Biology subjects

Kohn, A.

Publications and source records attributed to Kohn, A..

11 recordsLinked to original sources

bioRxiv2026

Input-dependent directionality of interactions between cortical areas

Tracking signal flow across areas is essential for understanding brain function. Recent studies using cross-covariances show that activity directionality can shift rapidly with behavioral or task demands; yet, the circuit mechanisms underlying these changes remain unclear. Here, we use recurrent network models to investigate how directional interactions emerge and are flexibly reconfigured in multi-area cortical circuits. We show that, for fixed connectivity, directionality is shaped by how common inputs align with recurrent connectivity and the associated internal timescales of activity. In multi-area circuits with locally balanced excitation and inhibition, this reveals a predominant role for inputs to excitatory over inhibitory populations in controlling directionality. These inputs govern the directionality of the latent signals that account for most of the shared activity across areas, predominantly reflecting widespread and coherent activity fluctuations. Our models capture key features of cross-covariances from primate areas V1 and V2 and suggest parsimonious mechanisms for the shift in directionality reported in these areas. This work establishes a mechanistic framework for understanding dynamic changes in signal flow between brain areas.

neuroscience↗

bioRxiv2024

Targeted pre-conditioning and cell transplantation in the murine lower respiratory tract

Transplantation of airway basal stem cells could achieve a durable cure for genetic diseases of the airway, such as cystic fibrosis and primary ciliary dyskinesia. Recent work demonstrated the potential of primary- and pluripotent stem cell (PSC)-derived basal cells to efficiently engrai into the mouse trachea aier injury. However, there are many hurdles to overcome in translating these approaches to humans including developing safe and efficient methods for delivery in larger animal models. We propose a model which targets preconditioning and cell-delivery to the intrapulmonary airways utilizing a micro- bronchoscope for delivery. The detergent polidocanol was adapted for distal lung pre-conditioning, inducing intrapulmonary airway epithelial denudation by 5 and 24-hours post-delivery. While initial re- epithelialization of airways occurred later than tracheas, complete repair was observed within 7-days. Both PSC-derived and primary basal cells delivered via micro-bronchoscope post-polidocanol injury engraied in tracheas and intrapulmonary airways, respectively. Transplanted cells differentiated into ciliated and secretory lineages while maintaining a population of basal cells. These findings demonstrate the utility of bronchoscopically targeted pre-conditioning and cell delivery to the conducting intra- pulmonary airways, providing an important framework for pre-clinical translation of approaches for engineered airway epithelial regeneration.

cell biology↗

bioRxiv2024

The representational geometry for naturalistic textures in macaque V1 and V2

Our understanding of visual cortical processing has relied primarily on studying the selectivity of individual neurons in different areas. A complementary approach is to study how the representational geometry of neuronal populations differs across areas. Though the geometry is derived from individual neuronal selectivity, it can reveal encoding strategies difficult to infer from single neuron responses. In addition, recent theoretical work has begun to relate distinct functional objectives to different representational geometries. To understand how the representational geometry changes across stages of processing, we measured neuronal population responses in primary visual cortex (V1) and area V2 of macaque monkeys to an ensemble of synthetic, naturalistic textures. Responses were lower dimensional in V2 than V1, and there was a better alignment of V2 population responses to different textures. The representational geometry in V2 afforded better discriminability between out-of-sample textures. We performed complementary analyses of standard convolutional network models, which did not replicate the representational geometry of cortex. We conclude that there is a shift in the representational geometry between V1 and V2, with the V2 representation exhibiting features of a low-dimensional, systematic encoding of different textures and of different instantiations of each texture. Our results suggest that comparisons of representational geometry can reveal important transformations that occur across successive stages of visual processing.

neuroscience↗

bioRxiv2024

Assessing neuronal correlates of salience and their adaptability with naturalistic textures in macaque V1 and V2

Salience is critical to vision. It allows stimuli that are different from their surroundings to pop out, drawing our attention. Perceptual salience is postulated to be encoded via a saliency map, based on differences in neuronal responsivity to simple image features at different spatial locations. Simple image features such as luminance, orientation and color are known to affect saliency and many of these features are encoded in primary visual cortex (V1), which several influential theories propose instantiate a saliency map. However, the degree to which more complex image features can determine salience, and whether there are neural correlates of salience which are computed outside of V1, remains unclear. Here we use displays of naturalistic textures to test for neural correlates of salience--termed pop-out responses--in V1 and area V2 of anesthetized macaque monkeys. Sensitivity to higher-order texture statistics arises in V2, so pop-out responses for these displays, if they exist, would be expected to be computed after V1. We presented displays in which a target texture, presented within the neuronal receptive field, was surrounded by distractors. Distractors could differ from the target texture in either higher-order texture statistics only, or in both lower- and higher-order statistics. We found little evidence for pop-out signals in either V1 or V2, for either display type. However, brief periods of adaptation could induce pop-out responses in V2. This suggests that adaptation might define which features of the environment are most salient, even if those features would otherwise not evoke pop-out responses. Significance statementWe tested for neuronal correlates of salience--termed pop-out responses--using displays with multiple patches of naturalistic textures, whose higher-order statistics are encoded by neurons outside primary visual cortex. We found little evidence for pop-out responses in V1 or V2. However, brief periods of adaptation could induce pop-out responses in V2. Our results indicate that the computations that define bottom-up attention (i.e., salience) are malleable and continuously updated by our stimulus history.

neuroscience↗

bioRxiv2023

Circuit-motivated generalized affine models characterize stimulus-dependent visual cortical shared variability

Correlated variability in the visual cortex is modulated by stimulus properties. The stimulus dependence of correlated variability impacts stimulus coding and is indicative of circuit structure. An affine model combining a factor proportional to mean stimulus response and an additive offset has been proposed to explain how correlated variability in primary visual cortex (V1) depends on stimulus orientations. However, whether the affine model could be extended to explain modulations by other stimulus variables or variability shared between two brain areas is unknown. Motivated by a simple neural circuit mechanism, we modified the affine model to better explain the contrast-dependence of neural variability shared within either primary or secondary visual cortex (V1 or V2) as well as the orientation-dependence of neural variability shared between V1 and V2. Our results bridge neural circuit mechanisms and statistical models, and provide a parsimonious explanation for the stimulus-dependence of correlated variability within and between visual areas.

neuroscience↗

bioRxiv2023

Adaptation with naturalistic textures in macaque V1 and V2

Adaptation affects neuronal responsivity and selectivity throughout the visual hierarchy. However, because most prior studies have tailored stimuli to a single brain area of interest, we have a poor understanding of how exposure to a particular image alters responsivity and tuning at different stages of visual processing. Here we assess how adaptation with naturalistic textures alters neuronal responsivity and selectivity in primary visual cortex (V1) and area V2 of macaque monkeys. Neurons in both areas respond to textures, but V2 neurons are sensitive to high-order statistics which do not strongly modulate V1 responsivity. We tested the specificity of adaptation in each area with textures and spectrally-matched noise stimuli. Adaptation reduced responsivity in both V1 and V2, but only in V2 was the reduction dependent on the presence of higher-order texture statistics. Despite this specificity, the texture information provided by single neurons and populations was reduced after adaptation, in both V1 and V2. Our results suggest that adaptation effects for a given feature are induced at the stage of processing that tuning for those features first arises and that stimulus-specific adaptation effects need not result in improved sensory coding. Significance statementNearly all sensory neurons adapt to recent input. However, how the adjustment triggered by a particular input is distributed across brain areas and how these changes contribute to sensory processing are poorly understood. Here we explore adaptation with naturalistic textures, for which neurons in primary visual cortex and area V2 have differing selectivity. Adaptation reduced responsivity in both areas, but the effects in V2 alone depended on the presence of the higher-level texture statistics to which V2 neurons are sensitive. Though prior work with simpler stimuli has argued that stimulus-specific adaptation improves visual encoding, we found adaptation reduced texture information. Thus, adaptation need not improve visual encoding, suggesting its effects may serve some other purpose.

neuroscience↗

bioRxiv2021

Feature representation under crowding in V1 and V4 neuronal populations

Visual perception depends strongly on spatial context. A profound example is visual crowding, whereby the presence of nearby stimuli impairs discriminability of object features. Despite extensive work on both perceptual crowding and the spatial integrative properties of visual cortical neurons, the link between these two aspects of visual processing remains unclear. To understand better the neural basis of crowding, we recorded simultaneously from neuronal populations in V1 and V4 of fixating macaque monkeys. We assessed the information about the orientation of a visual target available from the measured responses, both for targets presented in isolation and amid distractors. Both single neuron and population responses had less information about target orientation when distractors were present. Information loss was moderate in V1 and more substantial in V4. Information loss could be traced to systematic divisive and additive changes in neuronal tuning. Tuning changes were more severe in V4; in addition, tuning exhibited greater context-dependent distortions in V4, further restricting the ability of a fixed sensory readout strategy to extract accurate feature information across changing environments. Our results provide a direct test of crowding effects at different stages of the visual hierarchy, reveal how these effects alter the spiking activity of cortical populations by which sensory stimuli are encoded, and connect these changes to established mechanisms of neuronal spatial integration.

neuroscience↗

bioRxiv2021

Disentangling the flow of signals between populations of neurons

Technological advances now allow us to record from large populations of neurons across multiple brain areas. These recordings may illuminate how communication between areas contributes to brain function, yet a substantial barrier remains: How do we disentangle the concurrent, bidirectional flow of signals between populations of neurons? We therefore propose here a novel dimensionality reduction framework: Delayed Latents Across Groups (DLAG). DLAG disentangles signals relayed in each direction, identifies how these signals are represented by each population, and characterizes how they evolve within and across trials. We demonstrate that DLAG performs well on synthetic datasets similar in scale to current neurophysiological recordings. Then we study simultaneously recorded populations in primate visual areas V1 and V2, where DLAG reveals signatures of bidirectional yet selective communication. Our framework lays a foundation for dissecting the intricate flow of signals across populations of neurons, and how this signaling contributes to cortical computation.

neuroscience↗

bioRxiv2021

Feedforward and feedback interactions between visual cortical areas use different population activity patterns

Brain function relies on the coordination of activity across multiple, recurrently connected, brain areas. For instance, sensory information encoded in early sensory areas is relayed to, and further processed by, higher cortical areas and then fed back. However, the way in which feedforward and feedback signaling interact with one another is incompletely understood. Here we investigate this question by leveraging simultaneous neuronal population recordings in early and midlevel visual areas (V1-V2 and V1-V4). Using a dimensionality reduction approach, we find that population interactions are feedforward-dominated shortly after stimulus onset and feedback-dominated during spontaneous activity. The population activity patterns most correlated across areas were distinct during feedforward- and feedback-dominated periods. These results suggest that feedforward and feedback signaling rely on separate "channels", such that feedback signaling does not directly affect activity that is fed forward.

neuroscience↗

bioRxiv2020

Neuronal variability reflects probabilistic inference tuned to natural image statistics

Neuronal activity in sensory cortex fluctuates over time and across repetitions of the same input. This variability is often considered detrimental to neural coding. The theory of neural sampling proposes instead that variability encodes the uncertainty of perceptual inferences. In primary visual cortex (V1), modulation of variability by sensory and non-sensory factors supports this view. However, it is unknown whether V1 variability reflects the statistical structure of visual inputs, as would be required for inferences correctly tuned to the statistics of the natural environment. Here we combine analysis of image statistics and recordings in macaque V1 to show that probabilistic inference tuned to natural image statistics explains the widely observed dependence between spike-count variance and mean, and the modulation of V1 activity and variability by spatial context in images. Our results show that the properties of a basic aspect of cortical responses -- their variability -- can be explained by a probabilistic representation tuned to naturalistic inputs.

neuroscience↗

bioRxiv2019

Strong information-limiting correlations in early visual areas

If the brain processes incoming data efficiently, information should degrade little between early and later neural processing stages, and so information in early stages should match behavioral performance. For instance, if there is enough information in a visual cortical area to determine the orientation of a grating to within 1 degree, and the code is simple enough to be read out by downstream circuits, then animals should be able to achieve that performance behaviourally. Despite over 30 years of research, it is still not known how efficient the brain is. For tasks involving a large number of neurons, the amount of information encoded by neural circuits is limited by differential correlations. Therefore, determining how much information is encoded requires quantifying the strength of differential correlations. Detecting them, however, is difficult. We report here a new method, which requires on the order of 100s of neurons and trials. This method relies on computing the alignment of the neural stimulus encoding direction, f', with the eigenvectors of the noise covariance matrix,{Sigma} . In the presence of strong differential correlations, f' must be spanned by a small number of the eigenvectors with largest eigenvalues. Using simulations with a leaky-integrate-and-fire neuron model of the LGN-V1 circuit, we confirmed that this method can indeed detect differential correlations consistent with those that would limit orientation discrimination thresholds to 0.5-3 degrees. We applied this technique to V1 recordings in awake monkeys and found signatures of differential correlations, consistent with a discrimination threshold of 0.47-1.20 degrees, which is not far from typical discrimination thresholds (1-2 deg). These results suggest that, at least in macaque monkeys, V1 contains about as much information as is seen in behaviour, implying that downstream circuits are efficient at extracting the information available in V1.

neuroscience↗