Biology subjects

Strohmer, B.

Publications and source records attributed to Strohmer, B..

5 recordsLinked to original sources

bioRxiv2025

Predicting therapeutic windows for synaptic intervention in ALS

Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disease characterized by progressive breakdown of neural circuits which leads to motoneuron death. Earlier work from our lab showed that dysregulation of inhibitory V1 interneurons precedes the degeneration of excitatory V2a interneurons and motoneurons and that stabilizing V1-motoneuron connections improved motor function and saved motoneurons in the SOD1G93A ALS mouse model. However, the optimal timing for this intervention remains unclear. To address this, we developed a spiking neural network model of spinal locomotor circuits to simulate healthy and ALS-like conditions. By modeling changes in network connectivity and synaptic dynamics, we predict that V1 dysregulation induces hyperexcitation in motoneurons which is preferentially observed in flexor motoneurons leading to the disruption of flexor-extensor coordination, and potentially contributing to selective vulnerability of flexor motoneurons. Stabilizing V1 synapses preserved motor output even after motoneuron loss, suggesting that therapeutic benefit is possible into symptomatic stages. However, model predictions also highlighted that after sustained synaptic loss and the development of slower synaptic dynamics within the network, synaptic stabilization leads to maladaptive extensor-biased activity, suggesting that excitatory/inhibitory balance impacts treatment effectiveness. Finally, the model indicated that V1 stabilization could lead to rescue of the V2a excitatory interneurons, a finding that we were able to confirm experimentally in the SOD1G93A ALS mouse model. By exploring different scenarios of synaptic loss and cell dysregulation during synaptic stabilization, our models provide a framework for predicting candidate time windows for spinal circuit interventions, which may guide future preclinical investigations.

neuroscience↗

bioRxiv2025

Single-Cell and Population-Level Neuromodulation Dynamics in Dual-Electrode Intracortical Stimulation

In neuroprosthetics, intracortical microstimulation (ICMS) recruits cortical networks to evoke brain responses and sensory perceptions. However, multi-electrode ICMS often generates suboptimal percepts compared to single-electrode ICMS, suggesting nonlinear neuromodulation rather than simple summation by multi-electrode ICMS. Yet, the factors and mechanisms underlying this modulation remain poorly understood. To investigate multi-electrode ICMS, we combined two-photon calcium imaging with a well-controlled dual-electrode ICMS in the mouse visual cortex to investigate how neurons integrate converging ICMS inputs at varying intensities. We found that stimulation intensity significantly shapes neuromodulation at both single-cell and population levels. Specifically, low intensities (5-7 {micro}A) have a minimal effect on neural responses. At intermediate intensities (10-15 {micro}A), we observed diverse, nonlinear bipolar modulation--both enhancement and attenuation--at the single-cell level. However, we achieved net enhancement at the population level. At higher intensities (15-20 {micro}A), although the proportion of modulated neurons increased in both enhancement and attenuation directions, the net effect at the population level was neutral (zero modulation). Furthermore, neurons strongly responsive to single-electrode ICMS were more likely to be attenuated, while weaker responding cells exhibited enhanced modulation. The strongest neuromodulatory effects occur at intermediate spatial distances in between the two electrodes. Computational modeling based on spiking neural network composed of adaptive exponential integrate-and-field neurons implicated the importance of inhibitory network dynamics and network variability as key mechanisms. Our experimental data was used to train an advanced deep learning approach, which successfully predicted the neuromodulation patterns induced by dual-electrode ICMS. Our findings reveal intensity- and spatial-dependent rules of neuromodulation by ICMS, providing necessary insights to optimize multi-electrode ICMS for neuroprosthetic applications. Significance statementUnderstanding how cortical neurons integrate concurrent inputs from multi-electrode intracortical microstimulation (ICMS) is essential for advancing neuroprosthetic technologies. We show that dual-electrode ICMS evokes distinct, predictable neuromodulatory effects that depend on (i) stimulation intensity, (ii) a neurons baseline responsiveness to single electrode input, and (iii) its proximity to the electrodes. Low and intermediate intensity dual-electrode ICMS amplifies neural activity compared to single-electrode ICMS, whereas high-intensity stimulation leads to attenuation, limiting net activation.

neuroscience↗

bioRxiv2022

Network architecture producing swing to stance transitions in an insect walking system

The walking system of the stick insect is one of the most thoroughly described invertebrate systems. We know a lot about the role of sensory input in the control of stepping of a single leg. However, the neuronal organization and connectivity of the central neural networks underlying the rhythmic activation and coordination of leg muscles still remain elusive. It is assumed that these networks can couple in the absence of phasic sensory input due to the observation of spontaneous recurrent patterns (SRPs) of coordinated motor activity equivalent to fictive stepping-phase transitions. Here we sought to quantify the phase of motor activity within SRPs in the isolated and interconnected meso- and metathoracic ganglia. We show that SRPs occur not only in the meso-, but also in the metathoracic ganglia of the stick insect, discovering a qualitative difference between them. We construct a network based on neurophysiological data capable of reproducing the measured SRP phases to investigate this difference. By comparing network output to the biological measurements we confirm the plausibility of the architecture and provide a hypothesis to account for these qualitative differences. The neural architecture we present couples individual central pattern generators to reproduce the fictive stepping-phase transitions observed in deafferented stick insect preparations after pharmacological activation, providing insights into the neural architecture underlying coordinated locomotion.

neuroscience↗

bioRxiv2021

CloudBrain: Online neural computation in the cloud

Neuromorphic computing currently relies heavily on complicated hardware design to implement asynchronous, parallel and very large-scale brain simulations. This dependency slows down the migration of biological insights into technology. It typically takes several years from idea to finished hardware and once developed the hardware is not broadly available to the community. In this contribution, we present the CloudBrain research platform, an alternative based on modern cloud computing and event stream processing technology. Typical neuromorphic design goals, such as small form factor and low power consumption, are traded for 1) no constraints on the model elements, 2) access to all events and parameters during and after the simulation, 3) online reconfiguration of the network, and 4) real-time simulation. We explain principles for how neuron, synapse and network models can be implemented and we demonstrate that our implementation can be used to control a physical robot in real-time. CloudBrain is open source and can run on commodity hardware or in the cloud, thus providing the community a new platform with a different set of features supporting research into, for example, neuron models, structural plasticity and three-factor learning.

neuroscience↗

bioRxiv2020

Integrating Non-Spiking Interneurons in Spiking Neural Networks

Researchers working with neural networks have historically focused on either non-spiking neurons tractable for running on computers or more biologically plausible spiking neurons typically requiring special hardware. However, in nature homogeneous networks of neurons do not exist. Instead, spiking and non-spiking neurons cooperate, each bringing a different set of advantages. A well researched biological example of such a mixed network is the sensorimotor pathway, responsible for mapping sensory inputs to behavioral changes. This pathway is also well researched in robotics where it is applied to achieve closed-loop operation of legged robots by adapting amplitude, frequency, and phase of the motor output. In this paper we investigate how spiking and non-spiking neurons can be combined to create a sensorimotor neuron pathway capable of shaping network output based on analog input. We propose sub-threshold operation of an existing spiking neuron model to create a non-spiking neuron able to interpret analog information and communicate with spiking neurons. The validity of this methodology is confirmed through a simulation of a closed-loop amplitude regulating network. Additionally, we show that non-spiking neurons can effectively manipulate post-synaptic spiking neurons in an event-based architecture. The ability to work with mixed networks provides an opportunity for researchers to investigate new network architectures for adaptive controllers, potentially improving locomotion strategies of legged robots.

neuroscience↗