scholarly journals Analysing neuronal correlates of the comparison of two sequentially presented sensory stimuli

2002 ◽  
Vol 357 (1428) ◽  
pp. 1843-1850 ◽  
Author(s):  
Carlos D. Brody ◽  
Adrián Hernández ◽  
Antonio Zainos ◽  
Luis Lemus ◽  
Ranulfo Romo
Keyword(s):  
Macaca Mulatta ◽  
Cortical Area ◽  
Discrimination Task ◽  
Memory Trace ◽  
Sensory Stimulus ◽  
Neuronal Responses ◽  
Sensory Stimuli ◽  
Sensory Discrimination ◽  
Cortical Areas ◽  
Highly Correlated

In a typical sequential sensory discrimination task, subjects are required to make a decision based on comparing a sensory stimulus against the memory trace left by a previous stimulus. What is the neuronal substrate for such comparisons and the resulting decisions? This question was studied by recording neuronal responses in a variety of cortical areas of awake monkeys ( Macaca mulatta ), trained to carry out a vibrotactile sequential discrimination task. We describe methods to analyse responses obtained during the comparison and decision phases of the task, and describe the resulting findings from recordings in secondary somatosensory cortical area (S2). A subset of neurons in S2 become highly correlated with the monkey's decision in the task.

scholarly journals Distinct Spiking Patterns of Excitatory and Inhibitory Neurons and LFP Oscillations in Prefrontal Cortex During Sensory Discrimination

2021 ◽  
Vol 12 ◽  
Author(s):  
Hua-an Tseng ◽  
Xue Han
Keyword(s):  
Prefrontal Cortex ◽  
Discrimination Task ◽  
Field Potential ◽  
Inhibitory Neurons ◽  
Sensory Stimuli ◽  
Sensory Discrimination ◽  
Related Information ◽  
Entire Duration ◽  
Excitatory Neurons ◽  
Spike Waveforms

Prefrontal cortex (PFC) are broadly linked to various aspects of behavior. During sensory discrimination, PFC neurons can encode a range of task related information, including the identity of sensory stimuli and related behavioral outcome. However, it remains largely unclear how different neuron subtypes and local field potential (LFP) oscillation features in the mouse PFC are modulated during sensory discrimination. To understand how excitatory and inhibitory PFC neurons are selectively engaged during sensory discrimination and how their activity relates to LFP oscillations, we used tetrode recordings to probe well-isolated individual neurons, and LFP oscillations, in mice performing a three-choice auditory discrimination task. We found that a majority of PFC neurons, 78% of the 711 recorded individual neurons, exhibited sensory discrimination related responses that are context and task dependent. Using spike waveforms, we classified these responsive neurons into putative excitatory neurons with broad waveforms or putative inhibitory neurons with narrow waveforms, and found that both neuron subtypes were transiently modulated, with individual neurons’ responses peaking throughout the entire duration of the trial. While the number of responsive excitatory neurons remain largely constant throughout the trial, an increasing fraction of inhibitory neurons were gradually recruited as the trial progressed. Further examination of the coherence between individual neurons and LFPs revealed that inhibitory neurons exhibit higher spike-field coherence with LFP oscillations than excitatory neurons during all aspects of the trial and across multiple frequency bands. Together, our results demonstrate that PFC excitatory neurons are continuously engaged during sensory discrimination, whereas PFC inhibitory neurons are increasingly recruited as the trial progresses and preferentially coordinated with LFP oscillations. These results demonstrate increasing involvement of inhibitory neurons in shaping the overall PFC dynamics toward the completion of the sensory discrimination task.

Primate striate and prestriate cortical neurons during discrimination. II. separable temporal codes for color and pattern

1996 ◽  
Vol 75 (1) ◽  
pp. 496-507 ◽  
Author(s):  
J. W. McClurkin ◽  
J. A. Zarbock ◽  
L. M. Optican
Keyword(s):  
Cortical Neurons ◽  
Cortical Area ◽  
Temporal Distribution ◽  
Model Parameters ◽  
Single Neurons ◽  
Neuronal Responses ◽  
Training Set ◽  
Cortical Areas ◽  
Area 5 ◽  
Test Sets

1. In the previous paper we reported our analysis of the responses of neurons in cortical areas V1, V2, and V4 to a set of stimuli that consisted of all 36 combinations of six colors and six patterns. Neurons in all three cortical areas simultaneously encoded information about both the color and pattern of the stimulus in the number and temporal distribution of spikes in their responses. To account for this ability, we propose that a neuron's response consists of separable temporal codes representing the color and pattern of the stimulus that are multiplexed together. 2. We used nonlinear regression to fit the model parameters to the data. We used the responses to 30 of the 36 stimuli as a training set to estimate the parameters of the model and the responses to the remaining 6 stimuli as a test set. After training, the model fitted the responses to stimuli in the training sets very well and predicted the responses to stimuli in the test sets. Thus neuronal responses to colored patterns contain separate temporal codes representing color and pattern. 3. After establishing the model parameters, we obtained the waveforms that represented each neuron's temporal codes for the six colors and six patterns of our stimulus set. We then proceeded with a series of analyses to determine whether these waveforms were viable candidates for neuronal codes. Cluster analysis revealed that there were only a few different classes of waveforms representing each color and pattern, and there were many neurons in each class. Further, neurons that used similar waveforms to represent one color or pattern also tended to use similar waveforms to represent other colors or patterns. The waveforms representing five of the six colors and three of the six patterns were similar in the two monkeys used in this study. 4. We compared the shapes of the code waveforms across cortical areas and found no differences among areas in the shapes of the waveforms representing four of the six colors. In contrast, we found that there were differences among areas in the shapes of the waveforms representing all six patterns. These results suggest that messages about color are encoded at an early level and are then propagated upward, but that messages about pattern are altered in each successive cortical area. 5. Our results offer a neurophysiological explanation for the psychophysical evidence that color and form are processed by different channels. We propose that the psychophysical channels for color and pattern arise from the separability of the temporal codes for color and pattern in the responses of single neurons. This hypothesis implies that psychophysical channels correspond to classes of temporal codes rather than to classes of neurons.

Increased attention enhances both behavioral and neuronal performance

Science ◽  
1988 ◽  
Vol 240 (4850) ◽  
pp. 338-340 ◽  
Author(s):  
H Spitzer ◽  
R Desimone ◽  
J Moran
Keyword(s):  
Visual Discrimination ◽  
Rhesus Monkeys ◽  
Cortical Area ◽  
Task Difficulty ◽  
Discrimination Task ◽  
Control Experiment ◽  
Single Cells ◽  
Neuronal Responses ◽  
Area V4 ◽  
Behavioral Evidence

Single cells were recorded from cortical area V4 of two rhesus monkeys (Macaca mulatta) trained on a visual discrimination task with two levels of difficulty. Behavioral evidence indicated that the monkeys' discriminative abilities improved when the task was made more difficult. Correspondingly, neuronal responses to stimuli became larger and more selective in the difficult task. A control experiment demonstrated that changes in general arousal could not account for the effects of task difficulty on neuronal responses. It is concluded that increasing the amount of attention directed toward a stimulus can enhance the responsiveness and selectivity of the neurons that process it.

scholarly journals Neuronal responses in the cortical area MSTd during smooth pursuit and ocular following eye movements

2009 ◽  
Vol 10 (S1) ◽  
Author(s):  
Lukas Brostek ◽  
Seiji Ono ◽  
Michael J Mustari ◽  
Ulrich Nuding ◽  
Ulrich Büttner ◽  
...  
Keyword(s):  
Eye Movements ◽  
Smooth Pursuit ◽  
Cortical Area ◽  
Neuronal Responses ◽  
Ocular Following

Neuronal Responses Related to Smooth Pursuit Eye Movements in the Periarcuate Cortical Area of Monkeys

1998 ◽  
Vol 80 (1) ◽  
pp. 28-47 ◽  
Author(s):  
Masaki Tanaka ◽  
Kikuro Fukushima
Keyword(s):  
Eye Movements ◽  
Smooth Pursuit ◽  
Cortical Area ◽  
Eye Position ◽  
Smooth Pursuit Eye Movements ◽  
Neuronal Responses ◽  
Stationary Target ◽  
Pursuit Eye Movements ◽  
Preferred Direction ◽  
Eye Velocity

Tanaka, Masaki and Kikuro Fukushima. Neuronal responses related to smooth pursuit eye movements in the periarcuate cortical area of monkeys. J. Neurophysiol. 80: 28–47, 1998. To examine how the periarcuate area is involved in the control of smooth pursuit eye movements, we recorded 177 single neurons while monkeys pursued a moving target in the dark. The majority (52%, 92/177) of task-related neurons responded to pursuit but had little or no response to saccades. Histological reconstructions showed that these neurons were located mainly in the posterior bank of the arcuate sulcus near the sulcal spur. Twenty-seven percent (48/177) changed their activity at the onset of saccades. Of these, 36 (75%) showed presaccadic burst activity with strong preference for contraversive saccades. Eighteen (10%, 18/177) were classified as eye-position–related neurons, and 11% (19/177) were related to other aspects of the stimuli or response. Among the 92 neurons that responded to pursuit, 85 (92%) were strongly directional with uniformly distributed preferred directions. Further analyses were performed in these directionally sensitive pursuit-related neurons. For 59 neurons that showed distinct changes in activity around the initiation of pursuit, the median latency from target motion was 96 ms and that preceding pursuit was −12 ms, indicating that these neuron can influence the initiation of pursuit. We tested some neurons by briefly extinguishing the tracking target ( n = 39) or controlling its movement with the eye position signal ( n = 24). The distribution of the change in pursuit-related activity was similar to previous data for the dorsomedial part of the medial superior temporal neurons ( Newsome et al. 1988) , indicating that pursuit-related neurons in the periarcuate area also carry extraretinal signals. For 22 neurons, we examined the responses when the animals reversed pursuit direction to distinguish the effects of eye acceleration in the preferred direction from oppositely directed eye velocity. Almost all neurons discharged before eye velocity reached zero, however, only nine neurons discharged before the eyes were accelerated in the preferred direction. The delay in neuronal responses relative to the onset of eye acceleration in these trials might be caused by suppression from oppositely directed pursuit velocity. The results suggest that the periarcuate neurons do not participate in the earliest stage of eye acceleration during the change in pursuit direction, although most of them may participate in the early stages of pursuit initiation in the ordinary step-ramp pursuit trials. Some neurons changed their activity when the animals fixated a stationary target, and this activity could be distinguished easily from the strong pursuit-related responses. Our results suggest that the periarcuate pursuit area carries extraretinal signals and affects the premotor circuitry for smooth pursuit.

scholarly journals Statistical Comparison of Spike Responses to Natural Stimuli in Monkey Area V1 With Simulated Responses of a Detailed Laminar Network Model for a Patch of V1

2011 ◽  
Vol 105 (2) ◽  
pp. 757-778 ◽  
Author(s):  
Malte J. Rasch ◽  
Klaus Schuch ◽  
Nikos K. Logothetis ◽  
Wolfgang Maass
Keyword(s):  
Network Model ◽  
Network Models ◽  
Cortical Network ◽  
Inhibitory Neurons ◽  
Sensory Stimuli ◽  
Natural Stimuli ◽  
Cortical Areas ◽  
Model Response ◽  
Spiking Activity

A major goal of computational neuroscience is the creation of computer models for cortical areas whose response to sensory stimuli resembles that of cortical areas in vivo in important aspects. It is seldom considered whether the simulated spiking activity is realistic (in a statistical sense) in response to natural stimuli. Because certain statistical properties of spike responses were suggested to facilitate computations in the cortex, acquiring a realistic firing regimen in cortical network models might be a prerequisite for analyzing their computational functions. We present a characterization and comparison of the statistical response properties of the primary visual cortex (V1) in vivo and in silico in response to natural stimuli. We recorded from multiple electrodes in area V1 of 4 macaque monkeys and developed a large state-of-the-art network model for a 5 × 5-mm patch of V1 composed of 35,000 neurons and 3.9 million synapses that integrates previously published anatomical and physiological details. By quantitative comparison of the model response to the “statistical fingerprint” of responses in vivo, we find that our model for a patch of V1 responds to the same movie in a way which matches the statistical structure of the recorded data surprisingly well. The deviation between the firing regimen of the model and the in vivo data are on the same level as deviations among monkeys and sessions. This suggests that, despite strong simplifications and abstractions of cortical network models, they are nevertheless capable of generating realistic spiking activity. To reach a realistic firing state, it was not only necessary to include both N -methyl-d-aspartate and GABAB synaptic conductances in our model, but also to markedly increase the strength of excitatory synapses onto inhibitory neurons (>2-fold) in comparison to literature values, hinting at the importance to carefully adjust the effect of inhibition for achieving realistic dynamics in current network models.

scholarly journals Ant colonies outperform individuals when a sensory discrimination task is difficult but not when it is easy

2013 ◽  
Vol 110 (34) ◽  
pp. 13769-13773 ◽  
Author(s):  
Takao Sasaki ◽  
Boris Granovskiy ◽  
Richard P. Mann ◽  
David J. T. Sumpter ◽  
Stephen C. Pratt
Keyword(s):  
Discrimination Task ◽  
Ant Colonies ◽  
Sensory Discrimination

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.

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