Neuroscience seminars

Brains can gracefully weed out irrelevant stimuli to guide behavior. This feat is believed to rely on a progressive selection of task-relevant stimuli across the cortical hierarchy, but the specific across-area interactions enabling stimulus selection are still unclear. Here, we propose that population gating, occurring within A1 but controlled by top-down inputs from mPFC, can support across-area stimulus selection. Examining single-unit activity recorded while rats performed an auditory context-dependent task, we found that A1 encoded relevant and irrelevant stimuli along a common dimension of its neural space. Yet, the relevant stimulus encoding was enhanced along an extra dimension. In turn, mPFC encoded only the stimulus relevant to the ongoing context. To identify candidate mechanisms for stimulus selection within A1, we reverse-engineered low-rank RNNs trained on a similar task. Our analyses predicted that two context-modulated neural populations gated their preferred stimulus in opposite contexts, which we confirmed in further analyses of A1. Finally, we show in a two-region RNN how population gating within A1 could be controlled by top-down inputs from PFC, enabling flexible across-area communication despite fixed inter-areal connectivity.

Physiological experiments have highlighted how the dendrites of biological neurons can nonlinearly process distributed synaptic inputs. However, it is unclear how qualitative aspects of a dendritic tree, such as its branched morphology, its repetition of presynaptic inputs, voltage-gated ion channels, electrical properties and complex synapses, determine neural computation beyond this apparent nonlinearity. While it has been speculated that the dendritic tree of a neuron can be seen as a multi-layer neural network and it has been shown that such an architecture could be computationally strong, we do not know if that computational strength is preserved under these qualitative biological constraints. Here we simulate multi-layer neural network models of dendritic computation with and without these constraints. We find that dendritic model performance on interesting machine learning tasks is not hurt by most of these constraints and may synergistically benefit from all of them combined. Our results suggest that single real dendritic trees may be able to learn a surprisingly broad range of tasks through the emergent capabilities afforded by their properties.

Behavioral studies have demonstrated that certain task features reliably enhance classification performance for challenging visual stimuli. These include extended image presentation time and the valid cueing of attention. Here, I will show how convolutional neural networks can be used as a model of the visual system that connects neural activity changes with such performance changes. Specifically, I will discuss how different anatomical forms of recurrence can account for better classification of noisy and degraded images with extended processing time. I will then show how experimentally-observed neural activity changes associated with feature attention lead to observed performance changes on detection tasks. I will also discuss the implications these results have for how we identify the neural mechanisms and architectures important for behavior.

This course provides a fundamental foundation in the modern techniques of experimental neuroscience. It introduces the essentials of sensors, motor control, microcontrollers, programming, data analysis, and machine learning by guiding students through the “hands on” construction of an increasingly capable robot. In parallel, related concepts in neuroscience are introduced as nature’s solution to the challenges students encounter while designing and building their own intelligent system.

The hippocampus is one of the few regions in the adult mammalian brain which is endowed with life-long neurogenesis. Despite intense investigation, it remains unclear how neurons newly-generated may retain unique functions that contribute to modulate hippocampal information processing and cognition. In this talk, I will present some recent findings revealing how enhanced forms of plasticity in adult-born neurons underlie the way they become incorporated into pre-existing networks in response to experience.

Cognitive control guides behavior by controlling what, where, and how information is represented in the brain. Perhaps the most well-studied form of cognitive control has been ‘attention’, which controls how external sensory stimuli are represented in the brain. In contrast, the neural mechanisms controlling the selection of representations held ‘in mind’, in working memory, are unknown. In this talk, I will present evidence that the prefrontal cortex controls working memory by selectively enhancing and transforming the contents of working memory. In particular, I will show how the neural representation of the content of working memory changes over time, moving between different ‘subspaces’ of the neural population. These dynamics may play a critical role in controlling what and how neural representations are acted upon.

This webinar will demonstrate changes that are already happening at individual, institutional and funder level to shift research culture toward supporting credible research, and will allow attendees working in neuroscience to ask further questions to our speakers. Our panel of speakers, chaired by Ana Dorrego-Rivas: Emily Farran, Professor in Developmental Psychology and Academic Lead Research Culture and Integrity at the University of Surrey Rosa Sancho, Head of Research at Alzheimer's Research UK Sepideh Keshavarzi, Senior Research Fellow at the Sainsbury Wellcome Centre

On Wednesday, November 30th, at noon ET / 6PM CET, we will host Alexandre Boutet and Erik H. Middlebrooks. Alexandre Boutet, MD, PhD, is a neuroradiology fellow at the University of Toronto, and will tell us about “How to become an integral part of the functional neurosurgery team as a radiologist”. Erik H. Middlebrooks, MD, is a Professor and Consultant of Neuroradiology and Neurosurgery and the Neuroradiology Program Director at Mayo Clinic. Beside his scientific presentation about “Bridging the Gap from Research to Clinical Decision Making in Epilepsy Neuromodulation”, he will also give us a glimpse at the “Person behind the science”. The talks will be followed by a shared discussion. You can register via talks.stimulatingbrains.org to receive the (free) Zoom link!

In this talk, we propose to decipher the activity of neural networks via a “multiply and conquer” approach. This approach considers limit networks made of infinitely many replicas with the same basic neural structure. The key point is that these so-called replica-mean-field networks are in fact simplified, tractable versions of neural networks that retain important features of the finite network structure of interest. The finite size of neuronal populations and synaptic interactions is a core determinant of neural dynamics, being responsible for non-zero correlation in the spiking activity and for finite transition rates between metastable neural states. Theoretically, we develop our replica framework by expanding on ideas from the theory of communication networks rather than from statistical physics to establish Poissonian mean-field limits for spiking networks. Computationally, we leverage our original replica approach to characterize the stationary spiking activity of various network models via reduction to tractable functional equations. We conclude by discussing perspectives about how to use our replica framework to probe nontrivial regimes of spiking correlations and transition rates between metastable neural states.

Understanding the brain is not only intrinsically fascinating, but also highly relevant to increase our well-being since our brain exhibits a power over the body that makes it capable both of provoking illness or facilitating the healing process. Bearing in mind this dark force, brain sciences have undergone and will undergo an important revolution, redefining its boundaries beyond the cranial cavity. During this presentation, we will discuss about the communication between the brain and other systems that shapes how we feel the external word and how we think. We are starting to unravel how our organs talk to the brain and how the brain talks back. That two-way communication encompasses a complex, body-wide system of nerves, hormones and other signals that will be discussed. This presentation aims at challenging a long history of thinking of bodily regulation as separate from "higher" mental processes. Four centuries ago, René Descartes famously conceptualized the mind as being separate from the body, it is time now to embody our mind.

Cerebrovascular support is critical for healthy cognitive ageing. Reduced cerebral blood flow in ageing is caused, among other things, by hypertension, arteriosclerosis (i.e. stiffening of the arteries) and plaque formation. Arterial stiffness is predictive of cognitive decline, is a critical risk factor for cerebrovascular accidents, and has been linked to heightened risks for Alzheimer’s Disease and other forms of dementia. The elasticity of cerebral arteries is influenced by lifestyle factors, including cardiorespiratory fitness. Monica will discuss data obtained in their laboratory with new noninvasive measures of cerebrovascular health (pulse-DOT, a diffuse optical tomographic method for studying cerebral arteriosclerosis), in conjunction with structural and functional brain measures and cognitive assessments. These findings support a model in which localised changes in arteriosclerosis lead to specific profiles of structural, functional, and cognitive declines, paving a way to individualised interventions.

Post-diagnostic research on rare genetic developmental disorders presents new opportunities (and a few challenges) for discovery neuroscience and translation. In this talk, Kate will describe and discuss neurodevelopmental phenotypes arising from rare, high penetrance genomic variants which directly influence pre-synaptic vesicle cycling (SVC disorders). She will focus on Synaptotagmin-1 Associated Neurodevelopmental Disorder (also known as Baker Gordon Syndrome), first described in 2015 and now diagnosed in more than 50 children and young people worldwide. She will then present work-in-progress by her group on the neurodevelopmental spectrum of SVC disorders more broadly, and discuss opportunities for collaborative neuroscience which can bridge the gaps between genetic cause and complex neurological, cognitive and mental health outcomes.

Several species of insects have been observed to perform accurate path integration, constantly updating a vector memory of their location relative to a starting position, which they can use to take a direct return path. Foraging insects such as bees and ants are also able to store and recall the vectors to return to food locations, and to take novel shortcuts between these locations. Other insects, such as dung beetles, are observed to integrate multimodal directional cues in a manner well described by vector addition. All these processes appear to be functions of the Central Complex, a highly conserved and strongly structured circuit in the insect brain. Modelling this circuit, at the single neuron level, suggests it has general capabilities for vector encoding, vector memory, vector addition and vector rotation that can support a wide range of directed and navigational behaviours.

We recorded and analyzed the population activity of hundreds of neurons in the medial premotor areas (MPC) of rhesus monkeys performing an isochronous tapping task guided by brief flashing stimuli or auditory tones. The animals showed a strong bias towards visual metronomes, with rhythmic tapping that was more precise and accurate than for auditory metronomes. The population dynamics in state space as well as the corresponding neural sequences shared the following properties across modalities: the circular dynamics of the neural trajectories and the neural sequences formed a regenerating loop for every produced interval, producing a relative time representation; the trajectories converged in similar state space at tapping times while the moving bumps restart at this point, resetting the beat-based clock; the tempo of the synchronized tapping was encoded by a combination of amplitude modulation and temporal scaling in the neural trajectories. In addition, the modality induced a displacement in the neural trajectories in auditory and visual subspaces without greatly altering time keeping mechanism. These results suggest that the interaction between the amodal internal representation of pulse within MPC and a modality specific external input generates a neural rhythmic clock whose dynamics define the temporal execution of tapping using auditory and visual metronomes.

Faces and voices convey much of the non-verbal information that we use when communicating with other people. We look at faces and listen to voices to recognize others, understand how they are feeling, and decide how to act. Recent research in my lab aims to investigate whether there are similar coding mechanisms to represent faces and voices, and whether there are brain regions that integrate information across the visual and auditory modalities. In the first part of my talk, I will focus on an fMRI study in which we found that a region of the posterior STS exhibits modality-general representations of familiar people that can be similarly driven by someone’s face and their voice (Tsantani et al. 2019). In the second part of the talk, I will describe our recent attempts to shed light on the type of information that is represented in different face-responsive brain regions (Tsantani et al., 2021).