Configuring spiking neural network training algorithms

Spiking neural networks, based on biologically-plausible neurons with temporal information coding, are provably more powerful than widely used artificial neural networks based on sigmoid neurons (ANNs). However, training them is more challenging than training ANNs. Several methods have been proposed in the literature, each with its limitations: SpikeProp, NSEBP, ReSuMe, etc. And setting numerous parameters of spiking networks to obtain good accuracy has been largely ad hoc. In this work, we used automated algorithm configuration tools to determine optimal combinations of parameters for ANNs, artificial neural networks with components simulating glia cells (astrocytes), and for spiking neural networks with SpikeProp learning algorithm. This allowed us to achieve better accuracy on standard datasets (Iris and Wisconsin Breast Cancer), and showed that even after optimization augmenting an artificial neural network with glia results in improved performance. Guided by the experimental results, we have developed methods for determining values of several parameters of spiking neural networks, in particular weight and output ranges. These methods have been incorporated into a SpikeProp implementation

Mammalian Brain As a Network of Networks

Acknowledgements AZ, SG and AL acknowledge support from the Russian Science Foundation (16-12-00077). Authors thank T. Kuznetsova for Fig. 6.Peer reviewedPublisher PD

The thalamocortical symphony:How thalamus and cortex play together in schizophrenia and plasticity

The work presented in this thesis aimed at investigating the function and mechanism of corticothalamic-thalamocortical network in schizophrenia and experience-dependent plasticity, further discussed their possible connection.In Chapter 2, we examined the effects of low-dose ketamine on the corticothalamic circuit (CTC) system. Our findings reveal that ketamine induces abnormal spindle activity and gamma oscillations in the CTC system. Notably, ketamine also leads to a transition in thalamic neurons from burst-firing to tonic action potential mode, which may underlie deficits in spindle oscillations. Chapter 3 addresses sensory perception deficits in schizophrenia, emphasizing disruptions in beta and gamma frequency oscillations due to signal-to-noise ratio imbalances. Chapter 4 explores experience-dependent plasticity, highlighting the role of thalamic synaptic inhibition in ocular dominance plasticity and the influence of cortical feedback. Chapter 5 investigates the involvement of endocannabinoids, particularly CB1 receptors, in inhibitory synaptic maturation and ocular dominance plasticity within the primary visual cortex.The general discussion raises the possibility of a link between neural plasticity and schizophrenia, particularly during the transformative phase of adolescence when the brain undergoes significant changes. An abnormal balance between inhibition and excitation, influenced by GABAergic maturation deficits, connectivity disruptions, and altered perceptual information transfer, may contribute to the development of schizophrenia.This thesis offers valuable insights into the intricate mechanisms underlying schizophrenia, with a particular focus on the CTC circuit, NMDA receptors, and endocannabinoids in the context of neuronal plasticity and cognitive function

The thalamocortical symphony:How thalamus and cortex play together in schizophrenia and plasticity

The work presented in this thesis aimed at investigating the function and mechanism of corticothalamic-thalamocortical network in schizophrenia and experience-dependent plasticity, further discussed their possible connection.In Chapter 2, we examined the effects of low-dose ketamine on the corticothalamic circuit (CTC) system. Our findings reveal that ketamine induces abnormal spindle activity and gamma oscillations in the CTC system. Notably, ketamine also leads to a transition in thalamic neurons from burst-firing to tonic action potential mode, which may underlie deficits in spindle oscillations. Chapter 3 addresses sensory perception deficits in schizophrenia, emphasizing disruptions in beta and gamma frequency oscillations due to signal-to-noise ratio imbalances. Chapter 4 explores experience-dependent plasticity, highlighting the role of thalamic synaptic inhibition in ocular dominance plasticity and the influence of cortical feedback. Chapter 5 investigates the involvement of endocannabinoids, particularly CB1 receptors, in inhibitory synaptic maturation and ocular dominance plasticity within the primary visual cortex.The general discussion raises the possibility of a link between neural plasticity and schizophrenia, particularly during the transformative phase of adolescence when the brain undergoes significant changes. An abnormal balance between inhibition and excitation, influenced by GABAergic maturation deficits, connectivity disruptions, and altered perceptual information transfer, may contribute to the development of schizophrenia.This thesis offers valuable insights into the intricate mechanisms underlying schizophrenia, with a particular focus on the CTC circuit, NMDA receptors, and endocannabinoids in the context of neuronal plasticity and cognitive function

Involvement of the cortico-basal ganglia-thalamocortical loop in developmental stuttering

Stuttering is a complex neurodevelopmental disorder that has to date eluded a clear explication of its pathophysiological bases. In this review, we utilize the Directions Into Velocities of Articulators (DIVA) neurocomputational modeling framework to mechanistically interpret relevant findings from the behavioral and neurological literatures on stuttering. Within this theoretical framework, we propose that the primary impairment underlying stuttering behavior is malfunction in the cortico-basal ganglia-thalamocortical (hereafter, cortico-BG) loop that is responsible for initiating speech motor programs. This theoretical perspective predicts three possible loci of impaired neural processing within the cortico-BG loop that could lead to stuttering behaviors: impairment within the basal ganglia proper; impairment of axonal projections between cerebral cortex, basal ganglia, and thalamus; and impairment in cortical processing. These theoretical perspectives are presented in detail, followed by a review of empirical data that make reference to these three possibilities. We also highlight any differences that are present in the literature based on examining adults versus children, which give important insights into potential core deficits associated with stuttering versus compensatory changes that occur in the brain as a result of having stuttered for many years in the case of adults who stutter. We conclude with outstanding questions in the field and promising areas for future studies that have the potential to further advance mechanistic understanding of neural deficits underlying persistent developmental stuttering.R01 DC007683 - NIDCD NIH HHS; R01 DC011277 - NIDCD NIH HHSPublished versio

Neurovascular coupling: insights from multi-modal dynamic causal modelling of fMRI and MEG

This technical note presents a framework for investigating the underlying mechanisms of neurovascular coupling in the human brain using multi-modal magnetoencephalography (MEG) and functional magnetic resonance (fMRI) neuroimaging data. This amounts to estimating the evidence for several biologically informed models of neurovascular coupling using variational Bayesian methods and selecting the most plausible explanation using Bayesian model comparison. First, fMRI data is used to localise active neuronal sources. The coordinates of neuronal sources are then used as priors in the specification of a DCM for MEG, in order to estimate the underlying generators of the electrophysiological responses. The ensuing estimates of neuronal parameters are used to generate neuronal drive functions, which model the pre or post synaptic responses to each experimental condition in the fMRI paradigm. These functions form the input to a model of neurovascular coupling, the parameters of which are estimated from the fMRI data. This establishes a Bayesian fusion technique that characterises the BOLD response - asking, for example, whether instantaneous or delayed pre or post synaptic signals mediate haemodynamic responses. Bayesian model comparison is used to identify the most plausible hypotheses about the causes of the multimodal data. We illustrate this procedure by comparing a set of models of a single-subject auditory fMRI and MEG dataset. Our exemplar analysis suggests that the origin of the BOLD signal is mediated instantaneously by intrinsic neuronal dynamics and that neurovascular coupling mechanisms are region-specific. The code and example dataset associated with this technical note are available through the statistical parametric mapping (SPM) software package

Physiologically informed dynamic causal modeling of fMRI data

AbstractThe functional MRI (fMRI) signal is an indirect measure of neuronal activity. In order to deconvolve the neuronal activity from the experimental fMRI data, biophysical generative models have been proposed describing the link between neuronal activity and the cerebral blood flow (the neurovascular coupling), and further the hemodynamic response and the BOLD signal equation. These generative models have been employed both for single brain area deconvolution and to infer effective connectivity in networks of multiple brain areas. In the current paper, we introduce a new fMRI model inspired by experimental observations about the physiological underpinnings of the BOLD signal and compare it with the generative models currently used in dynamic causal modeling (DCM), a widely used framework to study effective connectivity in the brain. We consider three fundamental aspects of such generative models for fMRI: (i) an adaptive two-state neuronal model that accounts for a wide repertoire of neuronal responses during and after stimulation; (ii) feedforward neurovascular coupling that links neuronal activity to blood flow; and (iii) a balloon model that can account for vascular uncoupling between the blood flow and the blood volume. Finally, we adjust the parameterization of the BOLD signal equation for different magnetic field strengths. This paper focuses on the form, motivation and phenomenology of DCMs for fMRI and the characteristics of the various models are demonstrated using simulations. These simulations emphasize a more accurate modeling of the transient BOLD responses — such as adaptive decreases to sustained inputs during stimulation and the post-stimulus undershoot. In addition, we demonstrate using experimental data that it is necessary to take into account both neuronal and vascular transients to accurately model the signal dynamics of fMRI data. By refining the models of the transient responses, we provide a more informed perspective on the underlying neuronal process and offer new ways of inferring changes in local neuronal activity and effective connectivity from fMRI

Astroglial Control of Respiratory Rhythm Generating Circuits

Astrocytes, the most numerous glial cells of the central nervous system, are well known to provide neuronal circuits with essential structural and metabolic support. There is also evidence that astrocytes may modulate the activities of neuronal circuits controlling motor rhythms including those of the brainstem’s preBötzinger complex (preBötC) that generates the rhythm of breathing in mammals. However, the extent and mechanisms of active astroglial control of the respiratory rhythm-generating circuits remain unknown. The morphological features of astrocytes in this critical brainstem region are also unknown. In this dissertation, viral gene transfer approaches designed to block or activate astroglial signaling pathways were used to determine the role of preBötC astrocytes in the control of breathing using in vitro and in vivo experimental models. Computer-aided morphometric analyses were used to investigate the structural features of brainstem astrocytes potentially contributing to their functional role. The results from these complementary, multi-faceted experiments show that (i) morphologically, preBötC astrocytes are larger, have more branches, and longer processes when compared to astrocytes residing in other regions of the brainstem; (ii) in conscious adult rats, blockade of vesicular release mechanisms or ATP-mediated signaling in preBötC astrocytes by virally-induced bilateral expression of either the light chain of tetanus toxin (TeLC), the dominant-negative SNARE proteins (dnSNARE), or a potent ectonucleotidase – transmembrane prostatic acid phosphatase – results in a significant reduction of resting respiratory frequency and frequency of sighs, augmented breaths that engage preBötC circuits to increase inspiratory effort; (iii) hypoxic- and CO2-induced ventilatory responses are significantly reduced when vesicular release mechanisms in preBötC astrocytes are blocked; (iv) activation of preBötC astrocytes expressing Gq-coupled Designer Receptor Exclusively Activated by Designer Drug is associated with higher frequency of both normal inspirations and sighs; (v) blockade of vesicular release mechanisms (expression of TeLC or dnSNARE) in preBötC astrocytes is associated with a dramatic reduction of exercise capacity. These data suggest that astroglial mechanisms involving exocytotic vesicular release of signaling molecules (gliotransmitters), provides tonic excitatory drive to the inspiratory rhythm-generating circuits of the preBötC and contributes to the generation of sighs. The role of preBötC astrocytes in central nervous mechanisms controlling breathing becomes especially important in conditions of metabolic stress requiring homeostatic adjustments of breathing such as systemic hypoxia, hypercapnia, and exercise, when enhanced respiratory efforts are critical to support physiological and behavioral demands of the body

Advancing functional connectivity research from association to causation

Cognition and behavior emerge from brain network interactions, such that investigating causal interactions should be central to the study of brain function. Approaches that characterize statistical associations among neural time series-functional connectivity (FC) methods-are likely a good starting point for estimating brain network interactions. Yet only a subset of FC methods ('effective connectivity') is explicitly designed to infer causal interactions from statistical associations. Here we incorporate best practices from diverse areas of FC research to illustrate how FC methods can be refined to improve inferences about neural mechanisms, with properties of causal neural interactions as a common ontology to facilitate cumulative progress across FC approaches. We further demonstrate how the most common FC measures (correlation and coherence) reduce the set of likely causal models, facilitating causal inferences despite major limitations. Alternative FC measures are suggested to immediately start improving causal inferences beyond these common FC measures