Biology subjects

Lieber, J. D.

Publications and source records attributed to Lieber, J. D..

8 recordsLinked to original sources

bioRxiv2024

Foundations of visual form selectivity for neurons in macaque V1 and V2

Neurons early in the primate visual cortical pathway generate responses by combining signals from other neurons: some from downstream areas, some from within the same area, and others from areas upstream. Here we develop a model that selectively combines afferents derived from a population model of V1 cells. We use this model to account for responses we recorded of both V1 and V2 neurons in awake fixating macaque monkeys to stimuli composed of a sparse collection of locally oriented features ("droplets") designed to drive subsets of V1 neurons. The first stage computes the rectified responses of a fixed population of oriented filters at different scales that cover the visual field. The second stage computes a weighted combination of these first-stage responses, followed by a final nonlinearity, with parameters optimized to fit data from physiological recordings and constrained to encourage sparsity and locality. The fitted model accounts for the responses of both V1 and V2 neurons, capturing an average of 43% of the explainable variance for V1 and 38% for V2. The models fitted to droplet recordings predict responses to classical stimuli, such as gratings of different orientations and spatial frequencies, as well as to textures of different spectral content, which are known to be especially effective in driving V2. The models are less effective, however, at capturing the selectivity of responses to textures that include naturalistic image statistics. The pattern of afferents -- defined by their weights over the 4 dimensions of spatial position, orientation, and spatial frequency -- provides a common and interpretable characterization of the origin of many neuronal response properties in the early visual cortex.

neuroscience↗

bioRxiv2024

Responses of neurons in macaque V4 to object and texture images

Humans and monkeys can rapidly recognize objects in everyday scenes. It is not fully understood where this computation arises, but previous work suggests selectivity for object shape first emerges in cortical area V4. To explore the mechanisms of this selectivity, we generated images on a continuum between "scrambled" textures and photographic images, preserving the local statistics of the original image while discarding structural information about scene and shape. We defined this continuum by the size of the region in which statistics were measured ("pooling region"). On average, neurons responded equally to photographic images and their scrambled counterparts. However, neurons exhibited a greater dynamic range of response to photographs. As a result, neuronal populations in V4 could more reliably discriminate between photographs than between scrambled images. Responses to partially scrambled images were more similar to responses to fully scrambled images than to photographs, even for perceptually subtle changes, and only began to resemble responses to photographs for pooling regions roughly half the size of typical V4 receptive fields. These same patterns emerged in an image similarity metric designed to predict human judgements of image degradation. Finally, V4 object selectivity showed dynamics that grew slowly and persisted following response offset, suggesting this signal may arise from recurrent mechanisms. Significance StatementObject recognition is a primary goal of visual processing, but is too complex to be computed by early visual areas. To evaluate where object-selective signals emerge, we recorded the responses of individual neurons in mid-level area V4 as macaque monkeys viewed images of objects ("things") and of matched textures ("stuff"). Object selective V4 responses emerged in two phases. Short-latency responses discriminate objects from one another, but the distinction between objects and textures arrived later. This late-emerging signal was sensitive even to small image degradations, as is human perception. Our results show how V4 initiates the brains representation of visual objects.

neuroscience↗

bioRxiv2022

Sensitivity to naturalistic texture relies primarily on high spatial frequencies

Natural images contain information at multiple spatial scales. Although we understand how early visual mechanisms split multi-scale images into distinct spatial frequency channels, we do not know how the outputs of these channels are processed further by mid-level visual mechanisms. We have recently developed a naturalness discrimination task that uses synthesized, multi-scale textures to isolate these mid-level mechanisms (Freeman et. al. 2013). Here, we use three experimental manipulations (image blur, image rescaling, and eccentric viewing) to show that naturalness sensitivity is strongly dependent on image features at high object spatial frequencies (measured in cycles/image). As a result, sensitivity depends on a texture acuity limit, a property of the visual system that sets the highest retinal spatial frequency (measured in cycles/degree) that can be used to solve the task. A model observer analysis shows that high object spatial frequencies carry more task-relevant information than low object spatial frequencies. Comparing the outcome of this analysis with human performance reveals that human observers efficiency is similar for all object spatial frequencies. We conclude that the mid-level mechanisms that underlie naturalness sensitivity effectively extract information from all image features below the texture acuity limit, regardless of their retinal and object spatial frequency.

neuroscience↗

bioRxiv2022

Texture Coding In Higher Order Somatosensory Cortices

AO_SCPLOWBSTRACTC_SCPLOWOur sense of touch confers to us the ability to perceive textural features over a broad range of spatial scales and material properties, giving rise to a complex sensory experience. To understand the neural basis of texture perception requires that the responses of somatosensory neurons be probed with stimuli that tile the space of spatial scales and material properties experienced during everyday interactions with objects. We have previously shown that neurons in early stages of somatosensory processing - the nerves and somatosensory cortex (S1) - are highly sensitive to texture and carry a representation of texture that is highly informative about the surface but also predicts the evoked sensory experience. In contrast, the texture signals in higher order areas - secondary somatosensory cortex (S2) and the parietal ventral area (PV) - have never been investigated with a rich and naturalistic textural set. To fill this gap, we recorded single-unit activity in S2/PV of macaques while they performed a texture discrimination task. We then characterized the neural responses to texture and compared these to their counterparts in somatosensory cortex (S1). We found that the representation of texture in S2/PV differs markedly from its counterpart in S1. In particular, S2/PV neurons carry a much sparser representation of texture identity and also information about task variables, including the animals eventual perceptual decision. S2/PV thus seems to carry a labile representation of texture that reflects task demands rather than faithfully encoding the stimulus.

neuroscience↗

bioRxiv2021

Texture is encoded in precise temporal spiking patterns in primate somatosensory cortex

AO_SCPLOWBSTRACTC_SCPLOWWe are exquisitely sensitive to the microstructure and material properties of surfaces. In the peripheral nerves, two separate mechanisms convey texture information: coarse textural features are encoded in spatial patterns of activation that reflect their spatial layout and fine features are encoded in highly repeatable, texture specific temporal spiking patterns evoked as the skin moves across the surface. In the present study, we examined whether this temporal code is preserved in the responses of neurons in somatosensory cortex. To this end, we scanned a diverse set of everyday textures across the fingertip of awake macaques while recording the responses evoked in individual cortical neurons. We found that temporal spiking patterns are highly repeatable across multiple presentations of the same texture, with millisecond precision. As a result, texture identity can be reliably decoded from the temporal patterns themselves, even after information carried in the spike rates is eliminated. However, the combination of rate and timing does is more informative than either code in isolation. The temporal precision of the texture response is heterogenous across cortical neurons and depends on the submodality composition of their input and on their location along the somatosensory neuraxis. Furthermore, temporal spiking patterns in cortex dilate and contract with decreases and increases in scanning speed and this systematic relationship between speed and patterning may contribute to the observed perceptual invariance to speed. Finally, we find that the quality of a texture percept can be better predicted when these temporal patterns are taken into consideration. We conclude that high-precision spike timing complements rate-based signals to encode texture in somatosensory cortex.

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bioRxiv2019

Texture-Elicited Vibrations Stretch Or Contract Systematically Depending On Scanning Speed

To sense the texture of a surface, we run our fingers across it, which leads to the elicitation of skin vibrations that depend both on the surface and on exploratory parameters, particularly scanning speed. The transduction and processing of these vibrations mediates the ability to discern fine surface features. In the present study, we seek to characterize the effect of changes in scanning speed on texture-elicited vibrations to better understand how the exploratory strategy shapes the neuronal representation of texture. To this end, we scanned a variety of textures across the fingertip of human participants at a variety of speeds (10 - 160 mm/s) while measuring the resulting vibrations using a laser Doppler vibrometer. We found that increases in speed led to systematic increases in vibratory intensity and to a systematic upward multiplicative shift in the frequency composition of the vibrations. Furthermore, we showed that the upward shift in frequency composition accounts for the increase in intensity. The enhancement of higher frequency components accounts for the observed increase in the firing rates of nerve fibers, particularly Pacinian corpuscle-associated fibers, which are most sensitive at the high frequencies.

neuroscience↗

bioRxiv2019

Emergence of an invariant representation of texture in primate somatosensory cortex

A major function of sensory processing is to achieve neural representations of objects that are stable across changes in context and perspective. Small changes in exploratory behavior can lead to large changes in signals at the sensory periphery, thus resulting in ambiguous neural representations of objects. Overcoming this ambiguity is a hallmark of human object recognition across sensory modalities. Here, we investigate how the perception of tactile texture remains stable across exploratory movements of the hand, including changes in scanning speed, despite the concomitant changes in afferent responses. To this end, we scanned a wide range of everyday textures across the fingertips of Rhesus macaques at multiple speeds and recorded the responses evoked in tactile nerve fibers and somatosensory cortical neurons. We found that individual cortical neurons exhibit a wider range of speed-sensitivities than do nerve fibers. The resulting representations of speed and texture in cortex are more independent than are their counterparts in the nerve and account for speed-invariant perception of texture. We demonstrate that this separation of speed and texture information is a natural consequence of previously described cortical computations.

neuroscience↗

bioRxiv2018

High-dimensional representation of texture in the somatosensory cortex of primates

In the somatosensory nerves, the tactile perception of texture is driven by spatial and temporal patterns of activation distributed across three populations of afferents. These disparate streams of information must then be integrated centrally to achieve a unified percept of texture. To investigate the representation of texture in somatosensory cortex, we scanned a wide range of natural textures across the fingertips of Rhesus macaques and recorded the responses evoked in Brodmann's areas 3b, 1, and 2. We found that texture identity is reliably encoded in the idiosyncratic responses of populations of cortical neurons, giving rise to a high-dimensional representation of texture. Cortical neurons fall along a continuum in their sensitivity to fine vs. coarse texture, and neurons at the extrema of this continuum seem to receive their major input from different afferent populations. Finally, we show that cortical responses can account for several aspects of texture perception in humans.

neuroscience↗