Jean-Philippe Thivierge

Can structure predict function in the human brain

Over the past decade, scientific interest in the properties of large-scale spontaneous neural dynamics has intensified. Concurrently, novel technologies have been developed for characterizing the connective anatomy of intra-regional circuits and inter-regional fiber pathways. It will soon be possible to build computational models that incorporate these newly detailed structural network measurements to make predictions of neural dynamics at multiple scales. Here, we review the practicality and the value of these efforts, while at the same time considering in which cases and to what extent structure does determine neural function. Studies of the healthy brain, of neural development, and of pathology all yield examples of direct correspondences between structural linkage and dynamical correlation. Theoretical arguments further support the notion that brain network topology and spatial embedding should strongly influence network dynamics. Although future models will need to be tested more quantitatively and against a wider range of empirical neurodynamic features, our present large-scale models can already predict the macroscopic pattern of dynamic correlation across the brain. We conclude that as neuroscience grapples with datasets of increasing completeness and complexity, and attempts to relate the structural and functional architectures discovered at different neural scales, the value of computational modeling will continue to grow.

Neurobiologically Realistic Determinants of Self-Organized Criticality in Networks of Spiking Neurons

Self-organized criticality refers to the spontaneous emergence of self-similar dynamics in complex systems poised between order and randomness. The presence of self-organized critical dynamics in the brain is theoretically appealing and is supported by recent neurophysiological studies. Despite this, the neurobiological determinants of these dynamics have not been previously sought. Here, we systematically examined the influence of such determinants in hierarchically modular networks of leaky integrate-and-fire neurons with spike-timing-dependent synaptic plasticity and axonal conduction delays. We characterized emergent dynamics in our networks by distributions of active neuronal ensemble modules (neuronal avalanches) and rigorously assessed these distributions for power-law scaling. We found that spike-timing-dependent synaptic plasticity enabled a rapid phase transition from random subcritical dynamics to ordered supercritical dynamics. Importantly, modular connectivity and low wiring cost broadened this transition, and enabled a regime indicative of self-organized criticality. The regime only occurred when modular connectivity, low wiring cost and synaptic plasticity were simultaneously present, and the regime was most evident when between-module connection density scaled as a power-law. The regime was robust to variations in other neurobiologically relevant parameters and favored systems with low external drive and strong internal interactions. Increases in system size and connectivity facilitated internal interactions, permitting reductions in external drive and facilitating convergence of postsynaptic-response magnitude and synaptic-plasticity learning rate parameter values towards neurobiologically realistic levels. We hence infer a novel association between self-organized critical neuronal dynamics and several neurobiologically realistic features of structural connectivity. The central role of these features in our model may reflect their importance for neuronal information processing.

The topographic brain: from neural connectivity to cognition

A hallmark feature of vertebrate brain organization is ordered topography, wherein sets of neuronal connections preserve the relative organization of cells between two regions. Although topography is often found in projections from peripheral sense organs to the brain, it also seems to participate in the anatomical and functional organization of higher brain centers, for reasons that are poorly understood. We propose that a key function of topography might be to provide computational underpinnings for precise one-to-one correspondences between abstract cognitive representations. This perspective offers a novel conceptualization of how the brain approaches difficult problems, such as reasoning and analogy making, and suggests that a broader understanding of topographic maps could be pivotal in fostering strong links between genetics, neurophysiology and cognition.

Nonperiodic Synchronization in Heterogeneous Networks of Spiking Neurons

Neural synchronization is of wide interest in neuroscience and has been argued to form the substrate for conscious attention to stimuli, movement preparation, and the maintenance of task-relevant representations in active memory. Despite a wealth of possible functions, the mechanisms underlying synchrony are still poorly understood. In particular, in vitro preparations have demonstrated synchronization with no apparent periodicity, which cannot be explained by simple oscillatory mechanisms. Here, we investigate the possible origins of nonperiodic synchronization through biophysical simulations. We show that such aperiodic synchronization arises naturally under a simple set of plausible assumptions, depending crucially on heterogeneous cell properties. In addition, nonperiodicity occurs even in the absence of stochastic fluctuation in membrane potential, suggesting that it may represent an intrinsic property of interconnected networks. Simulations capture some of the key aspects of population-level synchronization in spontaneous network spikes (NSs) and suggest that the intrinsic nonperiodicity of NSs observed in reduced cell preparations is a phenomenon that is highly robust and can be reproduced in simulations that involve a minimal set of realistic assumptions. In addition, a model with spike timing-dependent plasticity can overcome a natural tendency to exhibit nonperiodic behavior. After rhythmic stimulation, the model does not automatically fall back to a state of nonperiodic behavior, but keeps replaying the pattern of evoked NSs for a few cycles. A cluster analysis of synaptic strengths highlights the importance of population-wide interactions in generating this result and describes a possible route for encoding temporal patterns in networks of neurons.

ELEVATED SYNAPTIC ACTIVITY PRECONDITIONS NEURONS AGAINST AN IN VITRO MODEL OF ISCHEMIA

Tolerance to otherwise lethal cerebral ischemia in vivo or to oxygen-glucose deprivation (OGD) in vitro can be induced by prior transient exposure to N-methyl-d-aspartic acid (NMDA): preconditioning in this manner activates extrasynaptic and synaptic NMDA receptors and can require bringing neurons to the “brink of death.” We considered if this stressful requirement could be minimized by the stimulation of primarily synaptic NMDA receptors. Subjecting cultured cortical neurons to prolonged elevations in electrical activity induced tolerance to OGD. Specifically, exposing cultures to a K+-channel blocker, 4-aminopyridine (20–2500 μm), and a GABAA receptor antagonist, bicuculline (50 μm) (4-AP/bic), for 1–2 days resulted in potent tolerance to normally lethal OGD applied up to 3 days later. Preconditioning induced phosphorylation of ERK1/2 and CREB which, along with Ca2+ spiking and OGD tolerance, was eliminated by tetrodotoxin. Antagonists of NMDA receptors or l-type voltage-gated Ca2+ channels (L-VGCCs) applied during preconditioning decreased Ca2+ spiking, phosphorylation of ERK1/2 and CREB, and OGD tolerance more effectively when combined, particularly at the lowest 4-AP concentration. Inhibiting ERK1/2 or Ca2+/calmodulin-dependent protein kinases (CaMKs) also reduced Ca2+ spiking and OGD tolerance. Preconditioning resulted in altered neuronal excitability for up to 3 days following 4-AP/bic washout, based on field potential recordings obtained from neurons cultured on 64-channel multielectrode arrays. Taken together, the data are consistent with action potential-driven co-activation of primarily synaptic NMDA receptors and L-VGCCs, resulting in parallel phosphorylation of ERK1/2 and CREB and involvement of CaMKs, culminating in a potent, prolonged but reversible, OGD-tolerant phenotype.

Correlated Synaptic Inputs Drive Dendritic Calcium Amplification and Cooperative Plasticity during Clustered Synapse Development

The mechanisms that instruct the assembly of fine-scale features of synaptic connectivity in neural circuits are only beginning to be understood. Using whole-cell electrophysiology, two-photon calcium imaging, and glutamate uncaging in hippocampal slices, we discovered a functional coupling between NMDA receptor activation and ryanodine-sensitive intracellular calcium release that dominates the spatiotemporal dynamics of activity-dependent calcium signals during synaptogenesis. This developmentally regulated calcium amplification mechanism was tuned to detect and bind spatially clustered and temporally correlated synaptic inputs and enacted a local cooperative plasticity rule between coactive neighboring synapses. Consistent with the hypothesis that synapse maturation is spatially regulated, we observed clustering of synaptic weights in developing dendritic arbors. These results reveal developmental features of NMDA receptor-dependent calcium dynamics and local plasticity rules that are suited to spatially guide synaptic connectivity patterns in emerging neural networks.

Extracting functionally feedforward networks from a population of spiking neurons.

Neuronal avalanches are a ubiquitous form of activity characterized by spontaneous bursts whose size distribution follows a power-law. Recent theoretical models have replicated power-law avalanches by assuming the presence of functionally feedforward connections (FFCs) in the underlying dynamics of the system. Accordingly, avalanches are generated by a feedforward chain of activation that persists despite being embedded in a larger, massively recurrent circuit. However, it is unclear to what extent networks of living neurons that exhibit power-law avalanches rely on FFCs. Here, we employed a computational approach to reconstruct the functional connectivity of cultured cortical neurons plated on multielectrode arrays (MEAs) and investigated whether pharmacologically induced alterations in avalanche dynamics are accompanied by changes in FFCs. This approach begins by extracting a functional network of directed links between pairs of neurons, and then evaluates the strength of FFCs using Schur decomposition. In a first step, we examined the ability of this approach to extract FFCs from simulated spiking neurons. The strength of FFCs obtained in strictly feedforward networks diminished monotonically as links were gradually rewired at random. Next, we estimated the FFCs of spontaneously active cortical neuron cultures in the presence of either a control medium, a GABAA receptor antagonist (PTX), or an AMPA receptor antagonist combined with an NMDA receptor antagonist (APV/DNQX). The distribution of avalanche sizes in these cultures was modulated by this pharmacology, with a shallower power-law under PTX (due to the prominence of larger avalanches) and a steeper power-law under APV/DNQX (due to avalanches recruiting fewer neurons) relative to control cultures. The strength of FFCs increased in networks after application of PTX, consistent with an amplification of feedforward activity during avalanches. Conversely, FFCs decreased after application of APV/DNQX, consistent with fading feedforward activation. The observed alterations in FFCs provide experimental support for recent theoretical work linking power-law avalanches to the feedforward organization of functional connections in local neuronal circuits.

COULD KNOWLEDGE-BASED NEURAL LEARNING BE USEFUL IN DEVELOPMENTAL ROBOTICS? THE CASE OF KBCC

The new field of developmental robotics faces the formidable challenge of implementing effective learning mechanisms in complex, dynamic environments. We make a case that knowledge-based learning algorithms might help to meet this challenge. A constructive neural learning algorithm, knowledge-based cascade-correlation (KBCC), autonomously recruits previously-learned networks in addition to the single hidden units recruited by ordinary cascade-correlation. This enables learning by analogy when adequate prior knowledge is available, learning by induction from examples when there is no relevant prior knowledge, and various combinations of analogy and induction. A review of experiments with KBCC indicates that recruitment of relevant existing knowledge typically speeds learning and sometimes enables learning of otherwise impossible problems. Some additional domains of interest to developmental robotics are identified in which knowledge-based learning seems essential. The characteristics of KBCC in relation to other knowledge-based neural learners and analogical reasoning are summarized as is the neurological basis for learning from knowledge. Current limitations of this approach and directions for future work are discussed.

How does non-random spontaneous activity contribute to brain development?

Highly non-random forms of spontaneous activity are proposed to play an instrumental role in the early development of the visual system. However, both the fundamental properties of spontaneous activity required to drive map formation, as well as the exact role of this information remain largely unknown. Here, a realistic computational model of spontaneous retinal waves is employed to demonstrate that both the amplitude and frequency of waves may play determining roles in retinocollicular map formation. Furthermore, results obtained with different learning rules show that spike precision in the order of milliseconds may be instrumental to neural development: a rule based on precise spike interactions (spike-timing-dependent plasticity) reduced the density of aberrant projections to the SC to a markedly greater extent than a rule based on interactions at much broader time-scale (correlation-based plasticity). Taken together, these results argue for an important role of spontaneous yet highly non-random activity, along with temporally precise learning rules, in the formation of neural circuits.

Altered Network Communication Following a Neuroprotective Drug Treatment

Preconditioning is defined as a range of stimuli that allow cells to withstand subsequent anaerobic and other deleterious conditions. While cell protection under preconditioning is well established, this paper investigates the influence of neuroprotective preconditioning drugs, 4-aminopyridine and bicuculline (4-AP/bic), on synaptic communication across a broad network of in vitro rat cortical neurons. Using a permutation test, we evaluated cross-correlations of extracellular spiking activity across all pairs of recording electrodes on a 64-channel multielectrode array. The resulting functional connectivity maps were analyzed in terms of their graph-theoretic properties. A small-world effect was found, characterized by a functional network with high clustering coefficient and short average path length. Twenty-four hours after exposure to 4-AP/bic, small-world properties were comparable to control cultures that were not treated with the drug. Four hours following drug washout, however, the density of functional connections increased, while path length decreased and clustering coefficient increased. These alterations in functional connectivity were maintained at four days post-washout, suggesting that 4-AP/bic preconditioning leads to long-term effects on functional networks of cortical neurons. Because of their influence on communication efficiency in neuronal networks, alterations in small-world properties hold implications for information processing in brain systems. The observed relationship between density, path length, and clustering coefficient is captured by a phenomenological model where connections are added randomly within a spatially-embedded network. Taken together, results provide information regarding functional consequences of drug therapies that are overlooked in traditional viability studies and present the first investigation of functional networks under neuroprotective preconditioning.