Extending peripersonal space representation without tool-use: evidence from a combined behavioral-computational approach

Stimuli from different sensory modalities occurring on or close to the body are integrated in a multisensory representation of the space surrounding the body, i.e., peripersonal space (PPS). PPS dynamically modifies depending on experience, e.g., it extends after using a tool to reach far objects. However, the neural mechanism underlying PPS plasticity after tool use is largely unknown. Here we use a combined computational-behavioral approach to propose and test a possible mechanism accounting for PPS extension. We first present a neural network model simulating audio-tactile representation in the PPS around one hand. Simulation experiments showed that our model reproduced the main property of PPS neurons, i.e., selective multisensory response for stimuli occurring close to the hand. We used the neural network model to simulate the effects of a tool-use training. In terms of sensory inputs, tool use was conceptualized as a concurrent tactile stimulation from the hand, due to holding the tool, and an auditory stimulation from the far space, due to tool-mediated action. Results showed that after exposure to those inputs, PPS neurons responded also to multisensory stimuli far from the hand. The model thus suggests that synchronous pairing of tactile hand stimulation and auditory stimulation from the far space is sufficient to extend PPS, such as after tool-use. Such prediction was confirmed by a behavioral experiment, where we used an audio-tactile interaction paradigm to measure the boundaries of PPS representation. We found that PPS extended after synchronous tactile-hand stimulation and auditory-far stimulation in a group of healthy volunteers. Control experiments both in simulation and behavioral settings showed that the same amount of tactile and auditory inputs administered out of synchrony did not change PPS representation. We conclude by proposing a simple, biological-plausible model to explain plasticity in PPS representation after tool-use, which is supported by computational and behavioral data.

Limits to high-speed simulations of spiking neural networks using general-purpose computers

Wulfram Gerstner, Friedemann Zenke

To understand how the central nervous system performs computations using recurrent neuronal circuitry, simulations have become an indispensable tool for theoretical neuroscience. To study neuronal circuits and their ability to self-organize, increasing attention has been directed toward synaptic plasticity. In particular spike-timing-dependent plasticity (STDP) creates specific demands for simulations of spiking neural networks. On the one hand a high temporal resolution is required to capture the millisecond timescale of typical STDP windows. On the other hand network simulations have to evolve over hours up to days, to capture the timescale of long-term plasticity. To do this efficiently, fast simulation speed is the crucial ingredient rather than large neuron numbers. Using different medium-sized network models consisting of several thousands of neurons and off-the-shelf hardware, we compare the simulation speed of the simulators: Brian, NEST and Neuron as well as our own simulator Auryn. Our results show that real-time simulations of different plastic network models are possible in parallel simulations in which numerical precision is not a primary concern. Even so, the speed-up margin of parallelism is limited and boosting simulation speeds beyond one tenth of real-time is difficult. By profiling simulation code we show that the run times of typical plastic network simulations encounter a hard boundary. This limit is partly due to latencies in the inter-process communications and thus cannot be overcome by increased parallelism. Overall, these results show that to study plasticity in medium-sized spiking neural networks, adequate simulation tools are readily available which run efficiently on small clusters. However, to run simulations substantially faster than real-time, special hardware is a prerequisite.

Frontiers Research Foundation2014

Uncovering the neural encoding of behavior in the adult Drosophila motor system

Chin-Lin Chen

One of the most important goals in neuroscience research has always been to understand how animals control their behavior. However, the long focus on the role of brain neurons in behavioral control might be missing the full story. In fact, brain-wide fluctuations in neural activity are highly correlated to the behavioral state, suggesting a closed-loop system where the behavioral command and the body state interact to interpret sensory inputs and dynamically guide action selection. However, to date, little is known about how and where information about the behavioral state is generated. In principle, information about the behavioral state is a relatively high-level encoding of the body state derived from the integration of signals from primary sensors and the local network. Local motor circuits residing in the spinal cord of mammals or the ventral nerve cord (VNC) of insects may give rise to the behavioral state and pass to the brain via brain-projecting interneurons---termed ascending neurons (ANs). However, what information ANs carry and where this information originates is still a mystery due to the technical challenges in measuring these signals in behaving animals. Therefore, it is critical to develop tools to track neuron identity and record neuronal activity during behavior to crack the functional network. In this work, a method is introduced for imaging the motor circuit in the VNC of Drosophila melanogaster. This preparation demonstrates how to retain fly limb function after dissection and allows simultaneous recording of a tethered fly's behavior and its neural activity in the VNC. Incorporating this methodology with other approaches can verify the conclusions from the experiments such as those manipulating neuronal excitability, and motivate further investigation into neuronal function in specific types of behavior. Since it has long been theorized that ANs are the cellular basis of the behavioral state information sent in the brain, the technique introduced in this work was designed to resolve questions relating to what type of information ANs convey. Here, a large-scale functional screen was performed to relate neural activity, behavior, and neuronal morphology for 247 genetically identified ANs in the adult fly. This is the first large-scale screen of neural activity in a behaving animal, and this work uncovered three fundamental features of AN populations. First, rather than low-level limb movements, ANs encode high-level behaviors like walking, grooming, and resting. Second, ANs project nearly exclusively to an integrative sensory center (AVLP) and an action selection hub (GNG). Remarkably, the encodings projected to these distinct areas reflect their potential roles in contextualizing sensory cues (AVLP) and guiding future actions (GNG). Third, the behavioral encodings of ANs are closely linked to their motor system patterning. In conclusion, this work provides an indispensable tool to study the motor system beyond the brain network by overcoming a major technical barrier in in-vivo recording of the VNC of a behaving fly. This tool was used to help resolve what is encoded by ANs as a higher-level behavioral state, suggesting that the primary cellular origin of the behavioral information that is used to modulate brain function comes from the VNC. Overall, the technique and the findings on AN encoding should inspire follow-up studies to uncover additional neural mechanisms used in adaptive behavior generation.

EPFL2022

Spatio-temporal coupling of the electric and hemodynamic brain responses in humans

Roberto Martuzzi

Our comprehension of human brain functions and their dynamics has been dramatically improved by recent developments in non-invasive imaging techniques. These methods can be divided into two different categories, according to the nature of the measured signal: hemodynamic techniques, such as functional magnetic resonance imaging (fMRI) and positiron emission tomography (PET), and electromagnetic techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG). These two categories are have complementary characteristics: hemodynamic techniques have a good spatial resolution (on a millimeter spatial scale) but have a poor temporal resolution, which is inherently limited by the rate changes in blood flow and oxygenation. Electromagnetic techniques have sub-millisecond temporal resolution but have a poor spatial resolution, since the analysis of intracranial generators requires the solution of an underdetermined inverse problem (i.e. there are infinite solutions that can explain equally well the same scalp-recorded distribution). The complementarity of the characteristics of these two families of methods allowed researchers to suppose that the understanding of spatio-temporal brain dynamics can be drastically improved by their combination (so-called multimodal imaging). Unfortunately some caveats hinder such combination. First, the nature of neurovascular coupling is still poorly understood. Second, analytical methods for multimodal imaging are largely in their infancy. The first part of this thesis focuses on the analysis of the temporal characteristics of the blood oxygenation level dependent (BOLD) signal and on how they are modulated by stimulus conditions. To analyze the BOLD dynamics, a novel method for synchronizing stimulus delivery and volume acquisition was developed. This method allows for estimating the BOLD signal with a high temporal resolution (in this thesis up to 125 ms) and for studying how the temporal characteristics (in this thesis mainly the BOLD peak latency and slope) are modulated by stimulus conditions (with an approach similar to that used in the analysis of the EEG evoked potentials). We applied this novel technique to a simple reaction time task to lateralized visual stimuli (the so-called Poffenberger paradigm) as well as to a multisensory auditory-visual reaction time task. In the first study (the Poffenberger paradigm) the analysis of BOLD dynamics supported the theory of a bilateral visuo-motor pathway even in the case of a visual stimulus ipsilateral to the responding hand. In the second study, (the auditory-visual multisensory reaction-time task), the analysis showed auditory-visual interactions within both primary auditory and visual cortices that could not be otherwise revealed by traditional fMRI analysis methods since it does not involve changes in signal amplitude. The second part of this thesis focuses on the comparison of the statistical results obtained by the analyses of fMRI and of the intracranial local field potentials (LFPs), estimated by the ELECTRA inverse solution. We first developed a new method for the analysis of EEG data. This method is based on the statistical comparison of the spectral characteristics of the estimated intracranial LFPs of the pre- and post- stimulus onset periods. Each single trial is analyzed independently, without including an averaging step, so that the information carried by high frequencies is preserved. We also propose a new metric, called resemblance, to investigate the relationship between fMRI and the estimated intracranial LFPs. Single-trial analysis and the resemblance metric were applied in an experiment involving separate EEG and fMRI acquisitions during the same passive visual stimulation protocol. This experiment revealed that only a limited set of LFP frequencies shows a spatial correlation with fMRI. This set of frequencies changes across brain areas, such that progression from lower to higher cortical levels of visual processing incorporates at each step new frequencies. In conclusion, in this thesis we show that the estimation and the analysis of the BOLD time course can give an important contribution to better understanding brain functions and brain organization. To fully understand the meaning of changes in BOLD dynamics, we need a better knowledge of the neuro-vascular coupling. To do that, we introduced a new method for evaluating the relationship between EEG and fMRI across frequencies and anatomical regions.

EPFL2006