Neural computations

…similarity between high-dimensional neural representations is an active research in computational neuroscience. Such similarity measures have implications for understanding variability in neural computations across individuals and species [1], as well as for comparisons between biological systems and computational models [2]. One common approach to understand these systems is to build regression models or “decoders” that reconstruct features of the stimulus from neural responses. Here, we leverage this idea to quantify the similarity of

Every movement we make requires us to precisely coordinate muscle activity across our body in space and time. In this talk I will describe our efforts to understand how the brain generates flexible, coordinated movement. We have taken a behavior-centric approach to this problem, starting with the development of quantitative frameworks for mouse locomotion (LocoMouse; Machado et al., eLife 2015, 2020) and locomotor learning, in which mice adapt their locomotor symmetry in response to environmental perturbations (Darmohray et al., Neuron 2019). Combined with genetic circuit dissection, these stu

Neural computations are currently conceptualised using two separate approaches: sorting neurons into functional sub-populations or examining distributed collective dynamics. Whether and how these two aspects interact to shape computations is currently unclear. Using a novel approach to extract computational mechanisms from recurrent networks trained on neuroscience tasks, we show that the collective dynamics and sub-population structure play fundamentally complementary roles. Although various tasks can be implemented in networks with fully random population structure, we found that flexible i

…and can be used to interrogate neural computations underlying innate threat processing. We developed a virtual height threat task in which animals are placed on a pole and must choose to exit to either a “close” or “far” virtual platform. We find that animals perform around chance levels when the discrepancy between visual stimuli is small (5cm

Neural dynamics in a given brain region are generated either due to recurrent connections in the region (intrinsic dynamic), or due to temporally structured external inputs (input dynamics). These inputs can be sensory stimuli or excitations from upstream brain regions. While measuring all inputs is infeasible experimentally, measurements of sensory inputs such as task instructions or partial measurements of neural inputs into a brain region are often possible. When learning models of neural population dynamics, current learning methods can either account for measured input but not behavior, o

All behaviors, even simple actions, result from the interplay of multiple neural computations, and understanding how a given behavior is generated requires delineating these computations from one another. Traditionally, identifying these critical ‘computational building blocks’ requires a researcher to impose structure on the data: specific task epochs are analyzed separately or specific population-level structure is sought using supervised dimensionality reduction methods. While these approaches can be successful, they have obvious drawbacks – many behaviors cannot be easily parsed into disti

Neural computations are currently investigated using two separate approaches: sorting neurons into functional subpopulations or examining the low-dimensional dynamics of collective activity. Whether and how these two aspects interact to shape computations is currently unclear. Using a novel approach to extract computational mechanisms from networks trained on neuroscience tasks, here we show that the dimensionality of the dynamics and subpopulation structure play fundamentally com- plementary roles. Although various tasks can be implemented by increasing the dimensionality in networks with ful

Behavior results from neural computations that occur at the level of populations of neurons, and understanding how a given computation produces a particular behavior requires a characterization of how distinct signals interact and evolve. For example, in motor cortex, discrete reaches are generated by a combination of ‘preparatory’ and ‘execution-related’ signals. While the number of computationally-relevant signals is much lower than the number of neurons in a population – and is therefore amenable to dimensionality-reduction methods – standard techniques, like principal components analysis,

To move efficiently, animals must continuously work out their x,y,z positions with respect to real-world objects, and many animals have a pair of eyes to achieve this. How photoreceptors actively sample the eyes’ optical image disparity is not understood because this fundamental information-limiting step has not been investigated in vivo over the eyes’ whole sampling matrix. This integrative multiscale study will advance our current understanding of stereopsis from static image disparity comparison to a morphodynamic active sampling theory. It shows how photomechanical photoreceptor microsacca

Neural computations underlying complex behavior implicate multiple brain regions and large-scale, multi-site recordings from behaving animals are becoming increasingly common. Based on simultaneous recordings across the visual cortex (V1, V2, and V4), a recent hypothesis posits that brain regions communicate through low-dimensional subspaces [Semedo et al. (2019, 2021)]. In particular, it was found that these communication subspaces are not fully aligned with the internal variability of individual areas, with some information remaining in a private subspace and the rest being communicated thr

Diverse methods have been developed to visualize the representations learnt by artificial neural networks. Such visualization methods however are not easily adapted to biological systems. This prevents direct comparisons between the internal representations of artificial and biological neural systems. One promising approach to visualize sensory representations in biological systems is reverse correlation. Behavioural reverse correlation experiments leverage pareidolia, our tendency to detect spurious signals in noise, such as seeing shapes, objects, or faces in clouds. In these experiments, ob

…mechanisms for this coordination, the neural computations proposed by these models have yet to be experimentally validated. Two crucial barriers are (1) gaps in modeling approaches that integrate specific neural dynamics with realistic kinematics to connect predictions to observations and (2) the inherent difficulty in perturbing or recording from neurons during wal

Neural activity in frontal and motor cortices can be considered to be the manifestation of a dynamical system implemented by large neural populations in recurrently connected networks. The computations emerging from such population-level dynamics reflect the interaction between external inputs into a network and its internal, recurrent dynamics. Isolating these two contributions in experimentally recorded neural activity, however, is challenging, limiting the resulting insights into neural computations. I will present an approach to addressing this challenge based on response residuals, i.e. v

…great strides towards describing the neural computations underlying perceptual inference and decision-making under uncertainty. These tools tell us how and why perceptual illusions occur, which brain areas may represent noisy information in a probabilistic manner, and so on. However, an understanding of the subjective, qualitative aspects of perception remains elusive: qualia, or the personal, intrinsic properties of phenomenal awareness, have remained out of reach of these computational analytic insights. Here, I propose that metac

The description of neural computations currently relies on two competing views: (i) a classical single-cell view that aims to relate the activity of individual neurons to sensory or behavioural variables, and organize them into functional classes; (ii) a more recent population view that instead characterises computations in terms of collective neural trajectories, and focuses on the dimensionality of these trajectories as animals perform tasks. How the two key concepts of functional cell classes and low-dimensional trajectories interact to shape neural computations is however at present not un