An extensive dataset of spiking activity to reveal the syntax of the ventral stream

Abstract

Visual neuroscience benefits from high-quality datasets with neuronal responses to many images. Several neuroimaging datasets were published in recent years, but no comparable dataset with spiking activity exists. Here, we introduce the THINGS Ventral-stream Spiking Dataset (TVSD). We extensively sampled neuronal activity in response to >25,000 natural images from the THINGS database in macaques, using high-channel-count implants in three key cortical regions: V1, V4, and the inferotemporal cortex. We showcase the utility of TVSD by using an artificial neural network to visualize the tuning of neurons. We also characterize the correlated fluctuations in activity within and between areas and demonstrate that these noise correlations are strongest between neurons with similar tuning. The TVSD allows researchers to answer many questions about neuronal tuning, the interactions within and between cortical regions and to compare spiking activity in monkeys to human neuroimaging data.

Full text

An extensive dataset of spiking ac2vity to reveal the syntax of the
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1. Department of Vision & Cognition, Netherlands Institute for Neuroscience (KNAW), 1105 BA
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Amsterdam, Netherlands.
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2. Department of Integrative Neurophysiology, VU University, De Boelelaan 1085, 1081 HV
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Amsterdam, Netherlands.
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3. Department of Neurosurgery, Academic Medical Centre, Postbus 22660, 1100 DD Amsterdam,
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4. Laboratory of Visual Brain Therapy, Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17
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Monkey N
V1/V1
V4/V4
IT/IT
V1/V4
V1/IT
V4/IT
Spontanous noise correlation
0.19
0.16
0.09
0.16
0.17
0.14
Peak noise correlation
0.11
0.10
0.09
0.11
0.11
0.09
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0.35
0.12
0.14
0.27
0.27
0.12
Monkey F
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0.11
0.17
0.13
0.11
0.11
0.12
Peak noise correlation
0.07
0.07
0.08
0.07
0.07
0.07
Signal correlation
0.19
0.28
0.21
0.17
0.16
0.20
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𝑓(𝑡)=*𝑑∙exp*(𝜇𝛼+0.5𝜎1𝛼1−𝛼𝑡)∙(𝐺
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