Modern electrophysiological recordings simultaneously capture single-unit spiking activities of hundreds of neurons. These neurons exhibit highly complex coordination patterns. Where does this complexity stem from? One candidate is the ubiquitous heterogeneity in connectivity of local neural circuits. Studying neural network dynamics in the linearized regime and using tools from statistical field theory of disordered systems, we derive relations between structure and dynamics that are readily applicable to subsampled recordings of neural circuits: Measuring the statistics of pairwise covariances allows us to infer statistical properties of the underlying connectivity. Applying our results to spontaneous activity of macaque motor cortex, we find that the underlying network operates in a strongly recurrent regime. In this regime, network connectivity is highly heterogeneous, as quantified by a large radius of bulk connectivity eigenvalues. Being close to the point of linear instability, this dynamical regime predicts a rich correlation structure, a large dynamical repertoire, long-range interaction patterns, relatively low dimensionality and a sensitive control of neuronal coordination. These predictions are verified in analyses of spontaneous activity of macaque motor cortex and mouse visual cortex. Finally, we show that even microscopic features of connectivity, such as connection motifs, systematically scale up to determine the global organization of activity in neural circuits.

We use the olfactory system and forebrain of (adult) zebrafish as a model to analyze how relevant information is extracted from sensory inputs, how information is stored in memory circuits, and how sensory inputs inform behavior. A series of recent findings provides evidence that inhibition has not only homeostatic functions in neuronal circuits but makes highly specific, instructive contributions to behaviorally relevant computations in different brain regions. These observations imply that the connectivity among excitatory and inhibitory neurons exhibits essential higher-order structure that cannot be determined without dense network reconstructions. To analyze such connectivity we developed an approach referred to as “dynamical connectomics” that combines 2-photon calcium imaging of neuronal population activity with EM-based dense neuronal circuit reconstruction. In the olfactory bulb, this approach identified specific connectivity among co-tuned cohorts of excitatory and inhibitory neurons that can account for the decorrelation and normalization (“whitening”) of odor representations in this brain region. These results provide a mechanistic explanation for a fundamental neural computation that strictly requires specific network connectivity.

There is an avalanche of new data on activity in neural networks and the biological brain, revealing the collective dynamics of vast numbers of neurons. In principle, these collective dynamics can be of almost arbitrarily high dimension, with many independent degrees of freedom — and this may reflect powerful capacities for general computing or information. In practice, neural datasets reveal a range of outcomes, including collective dynamics of much lower dimension — and this may reflect other desiderata for neural codes. For what networks does each case occur? We begin by exploring bottom-up mechanistic ideas that link tractable statistical properties of network connectivity with the dimension of the activity that they produce. We then cover “top-down” ideas that describe how features of connectivity and dynamics that impact dimension arise as networks learn to perform fundamental computational tasks.

Cognitive control involves the flexible allocation of mental resources during goal-directed behaviour and comprises three correlated but distinct domains—inhibition, task shifting, and working memory. Healthy ageing is characterised by reduced cognitive control. Professor Cheryl Grady and her team have been studying the influence of age differences in large-scale brain networks on the three control processes in a sample of adults from 20 to 86 years of age. In this webinar, Professor Cheryl Grady will describe three aspects of this work: 1) age-related dedifferentiation and reconfiguration of brain networks across the sub-domains 2) individual differences in the relation of task-related activity to age, structural integrity and task performance for each sub-domain 3) modulation of brain signal variability as a function of cognitive load and age during working memory. This research highlights the reduction in dynamic range of network activity that occurs with ageing and how this contributes to age differences in cognitive control. Cheryl Grady is a senior scientist at the Rotman Research Institute at Baycrest, and Professor in the departments of Psychiatry and Psychology at the University of Toronto. She held the Canada Research Chair in Neurocognitive Aging from 2005-2018 and was elected as a Fellow of the Royal Society of Canada in 2019. Her research uses MRI to determine the role of brain network connectivity in cognitive ageing.

The analysis of functional magnetic resonance imaging (fMRI) data can greatly benefit from flexible analytic approaches. In particular, the advent of data-driven approaches to identify whole-brain time-varying connectivity and activity has revealed a number of interesting relevant variation in the data which, when ignored, can provide misleading information. In this lecture I will provide a comparative introduction of a range of data-driven approaches to estimating time-varying connectivity. I will also present detailed examples where studies of both brain health and disorder have been advanced by approaches designed to capture and estimate time-varying information in resting fMRI data. I will review several exemplar data sets analyzed in different ways to demonstrate the complementarity as well as trade-offs of various modeling approaches to answer questions about brain function. Finally, I will review and provide examples of strategies for validating time-varying connectivity including simulations, multimodal imaging, and comparative prediction within clinical populations, among others. As part of the interactive aspect I will provide a hands-on guide to the dynamic functional network connectivity toolbox within the GIFT software, including an online didactic analytic decision tree to introduce the various concepts and decisions that need to be made when using such tools

The link between behavior, learning and the underlying connectome is a fundamental open problem in neuroscience. In my talk I will show how it is possible to develop a theory that bridges across these three levels (animal behavior, learning and network connectivity) based on the geometrical properties of neural activity. The central tool in my approach is the dimensionality of neural activity. I will link animal complex behavior to the geometry of neural representations, specifically their dimensionality; I will then show how learning shapes changes in such geometrical properties and how local connectivity properties can further regulate them. As a result, I will explain how the complexity of neural representations emerges from both behavioral demands (top-down approach) and learning or connectivity features (bottom-up approach). I will build these results regarding neural dynamics and representations starting from the analysis of neural recordings, by means of theoretical and computational tools that blend dynamical systems, artificial intelligence and statistical physics approaches.

Understanding learning in neural circuits requires answering a number of difficult questions: (1) What is the computation being performed and what is its behavioral significance? (2) What are the inputs required for the computation and how are they represented at the level of spikes? (3) What are the sites and rules governing plasticity, i.e. how do pre and post-synaptic activity patterns produce persistent changes in synaptic strength? (4) How does network connectivity and dynamics shape the computation being performed? I will discuss joint experimental and theoretical work addressing these questions in the context of the electrosensory lobe (ELL) of weakly electric mormyrid fish.