Biology subjects

Koulakov, A. A.

Publications and source records attributed to Koulakov, A. A..

8 recordsLinked to original sources

bioRxiv2021

Wiring logic of the early rodent olfactory system revealed by high-throughput sequencing of single neuron projections

The structure of neuronal connectivity often provides insights into the relevant stimulus features, such as spatial location, orientation, sound frequency, etc1-6. The olfactory system, however, appears to lack structured connectivity as suggested by reports of broad and distributed connections both from the olfactory bulb to the piriform cortex7-22 and within the cortex23-25. These studies have inspired computational models of circuit function that rely on random connectivity26-33. It remains, nonetheless, unclear whether the olfactory connectivity contains spatial structure. Here, we use high throughput anatomical methods (MAPseq and BARseq)34-38 to analyze the projections of 5,309 bulb and 30,433 piriform cortex output neurons in the mouse at single-cell resolution. We identify previously unrecognized spatial organization in connectivity along the anterior-posterior axis (A-P) of the piriform cortex. We find that both the bulb projections to the cortex and the cortical outputs are not random, but rather form gradients along the A-P axis. Strikingly, these gradients are matched: bulb neurons targeting a given location within the piriform cortex co-innervate extra-piriform regions that receive strong inputs from neurons within that piriform locus. We also identify signatures of local connectivity in the piriform cortex. Our findings suggest an organizing principle of matched direct and indirect olfactory pathways that innervate extra-piriform targets in a coordinated manner, thus supporting models of information processing that rely on structured connectivity within the olfactory system.

neuroscience↗

bioRxiv2021

Spatiotemporal 3D image registration for mesoscale studies of brain development.

Comparison of brain samples representing different developmental stages often necessitates registering the samples to common coordinates. Although the available software tools are successful in registering 3D images of adult brains, registration of perinatal brains remains challenging due to rapid growth-dependent morphological changes and variations in developmental pace between animals. To address these challenges, we propose a multi-step algorithm for the registration of perinatal brains. First, we optimized image preprocessing to increase the algorithms sensitivity to mismatches in registered images. Second, we developed an attention-gated simulated annealing (Monte Carlo) procedure capable of focusing on the differences between perinatal brains. Third, we applied classical multidimensional scaling (CMDS) to align ("synchronize") brain samples in time, accounting for individual development paces. We tested this multi-step algorithm on 28 samples of whole-mounted perinatal mouse brains (P0 - P9) and observed accurate registration results. Our computational pipeline offers a runtime of several minutes per brain on a personal computer and automates brain registration tasks including mapping brain data to atlases, comparison of averaged experimental groups, and monitoring brain development dynamics.

neuroscience↗

bioRxiv2018

DeepNose: Using artificial neural networks to represent the space of odorants

The olfactory system employs an ensemble of odorant receptors (ORs) to sense odorants and to derive olfactory percepts. We trained artificial neural networks to represent the chemical space of odorants and used that representation to predict human olfactory percepts. We hypothesized that ORs may be considered 3D spatial filters that extract molecular features and can be trained using conventional machine learning methods. First, we trained an autoencoder, called DeepNose, to deduce a low-dimensional representation of odorant molecules which were represented by their 3D spatial structure. Next, we tested the ability of DeepNose features in predicting physical properties and odorant percepts based on 3D molecular structure alone. We found that despite the lack of human expertise, DeepNose features led to perceptual predictions of comparable accuracy to molecular descriptors often used in computational chemistry. We propose that DeepNose network can extract de novo chemical features predictive of various bioactivities and can help understand the factors influencing the composition of ORs ensemble.

neuroscience↗

bioRxiv2018

Computational algorithms and neural circuitry for compressed sensing in the mammalian main olfactory bulb

A major challenge for many sensory systems is the representation of stimuli that vary along many dimensions. This problem is particularly acute for chemosensory systems because they require sensitivity to a large number of molecular features. Here we use a combination of computational modeling and in vivo electrophysiological data to propose a solution for this problem in the circuitry of the mammalian main olfactory bulb. We model the input to the olfactory bulb as an array of chemical features that, due to the vast size of chemical feature space, is sparsely occupied. We propose that this sparseness enables compression of the chemical feature array by broadly-tuned odorant receptors. Reconstruction of stimuli is then achieved by a supernumerary network of inhibitory granule cells. The main olfactory bulb may therefore implement a compressed sensing algorithm that presents several advantages. First, we demonstrate that a model of synaptic interactions between the granule cells and the mitral cells that constitute the output of the olfactory bulb, can store a highly efficient representation of odors by competitively selecting a sparse basis set of \"expert\" granule cells. Second, we further show that this model network can simultaneously learn separable representations of each component of an odor mixture without exposure to those components in isolation. Third, our model is capable of independent and odor-specific adaptation, which could be used by the olfactory system to perform background subtraction or sensitively compare a sample odor with an internal expectation. This model makes specific predictions about the dynamics of granule cell activity during learning. Using in vivo electrophysiological recordings, we corroborate these predictions in an experimental paradigm that stimulates memorization of odorants.

neuroscience↗

bioRxiv2018

Primacy model and the evolution of the olfactory receptor repertoire

Understanding sensory processing relies on establishing a consistent relationship between the stimulus space, its neural representation, and perceptual quality. In olfaction, the difficulty in establishing these links lies partly in the complexity of the underlying odor input space and perceptual responses. Based on the recently proposed primacy code for concentration invariant odor identity representation and a few assumptions, we have developed a theoretical framework for mapping the odor input space to the response properties of olfactory receptors. We analyze a geometrical structure containing odor representations in a multidimensional space of receptor affinities and describe its low-dimensional implementation, the primacy hull. We propose the implications of the primacy hull for the structure of feedforward connectivity in early olfactory networks. We test the predictions of our theory by comparing the existing receptor-ligand affinity and connectivity data obtained in the fruit fly olfactory system. We find that the Kenyon cells of the insect mushroom body integrate inputs from the high-affinity (primacy) sets of olfactory receptors in agreement with the primacy theory.

neuroscience↗

bioRxiv2018

A computational framework for converting high-throughput DNA sequencing data into neural circuit connectivity.

There is growing interest in determining the connectivity of neural circuits-the "connectome"-at single neuron resolution. Most approaches to circuit mapping rely on either microscopy or physiology, but these approaches have very limited throughput. We have recently proposed BOINC (Barcoding of Individual Neuronal Connectivity), a radically different approach to connectivity mapping based on high-throughput DNA sequencing. Here we describe the set of computational algorithms that serve to convert sequencing data into neural connectivity. We apply our computational pipeline to the results of proof-of-principle experiments illustrating an implementation of BOINC based on pseudorabies virus (PRV). PRV is capable of traversing individual synapses and carry DNA barcodes from one cell to another. Using this high-throughput sequencing data, we obtain 456-by-486 connectivity matrix between putative neurons. An inexpensive high-throughput technique for establishing circuit connectivity at single neuron resolution would represent a major advance in neuroscience.

neuroscience↗

bioRxiv2017

Concentration invariant odor coding

Humans can identify visual objects independently of view angle and lighting, words independently of volume and pitch, and smells independently of concentration. The computational principles underlying invariant object recognition remain mostly unknown. Here we propose that, in olfaction, a small and relatively stable set made of the earliest activated receptors forms a code for concentration invariant odor identity. One prediction of this \"primacy coding\" scheme is that decisions based on odor identity can be made solely using early odor-evoked neural activity. Using an optogenetic masking paradigm, we define the sensory integration time necessary for odor identification and demonstrate that animals can use information occurring <100 ms after inhalation onset to identify odors. Using multi-electrode array recordings of odor responses in the olfactory bulb, we find that concentration invariant units respond earliest and at latencies that are within this behaviorally-defined time window. We propose a computational model demonstrating how such a code can be read by neural circuits of the olfactory system.

neuroscience↗

bioRxiv2017

Network cloning using DNA barcodes

The connections between neurons determine the computations performed by both artificial and biological neural networks. Recently, we have proposed SYNseq, a method for converting the connectivity of a biological network into a form that can exploit the tremendous efficiencies of high-throughput DNA sequencing. In SYNseq, each neuron is tagged with a random sequence of DNA--a \"barcode\"--and synapses are represented as barcode pairs. SYNseq addresses the analysis problem, reducing a network into a suspension of barcode pairs. Here we formulate a novel and complementary synthesis problem: How can the suspension of barcode pairs be used to \"clone\" or copy the network back into an uninitialized tabula rasa network? Although this synthesis problem might be expected to be computationally intractable, we find that, surprisingly, this problem can be solved efficiently, using only neuron-local information. We present the \"one barcode one cell\" (OBOC) algorithm, which forces all barcodes of a given sequence to coalesce into the same neuron, and show that it converges in a number of steps that is a power law of the network size. Rapid and reliable network cloning with single synapse precision is thus theoretically possible.

neuroscience↗