scholarly journals High baseline activity in inferior temporal cortex improves neural and behavioral discriminability during visual categorization

2014 ◽  
Vol 8 ◽  
Author(s):  
Nazli Emadi ◽  
Reza Rajimehr ◽  
Hossein Esteky
Keyword(s):  
Temporal Cortex ◽  
Inferior Temporal Cortex ◽  
High Baseline ◽  
Visual Categorization ◽  
Baseline Activity

scholarly journals Comparison of Primate Prefrontal and Premotor Cortex Neuronal Activity during Visual Categorization

2011 ◽  
Vol 23 (11) ◽  
pp. 3355-3365 ◽  
Author(s):  
Jason A. Cromer ◽  
Jefferson E. Roy ◽  
Timothy J. Buschman ◽  
Earl K. Miller
Keyword(s):  
Premotor Cortex ◽  
Temporal Cortex ◽  
Motor Planning ◽  
Brain Regions ◽  
Inferior Temporal Cortex ◽  
Decision Variable ◽  
Final Decision ◽  
Visual Categorization ◽  
Category Information ◽  
Cognitive Problem

Previous work has shown that neurons in the PFC show selectivity for learned categorical groupings. In contrast, brain regions lower in the visual hierarchy, such as inferior temporal cortex, do not seem to favor category information over information about physical appearance. However, the role of premotor cortex (PMC) in categorization has not been studied, despite evidence that PMC is strongly engaged by well-learned tasks and reflects learned rules. Here, we directly compare PFC neurons with PMC neurons during visual categorization. Unlike PFC neurons, relatively few PMC neurons distinguished between categories of visual images during a delayed match-to-category task. However, despite the lack of category information in the PMC, more than half of the neurons in both PFC and PMC reflected whether the category of a test image did or did not match the category of a sample image (i.e., had match information). Thus, PFC neurons represented all variables required to solve the cognitive problem, whereas PMC neurons instead represented only the final decision variable that drove the appropriate motor action required to obtain a reward. This dichotomy fits well with PFC's hypothesized role in learning arbitrary information and directing behavior as well as the PMC's role in motor planning.

scholarly journals Responses of Neurons in Inferior Temporal Cortex During Memory-Guided Visual Search

1998 ◽  
Vol 80 (6) ◽  
pp. 2918-2940 ◽  
Author(s):  
Leonardo Chelazzi ◽  
John Duncan ◽  
Earl K. Miller ◽  
Robert Desimone
Keyword(s):  
Visual Search ◽  
Visual Field ◽  
Visual Processing ◽  
Behavioral Response ◽  
Target Stimulus ◽  
Temporal Cortex ◽  
Inferior Temporal Cortex ◽  
Top Down ◽  
Baseline Activity ◽  
Stimulus Matching

Chelazzi, Leonardo, John Duncan, Earl K. Miller, and Robert Desimone. Responses of neurons in inferior temporal cortex during memory-guided visual search. J. Neurophysiol. 80: 2918–2940, 1998. A typical scene will contain many different objects, few of which are relevant to behavior at any given moment. Thus attentional mechanisms are needed to select relevant objects for visual processing and control over behavior. We examined this role of attention in the inferior temporal cortex of macaque monkeys, using a visual search paradigm. While the monkey maintained fixation, a cue stimulus was presented at the center of gaze, followed by a blank delay period. After the delay, an array of two to five choice stimuli was presented extrafoveally, and the monkey was rewarded for detecting a target stimulus matching the cue. The behavioral response was a saccadic eye movement to the target in one version of the task and a lever release in another. The array was composed of one “good” stimulus (effective in driving the cell when presented alone) and one or more “poor” stimuli (ineffective in driving the cell when presented alone). Most cells showed higher delay activity after a good stimulus used as the cue than after a poor stimulus. The baseline activity of cells was also higher preceding a good cue, if the animal expected it to occur. This activity may depend on a top-down bias in favor of cells coding the relevant stimulus. When the choice array was presented, most cells showed suppressive interactions between the stimuli as well as strong attention effects. When the choice array was presented in the contralateral visual field, most cells initially responded the same, regardless of which stimulus was the target. However, within 150–200 ms of array onset, responses were determined by the target stimulus. If the target was the good stimulus, the response to the array became equal to the response to the good stimulus presented alone. If the target was a poor stimulus, the response approached the response to that stimulus presented alone. Thus the influence of the nontarget stimulus was eliminated. These effects occurred well in advance of the behavioral response. When the array was positioned with stimuli on opposite sides of the vertical meridian, the contralateral stimulus appeared to dominate the response, and this dominant effect could not be overcome by attention. Overall, the results support a “biased competition” model of attention, according to which 1) objects in the visual field compete for representation in the cortex, and 2) this competition is biased in favor of the behaviorally relevant object by virtue of “top-down” feedback from structures involved in working memory.

Learning selective top-down control enhances performance in a visual categorization task

2012 ◽  
Vol 108 (11) ◽  
pp. 3124-3137 ◽  
Author(s):  
Mario Pannunzi ◽  
Guido Gigante ◽  
Maurizio Mattia ◽  
Gustavo Deco ◽  
Stefano Fusi ◽  
...  
Keyword(s):  
Time Course ◽  
Signal To Noise Ratio ◽  
Temporal Cortex ◽  
Inferior Temporal Cortex ◽  
Feedback Signal ◽  
Top Down ◽  
Categorization Task ◽  
Tuning Curves ◽  
Visual Categorization ◽  
Neural Activities

We model the putative neuronal and synaptic mechanisms involved in learning a visual categorization task, taking inspiration from single-cell recordings in inferior temporal cortex (ITC). Our working hypothesis is that learning the categorization task involves both bottom-up, ITC to prefrontal cortex (PFC), and top-down (PFC to ITC) synaptic plasticity and that the latter enhances the selectivity of the ITC neurons encoding the task-relevant features of the stimuli, thereby improving the signal-to-noise ratio. We test this hypothesis by modeling both areas and their connections with spiking neurons and plastic synapses, ITC acting as a feature-selective layer and PFC as a category coding layer. This minimal model gives interesting clues as to properties and function of the selective feedback signal from PFC to ITC that help solving a categorization task. In particular, we show that, when the stimuli are very noisy because of a large number of nonrelevant features, the feedback structure helps getting better categorization performance and decreasing the reaction time. It also affects the speed and stability of the learning process and sharpens tuning curves of ITC neurons. Furthermore, the model predicts a modulation of neural activities during error trials, by which the differential selectivity of ITC neurons to task-relevant and task-irrelevant features diminishes or is even reversed, and modulations in the time course of neural activities that appear when, after learning, corrupted versions of the stimuli are input to the network.

Learning Increases Stimulus Salience in Anterior Inferior Temporal Cortex of the Macaque

2001 ◽  
Vol 86 (1) ◽  
pp. 290-303 ◽  
Author(s):  
Bharathi Jagadeesh ◽  
Leonardo Chelazzi ◽  
Mortimer Mishkin ◽  
Robert Desimone
Keyword(s):  
Working Memory ◽  
Temporal Cortex ◽  
Poor Response ◽  
Neural Representation ◽  
Inferior Temporal Cortex ◽  
Neuronal Responses ◽  
Recording Session ◽  
Baseline Activity ◽  
Incorrect Stimulus ◽  
The Difference

With experience, an object can become behaviorally relevant and thereby quickly attract our interest when presented in a visual scene. A likely site of these learning effects is anterior inferior temporal (aIT) cortex, where neurons are thought to participate in the filtering of irrelevant information out of complex visual displays. We trained monkeys to saccade consistently to one of two pictures in an array, in return for a reward. The array was constructed by pairing two stimuli, one of which elicited a good response from the cell when presented alone (“good” stimulus) and the other of which elicited a poor response (“poor” stimulus). The activity of aIT cells was recorded while monkeys learned to saccade to either the good or poor stimulus in the array. We found that neuronal responses to the array were greater (before the saccade occurred) when training reinforced a saccade to the good stimulus than when training reinforced a saccade to the poor stimulus. This difference was not present on incorrect trials, i.e., when saccades to the incorrect stimulus were made. Thus the difference in activity was correlated with performance. The response difference grew over the course of the recording session, in parallel with the improvement in performance. The response difference was not preceded by a difference in the baseline activity of the cells, unlike what was found in studies of cued visual search and working memory in aIT cortex. Furthermore, we found similar effects in a version of the task in which any of 10 possible pairs of stimuli, prelearned before the recording session, could appear on a given trial, thereby precluding a working memory strategy. The results suggest that increasing the behavioral significance of a stimulus through training alters the neural representation of that stimulus in aIT cortex. As a result, neurons responding to features of the relevant stimulus may suppress neurons responding to features of irrelevant stimuli.

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