A normalization circuit of attention in primate lateral prefrontal cortex

The way in which visual neurons encode information pertaining to a cluttered scene with multiple stimuli, and subsequently filter behaviorally relevant information using attention remains poorly understood. Neurons of area 8a in the macaque lateral prefrontal cortex have been shown to encode visual and attentional signals. We trained two macaque monkeys in a visuospatial attention task and performed neurophysiological recordings to test how neurons in this area encode multiply presented stimuli and attentionally filter target stimuli from distractors. We found area 8a neuronal responses to several concurrently presented stimuli to resemble the average of individual responses to those stimuli when presented alone; this nonlinear response is characteristic of divisive normalization, a canonical brain computation seen to operate in various neural systems. Interestingly, the strength of normalization was dependent on visuospatial tuning, with neurons tuned for the ipsilateral visual hemifield displaying stronger normalized responses than those tuned for the contralateral hemifield. Furthermore, when presented with multiple stimuli and attending toward a target stimulus lying in the receptive field, contralateral-tuned neural activity increased and resembled that of when the target was presented alone (i.e. Winner-take-all response), whereas ipsilateral-tuned neurons were less modulated by attention and remained best-described by an average response. Taken together, our findings suggest a normalization circuit underlying attention in the primate lateral prefrontal cortex

Adaptive whitening with fast gain modulation and slow synaptic plasticity

Neurons in early sensory areas rapidly adapt to changing sensory statistics, both by normalizing the variance of their individual responses and by reducing correlations between their responses. Together, these transformations may be viewed as an adaptive form of statistical whitening. Existing mechanistic models of adaptive whitening exclusively use either synaptic plasticity or gain modulation as the biological substrate for adaptation; however, on their own, each of these models has significant limitations. In this work, we unify these approaches in a normative multi-timescale mechanistic model that adaptively whitens its responses with complementary computational roles for synaptic plasticity and gain modulation. Gains are modified on a fast timescale to adapt to the current statistical context, whereas synapses are modified on a slow timescale to match structural properties of the input statistics that are invariant across contexts. Our model is derived from a novel multi-timescale whitening objective that factorizes the inverse whitening matrix into basis vectors, which correspond to synaptic weights, and a diagonal matrix, which corresponds to neuronal gains. We test our model on synthetic and natural datasets and find that the synapses learn optimal configurations over long timescales that enable adaptive whitening on short timescales using gain modulation.Comment: NeurIPS 2023 Spotlight; 18 pages, 8 figure

Adaptive whitening in neural populations with gain-modulating interneurons

Statistical whitening transformations play a fundamental role in many computational systems, and may also play an important role in biological sensory systems. Existing neural circuit models of adaptive whitening operate by modifying synaptic interactions; however, such modifications would seem both too slow and insufficiently reversible. Motivated by the extensive neuroscience literature on gain modulation, we propose an alternative model that adaptively whitens its responses by modulating the gains of individual neurons. Starting from a novel whitening objective, we derive an online algorithm that whitens its outputs by adjusting the marginal variances of an overcomplete set of projections. We map the algorithm onto a recurrent neural network with fixed synaptic weights and gain-modulating interneurons. We demonstrate numerically that sign-constraining the gains improves robustness of the network to ill-conditioned inputs, and a generalization of the circuit achieves a form of local whitening in convolutional populations, such as those found throughout the visual or auditory systems.Comment: 20 pages, 10 figures (incl. appendix). To appear in the Proceedings of the 40th International Conference on Machine Learnin

Representational dissimilarity metric spaces for stochastic neural networks

Quantifying similarity between neural representations -- e.g. hidden layer activation vectors -- is a perennial problem in deep learning and neuroscience research. Existing methods compare deterministic responses (e.g. artificial networks that lack stochastic layers) or averaged responses (e.g., trial-averaged firing rates in biological data). However, these measures of deterministic representational similarity ignore the scale and geometric structure of noise, both of which play important roles in neural computation. To rectify this, we generalize previously proposed shape metrics (Williams et al. 2021) to quantify differences in stochastic representations. These new distances satisfy the triangle inequality, and thus can be used as a rigorous basis for many supervised and unsupervised analyses. Leveraging this novel framework, we find that the stochastic geometries of neurobiological representations of oriented visual gratings and naturalistic scenes respectively resemble untrained and trained deep network representations. Further, we are able to more accurately predict certain network attributes (e.g. training hyperparameters) from its position in stochastic (versus deterministic) shape space

Methylation of HOXA9 and ISL1 predicts patient outcome in high-grade non-invasive bladder cancer

Introduction Inappropriate DNA methylation is frequently associated with human tumour development, and in specific cases, is associated with clinical outcomes. Previous reports of DNA methylation in low/intermediate grade non-muscle invasive bladder cancer (NMIBC) have suggested that specific patterns of DNA methylation may have a role as diagnostic or prognostic biomarkers. In view of the aggressive and clinically unpredictable nature of high-grade (HG) NMIBC, and the current shortage of the preferred treatment option (Bacillus:Calmette-Guerin), novel methylation analyses may similarly reveal biomarkers of disease outcome that could risk-stratify patients and guide clinical management at initial diagnosis. Methods Promoter-associated CpG island methylation was determined in primary tumour tissue of 36 initial presentation high-grade NMIBCs, 12 low/intermediate-grade NMIBCs and 3 normal bladder controls. The genes HOXA9, ISL1, NKX6-2, SPAG6, ZIC1 and ZNF154 were selected for investigation on the basis of previous reports and/or prognostic utility in low/intermediate-grade NMIBC. Methylation was determined by Pyrosequencing of sodium-bisulphite converted DNA, and then correlated with gene expression using RT-qPCR. Methylation was additionally correlated with tumour behaviour, including tumour recurrence and progression to muscle invasive bladder cancer or metastases. Results The ISL1 genes’ promoter-associated island was more frequently methylated in recurrent and progressive high-grade tumours than their non-recurrent counterparts (60.0% vs. 18.2%, p = 0.008). ISL1 and HOXA9 showed significantly higher mean methylation in recurrent and progressive tumours compared to non-recurrent tumours (43.3% vs. 20.9%, p = 0.016 and 34.5% vs 17.6%, p = 0.017, respectively). Concurrent ISL1/HOXA9 methylation in HG-NMIBC reliably predicted tumour recurrence and progression within one year (Positive Predictive Value 91.7%), and was associated with disease-specific mortality (DSM). Conclusions In this study we report methylation differences and similarities between clinical sub-types of high-grade NMIBC. We report the potential ability of methylation biomarkers, at initial diagnosis, to predict tumour recurrence and progression within one year of diagnosis. We found that specific biomarkers reliably predict disease outcome and therefore may help guide patient treatment despite the unpredictable clinical course and heterogeneity of high-grade NMIBC. Further investigation is required, including validation in a larger patient cohort, to confirm the clinical utility of methylation biomarkers in high-grade NMIBC

A normalization circuit underlying coding of spatial attention in primate lateral prefrontal cortex

© 2019 Duong et al. Lateral prefrontal cortex (LPFC) neurons signal the allocation of voluntary attention; however, the neural computations underlying this function remain unknown. To investigate this, we recorded from neuronal ensembles in the LPFC of two Macaca fascicularis performing a visuospatial attention task. LPFC neural responses to a single stimulus were normalized when additional stimuli/distracters appeared across the visual field and were wellcharacterized by an averaging computation. Deploying attention toward an individual stimulus surrounded by distracters shifted neural activity from an averaging regime toward a regime similar to that when the attended stimulus was presented in isolation (winner-take-all; WTA). However, attentional modulation is both qualitatively and quantitatively dependent on a neuron’s visuospatial tuning. Our results show that during attentive vision, LPFC neuronal ensemble activity can be robustly read out by downstream areas to generate motor commands, and/or fed back into sensory areas to filter out distracter signals in favor of target signals

Context-dependent representations of objects and space in the primate hippocampus during virtual navigation

© 2019, The Author(s), under exclusive licence to Springer Nature America, Inc. The hippocampus is implicated in associative memory and spatial navigation. To investigate how these functions are mixed in the hippocampus, we recorded from single hippocampal neurons in macaque monkeys navigating a virtual maze during a foraging task and a context–object associative memory task. During both tasks, single neurons encoded information about spatial position; a linear classifier also decoded position. However, the population code for space did not generalize across tasks, particularly where stimuli relevant to the associative memory task appeared. Single-neuron and population-level analyses revealed that cross-task changes were due to selectivity for nonspatial features of the associative memory task when they were visually available (perceptual coding) and following their disappearance (mnemonic coding). Our results show that neurons in the primate hippocampus nonlinearly mix information about space and nonspatial elements of the environment in a task-dependent manner; this efficient code flexibly represents unique perceptual experiences and correspondent memories

Ketamine disrupts naturalistic coding of working memory in primate lateral prefrontal cortex networks

Data de publicació electrònica: 12-05-2021Ketamine is a dissociative anesthetic drug, which has more recently emerged as a rapid-acting antidepressant. When acutely administered at subanesthetic doses, ketamine causes cognitive deficits like those observed in patients with schizophrenia, including impaired working memory. Although these effects have been linked to ketamine’s action as an N-methyl-D-aspartate receptor antagonist, it is unclear how synaptic alterations translate into changes in brain microcircuit function that ultimately influence cognition. Here, we administered ketamine to rhesus monkeys during a spatial working memory task set in a naturalistic virtual environment. Ketamine induced transient working memory deficits while sparing perceptual and motor skills. Working memory deficits were accompanied by decreased responses of fast spiking inhibitory interneurons and increased responses of broad spiking excitatory neurons in the lateral prefrontal cortex. This translated into a decrease in neuronal tuning and information encoded by neuronal populations about remembered locations. Our results demonstrate that ketamine differentially affects neuronal types in the neocortex; thus, it perturbs the excitation inhibition balance within prefrontal microcircuits and ultimately leads to selective working memory deficits.We thank registered veterinary technicians Kim Thomaes and Rhonda Kersten from the University of Western Ontario for their assistance in surgery and animal care; Guillaume Doucet from the University of Ottawa for technical assistance related to Unreal Development Kit; Maryam Nouri Kadijani from the University of Western Ontario for assisting with initial data exploration; Kevin Barker from Neuronitek for engineering equipment for our experiments; Jonathan C. Lau from the Division of Neurosurgery, University Hospital for providing advice regarding surgery and surgical planning; Matthew Leavitt, AI Resident at Facebook for access to MATLAB code related to polynomial plane fitting and advice on electrophysiological analysis. This work was supported by Canadian Institute of Health Research Project Grant; Natural Sciences and Engineering Research Council of Canada (NSERC); Ontario Graduate Scholarship; Jonathan & Joshua Memorial Graduate Scholarship in Mental Health Research. Chrysalis Foundation (London, Ontario). LP acknowledges salary support from the Tanna Schulich Endowment Chair for Neuroscience and Mental Health. RMB acknowledges support from MINECO (Spain; BFU2017-85936-P), the Howard Hughes Medical Institute (HHMI, ref 55008742), and the ICREA Academia (2016)

Measuring the Confinement Free Energy and Effective Width of Single Polymer Chains via Single-Molecule Tetris

We perform single-molecule partitioning measurements of the free energy of chain confinement and self-exclusion as a function of confinement dimension and buffer ionic strength via single-molecule Tetris. Individual DNA chains, confined in a nanoslit with a lattice of embedded nanocavities, partition their contour between the cavities. Changes in device geometry and buffer chemistry lead to changes in the number of cavities occupied. We are able to deduce the confinement free energy difference between the nanocavities and the nanoslit by observing how the number of cavities occupied by a single chain varies as a function of device dimension. These measurements enable us to confirm theoretical predictions for confinement free energy based on worm-like chain formalism and quantify the chain effective width <i>w</i>, providing a direct measure of the size of excluded-volume effect on a single-chain level