Yaoguang Jiang

Morphological and neurochemical comparisons between pulvinar and V1 projections to V2

The flow of visual information is clear at the earliest stages: the retina provides the driving (main signature) activity for the lateral geniculate nucleus (LGN), which in turn drives the primary visual cortex (V1). These driving pathways can be distinguished anatomically from other modulatory pathways that innervate LGN and V1. The path of visual information after V1, however, is less clear. There are two primary feedforward projections to the secondary visual cortex (V2), one from the lateral/inferior pulvinar and the other from V1. Because both lateral/inferior pulvinar and V2 cannot be driven visually following V1 removal, either or both of these inputs to V2 could be drivers. Retinogeniculate and geniculocortical projections are privileged over modulatory projections by their layer of termination, their bouton size, and the presence of vesicular glutamate transporter 2 (Vglut2) or parvalbumin (PV). It has been suggested that such properties might also distinguish drivers from modulators in extrastriate cortex. We tested this hypothesis by comparing lateral pulvinar to V2 and V1 to V2 projections with LGN to V1 projections. We found that V1 and lateral pulvinar projections to V2 are similar in that they target the same layers and lack PV. Projections from pulvinar to V2, however, bear a greater similarity to projections from LGN to V1 because of their larger boutons (measured at the same location in V2) and positive staining for Vglut2. These data lend support to the hypothesis that the pulvinar could act as a driver for V2. J. Comp. Neurol. 521:813–832, 2013.

Perceptual decision related activity in the lateral geniculate nucleus

Fundamental to neuroscience is the understanding of how the language of neurons relates to behavior. In the lateral geniculate nucleus (LGN), cells show distinct properties such as selectivity for particular wavelengths, increments or decrements in contrast, or preference for fine detail versus rapid motion. No studies, however, have measured how LGN cells respond when an animal is challenged to make a perceptual decision using information within the receptive fields of those LGN cells. In this study we measured neural activity in the macaque LGN during a two-alternative, forced-choice (2AFC) contrast detection task or during a passive fixation task and found that a small proportion (13.5%) of single LGN parvocellular (P) and magnocellular (M) neurons matched the psychophysical performance of the monkey. The majority of LGN neurons measured in both tasks were not as sensitive as the monkey. The covariation between neural response and behavior (quantified as choice probability) was significantly above chance during active detection, even when there was no external stimulus. Interneuronal correlations and task-related gain modulations were negligible under the same condition. A bottom-up pooling model that used sensory neural responses to compute perceptual choices in the absence of interneuronal correlations could fully explain these results at the level of the LGN, supporting the hypothesis that the perceptual decision pool consists of multiple sensory neurons and that response fluctuations in these neurons can influence perception.

A computational relationship between thalamic sensory neural responses and contrast perception.

Uncovering the relationship between sensory neural responses and perceptual decisions remains a fundamental problem in neuroscience. Decades of experimental and modeling work in the sensory cortex have demonstrated that a perceptual decision pool is usually composed of tens to hundreds of neurons, the responses of which are significantly correlated not only with each other, but also with the behavioral choices of an animal. Few studies, however, have measured neural activity in the sensory thalamus of awake, behaving animals. Therefore, it remains unclear how many thalamic neurons are recruited and how the information from these neurons is pooled at subsequent cortical stages to form a perceptual decision. In a previous study we measured neural activity in the macaque lateral geniculate nucleus (LGN) during a two alternative forced choice (2AFC) contrast detection task, and found that single LGN neurons were significantly correlated with the monkeys’ behavioral choices, despite their relatively poor contrast sensitivity and a lack of overall interneuronal correlations. We have now computationally tested a number of specific hypotheses relating these measured LGN neural responses to the contrast detection behavior of the animals. We modeled the perceptual decisions with different numbers of neurons and using a variety of pooling/readout strategies, and found that the most successful model consisted of about 50–200 LGN neurons, with individual neurons weighted differentially according to their signal-to-noise ratios (quantified as d-primes). These results supported the hypothesis that in contrast detection the perceptual decision pool consists of multiple thalamic neurons, and that the response fluctuations in these neurons can influence contrast perception, with the more sensitive thalamic neurons likely to exert a greater influence.

Morphological and neurochemical comparisons between pulvinar and V1 projections to V2 (pages 813–832)

There was an error on pg. 820 of this article. In the sentence, “Projections from PL were seen in the temporal, parietal, and occipital cortices (data not shown). Most of these projections were to cortical layers III and IV, except in area V1, where there were sparse projections to layers III and IV and dense projections to layer I,” “layers III and IV” were incorrectly substituted for “layers II and III”. The corrected sentence should read, “Projections from PL were seen in the temporal, parietal, and occipital cortices (data not shown). Most of these projections were to cortical layers III and IV, except in area V1, where there were sparse projections to layers II and III and dense projections to layer I.”