Recognition by Top-Down and Bottom-Up Processing in Cortex: The Control of Selective Attention

2003 ◽  
Vol 90 (2) ◽  
pp. 798-810 ◽  
Author(s):  
Dan Graboi ◽  
John Lisman
Keyword(s):  
Word Recognition ◽  
Visual Recognition ◽  
Visual Image ◽  
Covert Attention ◽  
Long Term Memory ◽  
Top Down ◽  
Recognition Time ◽  
Bottom Up ◽  
Cortical Areas ◽  
High Level

Visual recognition is achieved by a hierarchy of bidirectionally connected cortical areas. The entry of signals into higher areas involves the serial sampling of information within a movable window of attention. Here we explore how the cortex can move this window and integrate the sampled information. To make this concrete, we modeled the process of visual word recognition by hierarchical cortical areas representing features, letters, and words. At the start of the recognition process, nodes representing all contextually possible words are active. Simple connectivity rules allow a parallel top-down (T-D) computation of the relative probability of each feature at each location given the set of active words. This information is then used to guide the window of attention to information-rich features (e.g., a feature that is present in the visual image but has lowest probability). Bottom-up processing of this feature excludes words that do not contain it and leads to T-D recomputation of feature probabilities. Recognition occurs after several such cycles when all but one word has been excluded. We show that when 950 words are stored in long-term memory, recognition occurs after an average of 4.9 cycles. Because covert attention can be moved every 20–30 ms, word recognition could be as fast as determined experimentally (<200 ms of cortical processing). This model accounts for the findings that recognition time depends logarithmically on set size, recognition time is reduced when context reduces the number of possible targets, the time to classify a nonword decreases when its approximation to English decreases, and in high level cortex, the firing of neurons tuned to an object increases progressively as its recognition occurs. More generally the model provides a physiologically plausible view of how bi-directional signal flow in cortex guides attention to produce efficient recognition.

scholarly journals Characterization of Feedback Neurons in the High-Level Visual Cortical Areas That Project Directly to the Primary Visual Cortex in the Cat

2021 ◽  
Vol 14 ◽  
Author(s):  
Huijun Pan ◽  
Shen Zhang ◽  
Deng Pan ◽  
Zheng Ye ◽  
Hao Yu ◽  
...  
Keyword(s):  
Information Processing ◽  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Visual Information ◽  
Higher Order ◽  
Top Down ◽  
Cortical Areas ◽  
Area 21A ◽  
High Level ◽  
Visual Cortical

Previous studies indicate that top-down influence plays a critical role in visual information processing and perceptual detection. However, the substrate that carries top-down influence remains poorly understood. Using a combined technique of retrograde neuronal tracing and immunofluorescent double labeling, we characterized the distribution and cell type of feedback neurons in cat’s high-level visual cortical areas that send direct connections to the primary visual cortex (V1: area 17). Our results showed: (1) the high-level visual cortex of area 21a at the ventral stream and PMLS area at the dorsal stream have a similar proportion of feedback neurons back projecting to the V1 area, (2) the distribution of feedback neurons in the higher-order visual area 21a and PMLS was significantly denser than in the intermediate visual cortex of area 19 and 18, (3) feedback neurons in all observed high-level visual cortex were found in layer II–III, IV, V, and VI, with a higher proportion in layer II–III, V, and VI than in layer IV, and (4) most feedback neurons were CaMKII-positive excitatory neurons, and few of them were identified as inhibitory GABAergic neurons. These results may argue against the segregation of ventral and dorsal streams during visual information processing, and support “reverse hierarchy theory” or interactive model proposing that recurrent connections between V1 and higher-order visual areas constitute the functional circuits that mediate visual perception. Also, the corticocortical feedback neurons from high-level visual cortical areas to the V1 area are mostly excitatory in nature.

scholarly journals The Bipolar Logic of Feedforward and Feedback Circuits

2021 ◽  
Author(s):  
Arturo Tozzi
Keyword(s):  
Visual Recognition ◽  
Coarse Grained ◽  
William Of Ockham ◽  
Top Down ◽  
Object Persistence ◽  
Bottom Up ◽  
Bipolar Mode ◽  
External Sources ◽  
Drug Resistant Epilepsy ◽  
Work Up

Instead of the conventional 0 and 1 values, bipolar reasoning uses -1, 0, +1 to describe double-sided judgements in which neutral elements are halfway between positive and negative evaluations (e.g., &ldquo;uncertain&rdquo; lies between &ldquo;impossible&rdquo; and &ldquo;totally sure&rdquo;). We discuss the state-of-the-art in bipolar logics and recall two medieval forerunners, i.e., William of Ockham and Nicholas of Autrecourt, who embodied a bipolar mode of thought that is eminently modern. Starting from the trivial observation that &ldquo;once a wheat sheaf is sealed and tied up, the packed down straws display the same orientation&rdquo;, we work up a new theory of the bipolar nature of networks, suggesting that orthodromic (i.e., feedforward, bottom-up) projections might be functionally coupled with antidromic (i.e., feedback, top-down) projections via the mathematical apparatus of presheaves/globular sets. When an entrained oscillation such as a neuronal spike propagates from A to B, changes in B might lead to changes in A, providing unexpected antidromic effects. Our account points towards the methodological feasibility of novel neural networks in which message feedback is guaranteed by backpropagation mechanisms endowed in the same feedforward circuits. Bottom-up/top-down transmission at various coarse-grained network levels provides fresh insights in far-flung scientific fields such as object persistence, memory reinforcement, visual recognition, Bayesian inferential circuits and multidimensional activity of the brain. Implying that axonal stimulation by external sources might backpropagate and modify neuronal electric oscillations, our theory also suggests testable previsions concerning the optimal location of transcranial magnetic stimulation&rsquo;s coils in patients affected by drug-resistant epilepsy.

Orphan Implementations of Public Policy for Conflict-Oriented Issues

2021 ◽  
Vol 21 (2) ◽  
pp. 43-63
Author(s):  
Shulamith Gertel Groome
Keyword(s):  
Public Policy ◽  
Decision Making ◽  
Public Interest ◽  
Policy Process ◽  
Frame Of Reference ◽  
Limited Resources ◽  
Top Down ◽  
Bottom Up ◽  
Field Level ◽  
High Level

This paper aims to broaden our understanding of public policy characterized by issues of non-consensus. The idea of flexible, independent administrative decision-making for a conflict-oriented policy-type is addressed in terms of chronological constructions of policy process. Distributions of limited resources are a source of public contention likely to draw ambiguous high-level policy decisions that lack practical administrative directives. Conflicting institutional, professional and stakeholder influences, at various levels of policy processes, illuminate circumstances fostering implementations incongruent with politically motivated macro-declarations. Yet, this does not necessarily represent failed policy. A reevaluation of administrative systems, by critical deconstruction of the dominant top-down discourse, provides a frame of reference for valid divergent implementations. A conceptual progression from field-level interpretation and adaptation of macro policy, initiatory orphan implementations emerge as policy itself. This revised bottom-up modality of the policy process implies a working balance of combined outputs, providing equitable outcome to serve largescale public interest.

scholarly journals Bottom-up Retinotopic Organization Supports Top-down Mental Imagery

2013 ◽  
Vol 7 (1) ◽  
pp. 58-67 ◽  
Author(s):  
Ruey-Song Huang ◽  
Martin I. Sereno
Keyword(s):  
Premotor Cortex ◽  
Brain Regions ◽  
Retrosplenial Cortex ◽  
Wide Field ◽  
Top Down ◽  
Bottom Up ◽  
Retinotopic Maps ◽  
Upper Visual Field ◽  
Retinotopic Organization ◽  
High Level

Finding a path between locations is a routine task in daily life. Mental navigation is often used to plan a route to a destination that is not visible from the current location. We first used functional magnetic resonance imaging (fMRI) and surface-based averaging methods to find high-level brain regions involved in imagined navigation between locations in a building very familiar to each participant. This revealed a mental navigation network that includes the precuneus, retrosplenial cortex (RSC), parahippocampal place area (PPA), occipital place area (OPA), supplementary motor area (SMA), premotor cortex, and areas along the medial and anterior intraparietal sulcus. We then visualized retinotopic maps in the entire cortex using wide-field, natural scene stimuli in a separate set of fMRI experiments. This revealed five distinct visual streams or ‘fingers’ that extend anteriorly into middle temporal, superior parietal, medial parietal, retrosplenial and ventral occipitotemporal cortex. By using spherical morphing to overlap these two data sets, we showed that the mental navigation network primarily occupies areas that also contain retinotopic maps. Specifically, scene-selective regions RSC, PPA and OPA have a common emphasis on the far periphery of the upper visual field. These results suggest that bottom-up retinotopic organization may help to efficiently encode scene and location information in an eye-centered reference frame for top-down, internally generated mental navigation. This study pushes the border of visual cortex further anterior than was initially expected.

scholarly journals Conflicting Bottom-up and Top-down Signals during Misrecognition of Visual Objects

2019 ◽  
Author(s):  
Mohamed Abdelhack ◽  
Yukiyasu Kamitani
Keyword(s):  
Visual Information ◽  
Visual Recognition ◽  
Brain Activity ◽  
Sensory Information ◽  
Visual Input ◽  
Neural Representation ◽  
Perceptual Decision Making ◽  
Inference Process ◽  
Top Down ◽  
Bottom Up

AbstractVisual recognition involves integrating visual information with other sensory information and prior knowledge. In accord with Bayesian inference under conditions of unreliable visual input, the brain relies on the prior as a source of information to achieve the inference process. This drives a top-down process to improve the neural representation of visual input. However, the extent to which non-stimulus-driven top-down information affects processing in the ventral stream is still unclear. We conducted a perceptual decision-making task using blurred images, while conducting functional magnetic resonance imaging. We then transformed brain activity into deep neural network features to distinguish bottom-up and top-down signals. We found that top-down information unrelated to the stimulus had a minimal effect on lower-level visual processes. The neural representations of degraded stimuli that were misrecognized were still correlated with the correct object category in the lower levels of processing. In contrast, activity in the higher cognitive areas was more strongly correlated with recognition reported by the subjects. The results indicated a discrepancy between the results of processing at the lower and higher levels, indicating the existence of a stimulus-independent top-down signal flowing back down the hierarchy. These findings suggest that integration between bottom-up and top-down information takes the form of competing evidence in higher visual areas between prior-driven top-down and stimulus-driven bottom-up signals. These findings could provide important insight into the different modes of integration of neural signals in the visual cortex that contribute to the visual inference process.

scholarly journals The road to long-term memory: Top-down attention is more effective than bottom-up attention for forming long-term memories

2021 ◽  
Author(s):  
Edyta Sasin ◽  
Daryl Fougnie
Keyword(s):  
Visual Search ◽  
Attentional Capture ◽  
Long Term Memory ◽  
Top Down ◽  
Bottom Up ◽  
Term Memory ◽  
The Road ◽  
Memory Traces ◽  
Better Than

AbstractDoes the strength of representations in long-term memory (LTM) depend on which type of attention is engaged? We tested participants’ memory for objects seen during visual search. We compared implicit memory for two types of objects—related-context nontargets that grabbed attention because they matched the target defining feature (i.e., color; top-down attention) and salient distractors that captured attention only because they were perceptually distracting (bottom-up attention). In Experiment 1, the salient distractor flickered, while in Experiment 2, the luminance of the salient distractor was alternated. Critically, salient and related-context nontargets produced equivalent attentional capture, yet related-context nontargets were remembered far better than salient distractors (and salient distractors were not remembered better than unrelated distractors). These results suggest that LTM depends not only on the amount of attention but also on the type of attention. Specifically, top-down attention is more effective in promoting the formation of memory traces than bottom-up attention.

scholarly journals Bottom-Up Parser: Look-Ahead LR Parser

2019 ◽  
Vol 8 (3) ◽  
pp. 2406-2410
Keyword(s):  
Second Step ◽  
Minimization Algorithm ◽  
Machine Code ◽  
Top Down ◽  
State Problem ◽  
Bottom Up ◽  
Look Ahead ◽  
High Level ◽  
The Given ◽  
Lexical Analyzer

Compiler is used for the purpose of converting high level code to machine code. For doing this procedure we have six steps. On these steps the syntax analyses is the second step of compiler. The lexical analyzer produce token in the output. The tokens are used as input to syntax analyzer. Syntax analyzer performs parsing operation. The parsing can be used for deriving the string from the given grammar called as derivation. It depend upon how derivation will be performed either top down or bottom up. The bottom up parsers LR (Left-to-right), SLR (simple LR) has some conflicts. To remove these conflicts we use LALR (Look ahead LR parser). The conflicts are available if the state contains minimum two or more productions. If there is one shift operation in state and other one is reduce operation it means that shift-reduce operation at the same time. Then this state is called as inadequate state. This Inadequate state problem is solved in LALR parser. Other problem with other parsers is that they have more states as compared to LALR parser. So cost will be high. But in LALR parser minimum states used and cost will automatically be reduced. LALR is also called as Minimization algorithm of CLR (Canonical LR parser).

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