Biology subjects

Rongala, U. B.

Publications and source records attributed to Rongala, U. B..

4 recordsLinked to original sources

bioRxiv2024

Sensory dependencies rapidly and autonomously yield generalizable representations in recurrent cortical-like networks

How biological brains become operational from development and onwards is an unresolved issue. Here we explored the emerging effects in a brain circuit model that received simulated touches through biological skin model, which featured the potentially critical aspect of sensory dependencies resulting from mechanical coupling across the tissue. Our skin model system was connected to a recurrent network a generic, primitive cortical-like subnetwork that was composed of fully connected excitatory and inhibitory neurons, where each individual synapse was subject to continuous, independent, Hebbian-like learning. We used continuous random mechanical activations of the skin model, in this regard mimicking behavioral patterns of early brain development observed in infants, and let the neuron-independent learning define the function that emerged in the network. Remarkably, we found that the network could rapidly learn to separate various naive dynamic skin inputs and solve a kinematics task it had never encountered, even when substantial parts of the sensor population or even network connections were removed post-training. We propose that autonomous learning from a sensor population with intrinsic dependencies could cause the extensively recursive cortical network to gradually adapt its intrinsic dynamics to better mirror the various dynamics of whatever body it is connected to, which result in many biologically useful features for the early acquisition of brain circuitry function.

neuroscience↗

bioRxiv2023

EEG covariance patterns detects global cortical activity distribution following weak tactile input

Many studies have suggested that the neocortex operates as a global network of functionally interconnected neurons, indicating that any sensory input could shift activity distributions across the whole brain. A tool assessing the activity distribution across cortical regions with high temporal resolution could then potentially detect subtle changes that may pass unnoticed in regionalized analyses. We used eight-channel, distributed electrocorticogram (ECoG) recordings to analyze changes in global activity distribution caused by single pulse electrical stimulations of the paw. We analyzed the temporally evolving covariance patterns of the activity distributions using principal component analysis (PCA). We found that localized tactile stimulation caused clearly measurable changes in global ECoG activity distribution. These changes in signal covariance patterns were detectable across a small number of ECoG channels, even when not including the somatosensory cortex, suggesting that the method has high sensitivity, potentially making it applicable to human EEG for detection of pathological changes.

neuroscience↗

bioRxiv2021

The dynamics of touch sensing studied in a mass-spring-damper model of the skin

The mammalian skin is a densely innervated soft tissue where embedded neural mechanoreceptors report to the brain the mechanical events arising at its surface. Up to now, models of the transformations of these events into neural signals relied on quasistatic or viscoelastic mechanical models of the skin. Here, we employed a model which, in addition to elasticity and viscosity, accounted for mass to accurately reproduce the propagation of mechanical waves observed in vivo. Skin dynamics converted sensory inputs into rapidly evolving spatiotemporal patterns that magnified the information made available to a population of mechanoreceptors. Accounting for dynamics greatly enhanced the separability of tactile inputs and was efficient for a large range of mechanical parameter values. This advantage vanished when these parameters were set to approximate the quasistatic or viscoelastic cases.

neuroscience↗

bioRxiv2020

Neuronal leak within recurrent neuronal networks prevents the generation of high frequency spurious signals

Recurrent circuitry components are distributed widely within the brain, including both excitatory and inhibitory synaptic connections. Recurrent neuronal networks have potential stability problems, perhaps a predisposition to epilepsy. More generally, instability risks making internal representations of information unreliable. To assess the inherent stability properties of such recurrent networks, we tested a linear summation, non-spiking neuron model with and without a dynamic leak, corresponding to the low-pass filtering of synaptic input current by the RC circuit of the biological membrane. We first show that the output of this neuron model, in either of its two forms, follows its input at a higher fidelity than a wide range of spiking neuron models across a range of input frequencies. Then we constructed fully connected recurrent networks with equal numbers of excitatory and inhibitory neurons and randomly distributed weights across all synapses. When the networks were driven by pseudorandom sensory inputs with varying frequency, the recurrent network activity tended to induce high frequency self-amplifying components, sometimes evident as distinct transients, which were not present in the input data. The addition of a dynamic leak based on known membrane properties consistently removed such spurious high frequency noise across all networks. Furthermore, we found that the neuron model with dynamic leak imparts a network stability that seamlessly scales with the size of the network, conduction delays, the input density of the sensory signal and a wide range of synaptic weight distributions. Our findings suggest that neuronal dynamic leak serves the beneficial function of protecting recurrent neuronal circuitry from the self-induction of spurious high frequency signals, thereby permitting the brain to utilize this architectural circuitry component regardless of network size or recurrency. Author SummaryIt is known that neurons of the brain are extensively interconnected, which can result in many recurrent loops within its neuronal network. Such loops are prone to instability. Here we wanted to explore the potential noise and instability that could result in recurrently connected neuronal networks across a range of conditions. To facilitate such simulations, we developed a non-spiking neuron model that captures the main characteristics of conductance-based neuron models of Hodgkin-Huxley type, but is more computationally efficient. We found that a so-called dynamic leak, which is a natural consequence of the way the membrane of the neuron is constructed and how the neuron integrates synaptic inputs, provided protection against spurious, high frequency noise that tended to arise in our recurrent networks of varying size. We propose that this linear summation model provides a stable and useful tool for exploring the computational behavior of recurrent neural networks.

neuroscience↗