Neural dynamics represent the hard-to-interpret substrate of circuit computations. Advances in large-scale recordings have highlighted the sheer spatiotemporal complexity of circuit dynamics within and across circuits, portraying in detail the difficulty of interpreting such dynamics and relating it to computation. Indeed, even in extremely simplified experimental conditions, one observes high-dimensional temporal dynamics in the relevant circuits. This complexity can be potentially addressed by the notion that not all changes in population activity have equal meaning, i.e., a small change in the evolution of activity along a particular dimension may have a bigger effect on a given computation than a large change in another. We term such conditions dimension-specific computation. Considering motor preparatory activity in a delayed response task we utilized neural recordings performed simultaneously with optogenetic perturbations to probe circuit dynamics. First, we revealed a remarkable robustness in the detailed evolution of certain dimensions of the population activity, beyond what was thought to be the case experimentally and theoretically. Second, the robust dimension in activity space carries nearly all of the decodable behavioral information whereas other non-robust dimensions contained nearly no decodable information, as if the circuit was setup to make informative dimensions stiff, i.e., resistive to perturbations, leaving uninformative dimensions sloppy, i.e., sensitive to perturbations. Third, we show that this robustness can be achieved by a modular organization of circuitry, whereby modules whose dynamics normally evolve independently can correct each other’s dynamics when an individual module is perturbed, a common design feature in robust systems engineering. Finally, we will recent work extending this framework to understanding the neural dynamics underlying preparation of speech.

Neural entrainment has become a phenomenon of exceptional interest to neuroscience, given its involvement in rhythm perception, production, and overt synchronized behavior. Yet, traditional methods fail to quantify neural entrainment due to a misalignment with its fundamental definition (e.g., see Novembre and Iannetti, 2018; Rajandran and Schupp, 2019). The definition of entrainment assumes that endogenous oscillatory brain activity undergoes dynamic frequency adjustments to synchronize with environmental rhythms (Lakatos et al., 2019). Following this definition, we recently developed a method sensitive to this process. Our aim was to isolate from the electroencephalographic (EEG) signal an oscillatory component that is attuned to the frequency of a rhythmic stimulation, hypothesizing that the oscillation would adaptively speed up and slow down to achieve stable synchronization over time. To induce and measure these adaptive changes in a controlled fashion, we developed the event-related frequency adjustment (ERFA) paradigm (Rosso et al., 2023). A total of twenty healthy participants took part in our study. They were instructed to tap their finger synchronously with an isochronous auditory metronome, which was unpredictably perturbed by phase-shifts and tempo-changes in both positive and negative directions across different experimental conditions. EEG was recorded during the task, and ERFA responses were quantified as changes in instantaneous frequency of the entrained component. Our results indicate that ERFAs track the stimulus dynamics in accordance with the perturbation type and direction, preferentially for a sensorimotor component. The clear and consistent patterns confirm that our method is sensitive to the process of frequency adjustment that defines neural entrainment. In this Virtual Journal Club, the discussion of our findings will be complemented by methodological insights beneficial to researchers in the fields of rhythm perception and production, as well as timing in general. We discuss the dos and don’ts of using instantaneous frequency to quantify oscillatory dynamics, the advantages of adopting a multivariate approach to source separation, the robustness against the confounder of responses evoked by periodic stimulation, and provide an overview of domains and concrete examples where the methodological framework can be applied.

To prevail in a dynamic and noisy environment, the brain must create reliable and meaningful representations from sensory inputs that are often ambiguous or corrupt. Since only information that permeates the cortical hierarchy can influence sensory perception and decision-making, it is critical that noisy external stimuli are encoded and propagated through different processing stages with minimal signal degradation. Here we hypothesize that stimulus-specific pathways akin to cortical topographic maps may provide the structural scaffold for such signal routing. We investigate whether the feature-specific pathways within such maps, characterized by the preservation of the relative organization of cells between distinct populations, can guide and route stimulus information throughout the system while retaining representational fidelity. We demonstrate that, in a large modular circuit of spiking neurons comprising multiple sub-networks, topographic projections are not only necessary for accurate propagation of stimulus representations, but can also help the system reduce sensory and intrinsic noise. Moreover, by regulating the effective connectivity and local E/I balance, modular topographic precision enables the system to gradually improve its internal representations and increase signal-to-noise ratio as the input signal passes through the network. Such a denoising function arises beyond a critical transition point in the sharpness of the feed-forward projections, and is characterized by the emergence of inhibition-dominated regimes where population responses along stimulated maps are amplified and others are weakened. Our results indicate that this is a generalizable and robust structural effect, largely independent of the underlying model specificities. Using mean-field approximations, we gain deeper insight into the mechanisms responsible for the qualitative changes in the system’s behavior and show that these depend only on the modular topographic connectivity and stimulus intensity. The general dynamical principle revealed by the theoretical predictions suggest that such a denoising property may be a universal, system-agnostic feature of topographic maps, and may lead to a wide range of behaviorally relevant regimes observed under various experimental conditions: maintaining stable representations of multiple stimuli across cortical circuits; amplifying certain features while suppressing others (winner-take-all circuits); and endow circuits with metastable dynamics (winnerless competition), assumed to be fundamental in a variety of tasks.

Neural responses are variable: even under identical experimental conditions, single neuron and population responses typically differ from trial to trial and across time. Recent work has demonstrated that this variability has predictable structure, can be modulated by sensory input and behaviour, and bears critical signatures of the underlying network dynamics and computations. However, current methods for characterising neural variability are primarily geared towards sensory coding in the laboratory: they require trials with repeatable experimental stimuli and behavioural covariates. In addition, they make strong assumptions about the parametric form of variability, rely on assumption-free but data-inefficient histogram-based approaches, or are altogether ill-suited for capturing variability modulation by covariates. Here we present a universal probabilistic spike count model that eliminates these shortcomings. Our method uses scalable Bayesian machine learning techniques to model arbitrary spike count distributions (SCDs) with flexible dependence on observed as well as latent covariates. Without requiring repeatable trials, it can flexibly capture covariate-dependent joint SCDs, and provide interpretable latent causes underlying the statistical dependencies between neurons. We apply the model to recordings from a canonical non-sensory neural population: head direction cells in the mouse. We find that variability in these cells defies a simple parametric relationship with mean spike count as assumed in standard models, its modulation by external covariates can be comparably strong to that of the mean firing rate, and slow low-dimensional latent factors explain away neural correlations. Our approach paves the way to understanding the mechanisms and computations underlying neural variability under naturalistic conditions, beyond the realm of sensory coding with repeatable stimuli.

One of the most important questions in psychology and neuroscience is understanding how the outside world maps to internal representations. Classical psychophysics approaches to this problem have a number of limitations: they mostly study low dimensional perpetual spaces, and are constrained in the number and diversity of participants and experiments. As ecologically valid perception is rich, high dimensional, contextual, and culturally dependent, these impediments severely bias our understanding of perceptual representations. Recent technological advances—the emergence of so-called “Virtual Labs”— can significantly contribute toward overcoming these barriers. Here I present a number of specific strategies that my group has developed in order to probe representations across a number of dimensions. 1) Massive online experiments can increase significantly the amount of participants and experiments that can be carried out in a single study, while also significantly diversifying the participant pool. We have developed a platform, PsyNet, that enables “experiments as code,” whereby the orchestration of computer servers, recruiting, compensation of participants, and data management is fully automated and every experiment can be fully replicated with one command line. I will demonstrate how PsyNet allows us to recruit thousands of participants for each study with a large number of control experimental conditions, significantly increasing our understanding of auditory perception. 2) Virtual lab methods also enable us to run experiments that are nearly impossible in a traditional lab setting. I will demonstrate our development of adaptive sampling, a set of behavioural methods that combine machine learning sampling techniques (Monte Carlo Markov Chains) with human interactions and allow us to create high-dimensional maps of perceptual representations with unprecedented resolution. 3) Finally, I will demonstrate how the aforementioned methods can be applied to the study of perceptual priors in both audition and vision, with a focus on our work in cross-cultural research, which studies how perceptual priors are influenced by experience and culture in diverse samples of participants from around the world.

Methodology for understanding the basic phenomena of life can be done in vitro or in vivo, under tightly-controlled experimental conditions designed to limit variability. However stringent the protocol, these experiments do not occur in a cultural vacuum and they are often subject to the same societal biases as other research disciplines. Many researchers uphold the status quo of biased basic research by not questioning the characteristics of their experimental animals, or the people from whom their tissue samples were collected. This means that our fundamental understanding of life has been built on biased models. This session will explore the ways in which basic life sciences research can be biased and the implications of this. We will discuss practical ways to assess your research design and how to make sure it is representative.