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Biology subjects

Hartmann, T. S.

Publications and source records attributed to Hartmann, T. S..

3 recordsLinked to original sources

Brain Feature Maps Reveal Progressive Animal-Feature Representations in the Ventral Stream.

What are the fundamental units of representation in the primate visual brain? While objects have become an intuitive framework for studying neurons in many parts of cortex, it is possible that neurons follow a more expressive organizational principle, such as encoding generic features present across textures, places, and objects. In this study, we used multi-electrode arrays to record from neurons in early (V1/V2), middle (V4), and late (posterior inferotemporal cortex (PIT)) areas across the visual hierarchy, estimating the local operation of each neuron over the entire visual scene. These estimates, called "heatmaps," approximate the weight sharing operation of convolutional neural networks. We found that while populations of neurons across V1, V4, and PIT responded over the full scene, they focused on salient sub-regions within object outlines. The best captured object feature class belonged to animals, not general objects, as a trend that increased over the visual hierarchy. These results show that the monkey ventral stream is partially organized to encode local animal features over objects, even as early as primary visual cortex.

neuroscience↗

Cortico-cortical feedback from V2 exerts a powerful influence over the visually evoked local field potential and associated spike timing in V1

The local field potential (LFP) is generally thought to be dominated by synaptic activity within a few hundred microns of the recording electrode. The sudden onset of a visual stimulus causes a large downward deflection of the LFP recorded in primary visual cortex, known as a visually evoked potential (VEP), followed by rhythmic oscillations in the gamma range (30-80 Hz) that are often in phase with action potentials of nearby neurons. By inactivating higher visual areas that send feedback projections to V1, we produced a large decrease in amplitude of the VEP, and a strong attenuation of gamma rhythms in both the LFP and multi-unit activity, despite an overall increase in neuronal spike rates. Our results argue that much of the recurrent, rhythmic activity measured in V1 is strongly gated by feed-back from higher areas, consistent with models of coincidence detection that result in burst firing by layer 5 pyramidal neurons.

neuroscience↗

Evolving super stimuli for real neurons using deep generative networks

Finding the best stimulus for a neuron is challenging because it is impossible to test all possible stimuli. Here we used a vast, unbiased, and diverse hypothesis space encoded by a generative deep neural network model to investigate neuronal selectivity in inferotemporal cortex without making any assumptions about natural features or categories. A genetic algorithm, guided by neuronal responses, searched this space for optimal stimuli. Evolved synthetic images evoked higher firing rates than even the best natural images and revealed diagnostic features, independently of category or feature selection. This approach provides a way to investigate neural selectivity in any modality that can be represented by a neural network and challenges our understanding of neural coding in visual cortex.\n\nHighlightsO_LIA generative deep neural network interacted with a genetic algorithm to evolve stimuli that maximized the firing of neurons in alert macaque inferotemporal and primary visual cortex.\nC_LIO_LIThe evolved images activated neurons more strongly than did thousands of natural images.\nC_LIO_LIDistance in image space from the evolved images predicted responses of neurons to novel images.\nC_LI

neuroscience↗