward System ◽  
Mental States ◽  
Neural Representation ◽  
Affective Behavior ◽  
Intrinsic Reward ◽  
Social Encounters

Being able to comprehend another person’s intentions and emotions is essential for successful social interaction. However, it is currently unknown whether the human brain possesses a neural mechanism that attracts people to others whose mental states they can easily understand. Here we show that the degree to which a person feels attracted to another person can change while they observe the other’s affective behavior, and that these changes depend on the observer’s confidence in having correctly understood the other’s affective state. At the neural level, changes in interpersonal attraction were predicted by activity in the reward system of the observer’s brain. Importantly, these effects were specific to individual observer–target pairs and could not be explained by a target’s general attractiveness or expressivity. Furthermore, using multivoxel pattern analysis (MVPA), we found that neural activity in the reward system of the observer’s brain varied as a function of how well the target’s affective behavior matched the observer’s neural representation of the underlying affective state: The greater the match, the larger the brain’s intrinsic reward signal. Taken together, these findings provide evidence that reward-related neural activity during social encounters signals how well an individual’s “neural vocabulary” is suited to infer another person’s affective state, and that this intrinsic reward might be a source of changes in interpersonal attraction.

Neural Representation of the Luminance and Brightness of a Uniform Surface in the Macaque Primary Visual Cortex

2001 ◽  
Vol 86 (5) ◽  
pp. 2559-2570 ◽  
Author(s):  
Masaharu Kinoshita ◽  
Hidehiko Komatsu
Keyword(s):  
Visual Cortex ◽  
Receptive Field ◽  
Primary Visual Cortex ◽  
Neural Activity ◽  
Dynamic Range ◽  
Neural Representation ◽  
Homogeneous Surface ◽  
V1 Neurons ◽  
Classical Receptive Field ◽  
Multiple Unit

The perceived brightness of a surface is determined not only by the luminance of the surface (local information), but also by the luminance of its surround (global information). To better understand the neural representation of surface brightness, we investigated the effects of local and global luminance on the activity of neurons in the primary visual cortex (V1) of awake macaque monkeys. Single- and multiple-unit recordings were made from V1 while the monkeys were performing a visual fixation task. The classical receptive field of each neuron was identified as a region responding to a spot stimulus. Neural responses were assessed using homogeneous surfaces at least three times as large as the receptive field as stimuli. We first examined the sensitivity of neurons to variation in local surface luminance, while the luminance of the surround was held constant. The activity of a large majority of surface-responsive neurons (106/115) varied monotonically with changes in surface luminance; in some the dynamic range was over 3 log units. This monotonic relation between surface luminance and neural activity was more evident later in the stimulus period than early on. The effect of the global luminance on neural activity was then assessed in 81 of the surface-responsive neurons by varying the luminance of the surround while holding the luminance of the surface constant. The activity of one group of neurons (25/81) was unaffected by the luminance of the surround; these neurons appear to encode the physical luminance of a surface covering the receptive field. The responses of the other neurons were affected by the luminance of the surround. The effects of the luminances of the surface and the surround on the activities of 26 of these neurons were in the same direction (either increased or decreased), while the effects on the remaining 25 neurons were in opposite directions. The activities of the latter group of neurons seemed to parallel the perceived brightness of the surface, whereas the former seemed to encode the level of illumination. There were differences across different types of neurons with regard to the layer distribution. These findings indicate that global luminance information significantly modulates the activity of surface-responsive V1 neurons and that not only physical luminance, but also perceived brightness, of a homogeneous surface is represented in V1.

Sensory discrimination: decision process

1980 ◽  
Vol 43 (6) ◽  
pp. 1771-1792 ◽  
Author(s):  
K. O. Johnson
Keyword(s):  
Experimental Design ◽  
Neural Activity ◽  
Decision Process ◽  
Experimental Studies ◽  
Experimental Designs ◽  
Neural Representation ◽  
Mathematical Representation ◽  
Sensory Discrimination ◽  
Sample Data ◽  
Sensory Processes

1. This paper and a following paper deal with problems, such as the following, that arise in experimental studies of the neural mechanisms underlying sensory discrimination: What measures of neural activity are relevant in such a study? How can sample data from the responses of single neurons be combined to represent the information relayed by a population of neurons? How can neural data be compared with results from psychophysical studies? What assumptions are implicit in any such comparison? What are the implications of assumptions that neurons respond independently or that they have homogeneous response properties? How can neural codes be assessed in a systematic way? Can psychophysical and neurophysiological observations be combined to infer mechanisms or relationships in the processes underlying discrimination? All of these questions require some theoretical framework before they can be answered. These papers set out such a framework, they deal with most of those questions, and they provide practicable formulas for relating sample data from neurophysiological experiments to behavioral measures derived from psychophysical experiments. 2. The processes that intervene between a relatively peripheral array of neural activity and a subject's decision in a discrimination task are split into two sections: a) the ascending sensory processes that provide the final patterns of neural activity on which discrimination is based, and b) a process that yields decisions of the type required by the experimental design used in the psychophysical study. The approach is to develop a theory of the decision process in this paper, and then to expand it to incorporate the ascending processes in the following paper. 3. The decision theory deals with a class of experimental designs in which a subject is required to make a decision about two stimuli S1 and S2 (e.g., S1 is larger than S2, S2 is the same as S1, S2 was the modified stimulus, and so on). A mathematical representation for experimental designs of this type is developed. 4. The decision process is analyzed in two forms: a) a multivariate form in which the discrimination decision results directly from multidimensional neural representation of the two stimuli, and b) a vivariate form in which the final representation of each stimulus is a unidimensional variable. Conditions required for equivalence of these formulations are examined. 5. The theory includes as explicit variables a) the experimental design, b) the subject's discrimination strategy, c) bias, d) memory variance, e) bias variance, f) variance in the final neural representations of the stimuli, and g) their functional dependence on the stimuli that they represent. 6. Formulas are developed for the expected values of commonly used psychophysical measures such as the classical psychometric function, receiver operating characteristic (ROC) functions, discriminatory separation index (d'), and the difference limen. 7. Optimum discrimination behavior is analyzed.

scholarly journals Decoding locomotion from population neural activity in moving C. elegans

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Kelsey M Hallinen ◽  
Ross Dempsey ◽  
Monika Scholz ◽  
Xinwei Yu ◽  
Ashley Linder ◽  
...  
Keyword(s):  
Population Dynamics ◽  
Neural Activity ◽  
Single Neuron ◽  
Neural Representation ◽  
Correlation Structure ◽  
Experimental Measurements ◽  
Population Activity ◽  
Calcium Activity ◽  
C Elegans

We investigated the neural representation of locomotion in the nematode C. elegans by recording population calcium activity during movement. We report that population activity more accurately decodes locomotion than any single neuron. Relevant signals are distributed across neurons with diverse tunings to locomotion. Two largely distinct subpopulations are informative for decoding velocity and curvature, and different neurons’ activities contribute features relevant for different aspects of a behavior or different instances of a behavioral motif. To validate our measurements, we labeled neurons AVAL and AVAR and found that their activity exhibited expected transients during backward locomotion. Finally, we compared population activity during movement and immobilization. Immobilization alters the correlation structure of neural activity and its dynamics. Some neurons positively correlated with AVA during movement become negatively correlated during immobilization and vice versa. This work provides needed experimental measurements that inform and constrain ongoing efforts to understand population dynamics underlying locomotion in C. elegans.

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