Sreedhar S. Kumar

• PhD
• PostDoc Position at ETH Zurich

Self-sustained recurrent activity in cortical networks is thought to be important for multiple crucial processes, including circuit development and homeostasis. Yet, the precise relationship between synaptic input patterns and the spiking output of individual neurons remains largely unresolved. Here, we developed, validated and applied a novel in v...

Electrical impedance spectroscopy (EIS) is a promising label-free tool for high-throughput analysis of 3D cellular constructs, also called spheroids. Here, we used an EIS platform featuring facing electrodes to characterize the viability of hepatic spheroids, which are used for bioprinting applications. By using principal component analysis (PCA),...

A growing consensus that the brain is a mechanosensitive organ is driving the need for tools that mechanically stimulate and simultaneously record the electrophysiological response of neurons within neuronal networks. Here we introduce a synchronized combination of atomic force microscopy, high-density microelectrode array and fluorescence microsco...

We present a high-resolution impedance imaging and electrophysiological recording platform and demonstrate its capabilities with brain slices. The platform is easy to operate featuring an efficient data acquisition system and user-friendly software that runs on a host computer. The data acquisition platform relies on an FPGA system that enables bid...

We present a novel method for inferring connectivity from large-scale neuronal networks with synchronous activity. Our approach leverages Dynamic Differential Covariance to address the associated computational challenges. First, we analyze spike trains generated from Leaky Integrate-and-Fire network simulations and evaluate the performance of sever...

Self-sustained recurrent activity in cortical networks is thought to be important for multiple crucial processes, including circuit development and homeostasis. Yet, the precise relationship between the synaptic input patterns and the spiking output of individual neurons remains largely unresolved. Here, we developed, validated and applied a novel...

Self-sustained recurrent activity in cortical networks is thought to be important for multiple crucial processes, including circuit development and homeostasis. Yet, the precise relationship between the synaptic input patterns and the spiking output of individual neurons remains largely unresolved. Here, we developed, validated and applied a novel...

Self-sustained recurrent activity in cortical networks is thought to be important for multiple crucial processes, including circuit development and homeostasis. However, the precise relationship between synaptic input patterns and spiking output of individual neurons remains unresolved during spontaneous network activity. Here, using whole-network...

Despite being composed of highly plastic neurons with extensive positive feedback, the nervous system maintains stable overall function. To keep activity within bounds, it relies on a set of negative feedback mechanisms that can induce stabilizing adjustments and that are collectively termed “homeostatic plasticity.” Recently, a highly excitable mi...

Objective: Techniques to identify monosynaptic connections between neurons have been vital for neuroscience research, facilitating important advancements concerning network topology, synaptic plasticity, and synaptic integration, among others. Approach: Here, we introduce a novel approach to identify and monitor monosynaptic connections using high-...

Spike sorting is an essential procedure to extract single-neuron spiking activity from extracellular electrical recordings, which is a mixture of signals from multiple neurons. To assess the performance of spike sorting algorithms, ground-truth data – where the true spiking times of individual underlying neurons are known – are necessary. However,...

Techniques to identify monosynaptic connections between neurons have been vital for neuroscience research, facilitating important advancements concerning network topology, synaptic plasticity, and synaptic integration, among others. Here, we introduce a novel approach to identify and monitor monosynaptic connections using high-resolution dendritic...

[This corrects the article DOI: 10.1371/journal.ppat.1010118.].

Antiphospholipid antibodies (aPL), assumed to cause antiphospholipid syndrome (APS), are notorious for their heterogeneity in targeting phospholipids and phospholipid-binding proteins. The persistent presence of Lupus anticoagulant and/or aPL against cardiolipin and/or β2-glycoprotein I have been shown to be independent risk factors for vascular th...

In extracellular neural electrophysiology, individual spikes have to be assigned to their cell of origin in a procedure called "spike sorting". Spike sorting is an unsupervised problem, since no ground-truth information is generally available. Here, we focus on improving spike sorting performance, particularly during periods of high synchronous act...

Antiphospholipid antibodies (aPL), assumed to cause antiphospholipid syndrome (APS), are notorious for their heterogeneity and detect phospholipids and phospholipid-binding proteins. The persistent presence of Lupus anticoagulant and/or aPL against cardiolipin and/or β2 glycoprotein I have been shown to be independent risk factors for vascular thro...

In article number 2000223, Silvia Ronchi, Michele Fiscella, and co‐workers show neurons plated on a high‐density microelectrode array. The small electrode size and the tight spacing between the 26 400 electrodes enable functional extracellular electrophysiological characterization of neurons across scales, from subcellular‐resolution features, like...

Recent advances in the field of cellular reprogramming have opened a route to studying the fundamental mechanisms underlying common neurological disorders. High‐density microelectrode‐arrays (HD‐MEAs) provide unprecedented means to study neuronal physiology at different scales, ranging from network through single‐neuron to subcellular features. In...

Recent advances in the field of cellular reprogramming have opened a route to study the fundamental mechanisms underlying common neurological disorders. High-density microelectrode-arrays (HD-MEAs) provide unprecedented means to study neuronal physiology at different scales, ranging from network through single-neuron to subcellular features. In thi...

Targeted interaction with networks in the brain holds immense therapeutic potential as a clinical technique for the treatment of neurological disorders like epilepsy and Parkinson's Disease. Aided by technological advances, electrical stimulation of the brain is increasingly explored as a therapeutic strategy. Most current approaches involve contin...

Targeted interaction with networks in the brain is of immense therapeutic relevance. The highly dynamic nature of neuronal networks and changes with progressive diseases create an urgent need for closed-loop control. Without adequate mathematical models of such complex networks, however, it remains unclear how tractable control problems can be form...

Electrical stimulation of the brain is increasingly used to alleviate the symptoms of a range of neurological disorders and as a means to artificially inject information into neural circuits in neuroprosthetic applications. Machine learning has been proposed to find optimal stimulation settings autonomously. However, this approach is impeded by the...

Motivation Electrical stimulation of the brain is increasingly used as a strategy to alleviate the symptoms of a range of neurological disorders, and as a possible means to artificially inject information into neural circuits, e.g. towards bidirectional neural prostheses [1]. Conventionally, stimulation of neuronal networks explicitly or implicitly...

Delay differential equations arewidely adopted in life sciences: including delays explicitly in mathematical models allows to simulate the systems under investigation more accurately, without the use of auxiliary fictitious compartments. This work deals with Delay Differential Equation (DDE) models exploited in the specific framework of the glucose...

Behaviors, from simple to most complex, require a two-way interaction between brain and the environment and the contribution of different brain areas depending on the orchestrated activation of neuronal assemblies. We used cultured networks coupled to Micro Electrode Arrays - MEAs as a simplified model system to investigate the computational proper...

Information coding in the central nervous system is still, under many aspects, a mystery. In this work, we made use of cortical and hippocampal cultures plated on micro-electrode arrays and embedded in a hybrid neuro-robotic platform to investigate the basis of "sensory" coding in neuronal cell assemblies. First, we asked which features of the obse...

A glucose control problem is considered, with the aim to regulate a basal hyperglycemic state down to a safe euglycemic level. A discrete Delay Differential Equation (DDE) model of the glucose-insulin system is considered, that properly takes into account also the pancreatic insulin release, not negligible in Type 2 diabetic patients. Insulin is su...