Cooperative and Competitive Interactions Facilitate Stereo Computations in Macaque Primary Visual Cortex

Samonds, Jason M.; Potetz, Brian; Lee, Tai Sing

doi:10.1523/jneurosci.2305-09.2009

Cited by 32 publications

(90 citation statements)

References 65 publications

(121 reference statements)

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“…Zhou et al (2008) showed that stimulusevoked gamma coherence between SUAs in V1 developed slowly over 500 ms after cue onset, and discussed that this slow development of the coherence is likely attributable to the network activity of surrounding neurons. Samonds et al (2009) demonstrated that spike-timing correlation in V1 showed temporally discrete early and late increase in response to the visual stimulus, and discussed that the circuits underlying these early and late increase in the neuronal interactions might be local and distant, respectively. Therefore, the observed backward causality might be the result of the interactions between the recorded neurons and their surrounding network of neurons responding to the same stimulus.…”

Section: Possible Mechanisms Underlying the Directed Interactionsmentioning

confidence: 99%

Triphasic Dynamics of Stimulus-Dependent Information Flow between Single Neurons in Macaque Inferior Temporal Cortex

Hirabayashi¹,

Takeuchi²,

Tamura³

et al. 2010

J. Neurosci.

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The functional connectivity between cortical neurons is not static and is known to exhibit contextual modulations in terms of the coupling strength. Here we hypothesized that the information flow in a cortical local circuit exhibits complex forward-and-back dynamics, and conducted Granger causality analysis between the neuronal spike trains that were simultaneously recorded from macaque inferior temporal (IT) cortex while the animals performed a visual object discrimination task. Spikes from neuron pairs with a displaced peak on the cross-correlogram (CCG) showed Granger causality in the gamma-frequency range (30 -80 Hz) with the dominance in the direction consistent with the CCG peak (forward direction). Although, in a classical view, the displaced CCG peak has been interpreted as an indicative of a pauci-synaptic serial linkage, temporal dynamics of the gamma Granger causality after stimulus onset exhibited a more complex triphasic pattern,withatransientforwardcomponentfollowedbyaslowlydevelopingbackwardcomponentandsubsequentreappearanceoftheforward component. These triphasic dynamics of causality were not explained by the firing rate dynamics and were not observed for cell pairs that exhibited a center peak on the CCG. Furthermore, temporal dynamics of Granger causality depended on the feature configuration within the presented object. Together, these results demonstrate that the classical view of functional connectivity could be expanded to incorporate more complex forward-and-back dynamics and also imply that multistage processing in the recognition of visual objects might be implemented by multiphasic dynamics of directional information flow between single neurons in a local circuit in the IT cortex.

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Section: Possible Mechanisms Underlying the Directed Interactionsmentioning

confidence: 99%

Triphasic Dynamics of Stimulus-Dependent Information Flow between Single Neurons in Macaque Inferior Temporal Cortex

Hirabayashi¹,

Takeuchi²,

Tamura³

et al. 2010

J. Neurosci.

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show abstract

“…Recording with multiple electrodes, we found that neurons with antagonistic tuning along the same feature dimension at the same location exhibited negative correlation (competitive interaction) in their spiking activity (Samonds et al 2008). Moreover, this behavior was evident for multiple cue dimensions.…”

Section: Encoding Correlation In Tuning Propertiesmentioning

confidence: 78%

“…Moreover, this behavior was evident for multiple cue dimensions. Neurons that exhibited competitive interactions tended to be different not only in disparity tuning, but these pairs also preferred opposite directions of motion as well (Samonds et al 2008). Figures 4A,D show the receptive field locations and tuning relationships of a typical pair of neurons that exhibit competitive interaction.…”

Section: Encoding Correlation In Tuning Propertiesmentioning

confidence: 99%

“…D, Disparity tuning for neuronal pair described in A. E, Population summary of disparity tuning for same pairs described in B. F, Population summary of sharpened disparity tuning after competitive interaction described in B. Adapted from Samonds et al (2008).…”

Section: Encoding Correlation In Tuning Propertiesmentioning

confidence: 99%

“…This suggests that for each location or hypercolumn, an entire population of neurons, each representing a potential solution, might represent a probability distribution of likely solutions. This would be analogous to beliefs at a node in a graphical model of a Bayes net (Rao 2004, Potetz andLee 2008). The firing rate of a particular neuron denotes the 'importance' or probability of an estimate leading to the correct inference, and the entropy of activities of the population can capture the uncertainty or confidence of the current inference.…”

Section: Encoding Correlation In Tuning Propertiesmentioning

confidence: 99%

See 2 more Smart Citations

Neural Encoding of Scene Statistics for Surface and Object Inference

Lee¹,

Stepleton²,

Potetz³

et al. 2009

Object Categorization

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Abstract:Features associated with an object or its surfaces in natural scenes tend to vary coherently in space and time. In psychological literature, these coherent covariations have been considered to be important for neural systems to acquire models of objects and object categories. From a statistical inference perspective, such coherent covariation can provide a mechanism to learn the statistical priors in natural scenes that are useful for probabilistic inference. In this article, we present some neurophysiological experimental observations in the early visual cortex that provide insights on how correlation structures in visual scenes are being encoded by neuronal tuning and connections among neurons. The key insight is that correlated structures in visual scenes result in correlated neuronal activities, which shapes the tuning properties of individual neurons and the connections between them, embedding Gestalt-related computational constraints or priors for surface inference. Extending these concepts to the inferotemporal cortex suggests a representational framework that is distinct from traditional feed-forward hierarchy of invariant object representation and recognition. In this framework, lateral connections among view-based neurons, learned from the temporal association of the object views observed over time, can form a linked graph structure with local dependency, akin to a dense aspect graph in computer vision. This web-like graph allows view-invariant object representation to be created using sparse feed-forward connections, while maintaining the explicit representation of the different views. Thus, it can serve as an effective prior model for generating predictions of future incoming views to facilitate object inference.

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Adjusted regularization of cortical covariance

Vinci

Ventura

2018

J Comput Neurosci

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It is now common to record dozens to hundreds or more neurons simultaneously, and to ask how the network activity changes across experimental conditions. A natural framework for addressing questions of functional connectivity is to apply Gaussian graphical modeling to neural data, where each edge in the graph corresponds to a non-zero partial correlation between neurons. Because the number of possible edges is large, one strategy for estimating the graph has been to apply methods that aim to identify large sparse effects using an [Formula: see text] penalty. However, the partial correlations found in neural spike count data are neither large nor sparse, so techniques that perform well in sparse settings will typically perform poorly in the context of neural spike count data. Fortunately, the correlated firing for any pair of cortical neurons depends strongly on both their distance apart and the features for which they are tuned. We introduce a method that takes advantage of these known, strong effects by allowing the penalty to depend on them: thus, for example, the connection between pairs of neurons that are close together will be penalized less than pairs that are far apart. We show through simulations that this physiologically-motivated procedure performs substantially better than off-the-shelf generic tools, and we illustrate by applying the methodology to populations of neurons recorded with multielectrode arrays implanted in macaque visual cortex areas V1 and V4.

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Cooperative and Competitive Interactions Facilitate Stereo Computations in Macaque Primary Visual Cortex

Cited by 32 publications

References 65 publications

Triphasic Dynamics of Stimulus-Dependent Information Flow between Single Neurons in Macaque Inferior Temporal Cortex

Triphasic Dynamics of Stimulus-Dependent Information Flow between Single Neurons in Macaque Inferior Temporal Cortex

Neural Encoding of Scene Statistics for Surface and Object Inference

Adjusted regularization of cortical covariance

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