Biology subjects

Mysore, S. P.

Publications and source records attributed to Mysore, S. P..

11 recordsLinked to original sources

bioRxiv2025

Pooled versus structured inhibition drives competitive stimulus interactions across distinct spatial scales of the superior colliculus

Competitive stimulus interactions across the space map in the midbrain superior colliculus (SC) are essential for spatial decision-making. Here, with electrophysiological recordings in intermediate and deep layers of the mouse SC (SCid), we discovered that rules and the spatial profiles of these competitive interactions are different between local, within-RF versus global, across-RF-boundary spatial scales. When one visual stimulus was centered in the receptive field (RF) and a second one was located within the RFs classical inhibitory surround, we found that stimulus interactions followed an averaging rule. This classical surround was spatially restricted, and the strength of inhibition underlying these within-RF interactions decreased with distance from the RF center. By contrast, when the second stimulus was located outside the RF, stimulus interactions followed a divisive rule. Strikingly, this extra-classical inhibitory surround was spatially global, and the strength of competitive inhibition underlying these across-RF-boundary interactions was distance-invariant. Computational modeling revealed that whereas within-RF interactions are well-explained by a divisive normalization-like mechanism driven by pooled inhibition, across-RF-boundary interactions are not, and instead, are well-explained by a winner-take-all-like mechanism driven by structured, donut-like inhibition. Combined, our results offer insights into the mechanistic logic of stimulus competition in the SCid, which critically underlies spatial decision-making in mammals.

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bioRxiv2024

Quantifying information stored in synaptic connections rather than in firing patterns of neural networks

A cornerstone of our understanding of both biological and artificial neural networks is that they store information in the strengths of synaptic connections among the neurons. However, in contrast to the well-established theory for quantifying information encoded by the firing activity of neural networks, there does not exist a framework for quantifying information stored in the networks connection distribution itself. Here, we develop a theoretical framework for synaptic information by using densely connected Hebbian networks performing autoassociative memory tasks and by modeling data patterns to be stored as log-normal distributions. Specifically, we derive analytical approximations for Shannon mutual information between the data and single-tons, pairs, and arbitrary n-tuples of synaptic connections within the network. Our framework corroborates well-established insights regarding pattern storage capacity, supports the principle of distributed coding in neural firing activities, and formalizes the heterogeneity inherent in information encoding across synapses in a network. Notably, it discovers synergistic interactions among synapses, revealing that the information encoded jointly by all the synapses exceeds the sum of its parts. Taken together, this study introduces a powerful, interpretable framework for quantitatively understanding information storage in the synapses of neural networks, one that illustrates the duality of synaptic connectivity and neural population activity in learning and memory.

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bioRxiv2024

Contribute to balance, wire in accordance: Emergence of backpropagation from a simple, bio-plausible neuroplasticity rule

Over the past several decades, backpropagation (BP) has played a critical role in the advancement of machine learning and remains a core method in numerous computational applications. It is also utilized extensively in comparative studies of biological and artificial neural network representations. Despite its widespread use, the implementation of BP in the brain remains elusive, and its biological plausibility is often questioned due to inherent issues such as the need for symmetry of weights between forward and backward connections, and the requirement of distinct forward and backward phases of computation. Here, we introduce a novel neuroplasticity rule that offers a potential mechanism for implementing BP in the brain. Similar in general form to the classical Hebbian rule, this rule is based on the core principles of maintaining the balance of excitatory and inhibitory inputs as well as on retrograde signaling, and operates over three progressively slower timescales: neural firing, retrograde signaling, and neural plasticity. We hypothesize that each neuron possesses an internal state, termed credit, in addition to its firing rate. After achieving equilibrium in firing rates, neurons receive credits based on their contribution to the E-I balance of postsynaptic neurons through retrograde signaling. As the networks credit distribution stabilizes, connections from those presynaptic neurons are strengthened that significantly contribute to the balance of postsynaptic neurons. We demonstrate mathematically that our learning rule precisely replicates BP in layered neural networks without any approximations. Simulations on artificial neural networks reveal that this rule induces varying community structures in networks, depending on the learning rate. This simple theoretical framework presents a biologically plausible implementation of BP, with testable assumptions and predictions that may be evaluated through biological experiments.

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bioRxiv2022

Ancient midbrain inhibitory neurons control selective spatial attention

To behave adaptively in complex environments, animals must selectively process the most important information in space while ignoring distractors. Contrary to the dominant cortico-centric view, we report that an evolutionarily old group of inhibitory neurons in the brainstem, called PLTi, is critical for this function of selective spatial attention. In freely behaving mice performing a human-like spatial attention task, we found that bilateral silencing of PLTi severely disrupted target selection without causing perceptual or motor impairments. PLTis effects depended necessarily on goal-relevant, rather than just physical salience-based signals, together revealing it as a dedicated brain site for priority-driven attentional target selection. PLTis core computational function is its explicit implementation of the abstract cognitive decision boundary separating target from distracters: it controlled the accuracy and categorical precision of this decision boundary, doing so by shaping neural representations of competing stimuli in the superior colliculus, a major sensorimotor hub. PLTi may, therefore, be a conserved brainstem engine across vertebrates for winner-take-all-like spatial decisions in goal-driven behavior.

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bioRxiv2020

Neural markers of vulnerability to anxiety outcomes following traumatic brain injury

Anxiety outcomes following traumatic brain injury (TBI) are complex, and the underlying neural mechanisms are poorly understood. Here, we developed a multidimensional behavioral profiling approach to investigate anxiety-like outcomes in mice that takes into account individual variability. Departing from the tradition of comparing outcomes in TBI versus sham groups, we identified animals within the TBI group that are vulnerable to anxiety dysfunction by applying dimensionality reduction, clustering and post-hoc validation to behavioral data obtained from multiple assays for anxiety at several post-injury timepoints. These vulnerable animals expressed distinct molecular profiles in the corticolimbic network, with downregulation in GABA and glutamate, and upregulation in NPY markers. Indeed, among vulnerable animals, not resilient or sham controls, severity of anxiety outcomes correlated strongly with expression of molecular markers. Our results establish a foundational approach, with predictive power, for reliably identifying maladaptive anxiety outcomes following TBI and uncovering neural signatures of vulnerability to anxiety.

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bioRxiv2020

Distinct neural mechanisms construct classical versus extraclassical inhibitory surrounds in an inhibitory nucleus in the midbrain attention network

Inhibitory neurons in the midbrain spatial attention network, called isthmi pars magnocellularis (Imc), control stimulus selection by the sensorimotor and attentional hub, the optic tectum (OT). Here, we investigate in the barn owl how classical as well as extraclassical (global) inhibitory surrounds of Imc receptive fields (RFs), fundamental units of Imc computational function, are constructed. We find that focal, reversible blockade of GABAergic input onto Imc neurons disconnects their extraclassical inhibitory surrounds, but, surprisingly, leaves intact their classical surrounds. Subsequently, with paired recordings and iontophoresis, first at spatially aligned site-pairs in Imc and OT, and then, at mutually distant site-pairs within Imc, we demonstrate that classical inhibitory surrounds of Imc RFs are inherited from OT, but their extraclassical inhibitory surrounds are constructed within Imc. These results reveal key design principles of the midbrain spatial attention circuit, and attest to the critical importance of competitive interactions within Imc for its operation.

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bioRxiv2020

Visual psychophysics and limits of visual discrimination performance in freely behaving mice

Studying the temporal dynamics of perceptual decisions offers key insights into the cognitive processes contributing to it. Conducting such investigation in a genetically tractable animal model can facilitate the subsequent unpacking of the mechanistic basis of different stages in perceptual dynamics. Here, we investigated the time course as well as fundamental psychophysical constants governing visual perceptual decision-making in freely behaving mice. We did so by analyzing response accuracy against reaction time (i.e., conditional accuracy), in a series of 2-AFC orientation discrimination tasks in which we varied target size, luminance, duration, and presence of a foil. Our results quantified two distinct stages in the time course of mouse visual decision-making - a sensory encoding stage, in which conditional accuracy exhibits a classic tradeoff with response speed, and a subsequent short term memory-dependent stage in which conditional accuracy exhibits a classic asymptotic decay following stimulus offset. We estimated the duration of visual sensory encoding as 200-320 ms across tasks, the lower bound of the duration of short-term memory as ~1700 ms, and the briefest duration of visual stimulus input that is informative as [≤]50 ms. Separately, by varying stimulus onset delay, we demonstrated that the conditional accuracy function and RT distribution can be independently modulated, and found that the duration for which mice naturally withhold from responding is a quantitative metric of impulsivity. Taken together, our results establish a quantitative foundation for investigating the neural circuit bases of visual decision dynamics in mice. SIGNIFICANCE STATEMENTThis study presents a quantitative breakdown of the time course of visual decision-making in mice during naturalistic behavior. It demonstrates parallel stages in mouse visual perceptual decision dynamics to those in humans, estimates their durations, and shows that mice are able to discriminate well under challenging visual conditions - with stimuli that are brief, low luminance, and small. These results set the stage for investigating the neural bases of visual perceptual decision dynamics and their dysfunction in mice.

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bioRxiv2019

Neural circuit mechanism for generating categorical representations

Categorical neural responses underlie various forms of selection and decision-making. Such binary-like responses promote robust signaling of the winner in the presence of input ambiguity and neural noise. Here, we show that a donut-like inhibitory mechanism in which each competing option suppresses all options except itself, is highly effective at generating categorical neural responses. It surpasses motifs of feedback inhibition, recurrent excitation, and divisive normalization invoked frequently in decision-making models. We demonstrate experimentally not only that this mechanism operates in the midbrain spatial selection network in barn owls, but also that it is necessary for categorical signaling by it. The functional pattern of neural inhibition in the midbrain forms an exquisitely structured multi-holed donut consistent with this networks combinatorial inhibitory function for stimulus selection. Additionally, modeling reveals a generalizable neural implementation of the donut-like motif for categorical selection. Self-sparing inhibition may, therefore, be a powerful circuit module central to categorization.

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bioRxiv2019

Categorical signaling of the strongest stimulus by an inhibitory midbrain nucleus

The isthmi pars magnocellularis (Imc), a group of inhibitory neurons in the vertebrate midbrain tegmentum, orchestrates stimulus competition and spatial selection in the optic tectum (OT). Here, we investigate the properties of relative-strength dependent competitive interactions within the barn owl Imc. Imc neurons exhibit switch-like as well as gradual response profiles as a function of relative stimulus strength, do so for competing stimuli both within and across sensory modalities, and signal the strongest stimulus in a dynamically flexible manner. Notably, Imc signals the strongest stimulus more categorically (with greater precision), and earlier than the OT. Paired recordings at spatially aligned Imc and OT sites reveal that although some properties of stimulus competition are correlated, others are set independently. Our results demonstrate that the Imc is itself an active site of competition, and may be the first site in the midbrain selection network at which stimulus competition is resolved.

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bioRxiv2019

Endogenous and exogenous control of visuospatial selective attention in freely behaving mice

Selective spatial attention, the ability to dynamically prioritize the most important spatial location, is essential for adaptive behavior. It has been studied primarily in head-fixed animals, and almost exclusively in primates. Here, we report the development of two human-inspired, discrimination-based behavioral paradigms for studying selective visuospatial attention in the freely behaving mouse: the spatial probability task, and the flanker task. In the spatial probability task, we found enhanced response accuracy, perceptual discriminability, and rates of sensory evidence accumulation at the location with higher probability of target occurrence, and opposite effects at the lower probability location. In the absence of systematic differences in sensory input, motor biases, and trial structure, these results demonstrated endogenous expectation-driven shifts of spatial attention. In the flanker task, we found that a second, flanker stimulus presented with the target, but with incongruent information, caused switch-like decrements in response accuracy and perceptual discriminability as a function of flanker contrast, as well as a reduced rate of evidence accumulation. These results demonstrated exogenous capture of spatial attention. The innovation of behavioral tasks for selective visuospatial attention in unrestrained mice opens up a rich avenue for future research dissecting the neural circuit mechanisms underlying this critical executive function.

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bioRxiv2018

Combinatorial neural inhibition for stimulus selection across space

The ability to select the most salient stimulus among competing ones is essential for animal behavior and operates regardless of the spatial locations that stimuli occupy. Here, we reveal that the brain employs a combinatorially optimized strategy to solve such location-invariant stimulus selection. With experiments in a key inhibitory nucleus in the vertebrate midbrain selection network, called isthmi pars magnocellularis (Imc) in owls, we discovered that the central element is a multilobe neuron, which encodes visual locations with multiple firing fields. This multilobed coding of space is necessitated by scarcity of Imc neurons. Although distributed seemingly randomly in space, the locations of these lobes are optimized across the high firing Imc neurons, allowing them to cooperatively suppress stimuli throughout 2D visual space while minimizing metabolic and circuit wiring costs. Our work suggests that combinatorial coding of space by sparse inhibitory neurons may be a general functional module for spatial selection.

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