Research

Learning in biological and artificial systems

While much of our cognitive abilities are determined by evolution, learning gives us the ability to improve our behaviours beyond our innate abilities. Learning involves updating synapses, the points of contact between neurons in our brain. While current large language models can have up to 1 trillion parameters (mathematical equivalent of synapses), the human brain has an estimated 700 trillion synapses. Artificial models like Chat-GPT involve propagating errors made during training to update artificial synapses, a processes referred to as gradient descent of backpropagated signed error. In the brain, and in particular in regions like the cortex, information flows in multiple directions (bottom-up and top-down), and hypothetically, some neurons could serve an analogous function of 'propagating error' so as to update synapses.

Simplified diagram of the bi-directional hierarchical microcircuit in the cortex. Neurons in layer II/III tend to project to deeper, higher-order neurons in the hierarchy, while neurons in layer V tend to send feedback projections to lower-order neurons.

My research is at the intersection between neuroscience and artificial intelligence and aims at establishing how networks of biological neurons coordinate their activities to propagate errors and update synapses, in turn supporting learning and higher-order cognition.

How to predict the future

As we explore the world, we collect information through our sensory modalities (sound, vision, touch, ...). These online signals help us build a model of the external world, and understand the rules that govern it. This internal knowledge can then be leverage to make predictions and hypotheses about the future, which are then tested against incoming sensory signals. This loop of testing predictions based on our internal knowledge could be essential to cognition, and more precisely, learning of new information. A particular brain region, called the hippocampus, contains neurons that constantly represent past and future events in space and time, and could support this function. A portion of my research has been to record the activity of these neurons and understand how they could contribute to learning and memory.

Neurons in the hippocampus tend to fire in a sequential order that depends on connectivity and is established at birth. When animals explore environments, these neurons fire sequentially (~8 times per second) with respect to local oscillations, and represent past, current, and potential future states.

Learning rules in biological neural networks

Learning involves updating synapses, the points of contact between neurons. While this phenomenon has been widely described in vitro, the exact mechanisms of synaptic updates in vivo remain misunderstood. One hypothesis is that, when predictions are validated by incoming sensory stimuli, neurons can be depolarized (activated) beyond their typical state, in turn facilitating the formation of new synapses (and enhancing existing ones). The exact mechanisms behind this phenomenon remain unknown, but could involve a local microcircuit including inhibitory interneurons. The aim of this research axis is to test local learning rules using a deep learning framework in silico, and test these hypotheses in vivo using voltage imaging of excitatory and inhibitory neurons within microcircuits.

Top: pyramidal neurons, which are most prevalent in the cortex and hippocampus, integrate information at their based (green) and apical (red) dendrites. Apical dendrites are thought to integrate predictions from higher-order neurons, whereas basal dendrites would integrate sensory signals predominantly. Only when sensory signals are predicted by higher-order neurons, pyramidal neurons emit bursts of action potentials.

Bottom: as animals explore environments, these bursts could in turn trigger plasticity events, update synapses, and induce long-term changes in representations.

Role of sleep in cognition

We spend an enormous amount of our life sleeping (and dreaming). Yet, the exact function of sleep remains largely misunderstood. Using large scale recording of thousands of neurons during sleep and awake experience, one of my objectives is to determine the computational role of sleep in cognition.

Neurons in the hippocampus and cortex establish their connectivity at birth (structural priors). These priors can dictate the expression of internal sequences that do not depend on sensory inputs or experiences. Experiences acquired during awake exploration can be replayed during subsequent REM sleep episodes. Alternatively, internal sequences present during REM sleep can be re-expressed in awake states (preplay).

Novel strategies to predict and treat Alzheimer's disease

As the world population continues to age, more and more people are affected by Alzheimer's disease and associated dementia. This prompts the need for early diagnostic, and non-invasive targeted treatments. Using machine learning, my research aims to discover features in neural recordings (single cell and local field potential) to predict cognitive decline as early as possible. Additionally, I have been developing red-shifted optogenetics, where light can pass through the skull to activate transfected neurons deep in the brain without the need for invasive brain implants.

Left: Typical optogenetic actuators (e.g. ChR2) are most activated by blue light (~450nm) which necessitates light delivery close to targets (using fiber optic implants). On the other hand, red-shifted actuators (e.g. ChrimsonR) can be activated through the cranium using red lasers, thus removing the need for invasive brain implants.

Right: using deep learning models, neural activities from healthy and Alzheimer's disease conditions can be encoded in low-dimensional manifolds. Subsequently, neural signatures and predictors can be extracted from these low-dimensional embeddings.

Technical approaches

Calcium imaging

Using open-source miniaturized head-mounted microscopes (miniscopes), we can record activity from large assemblies (>1,000) neurons over extended periods of time (>2 years) for affordable costs. Here is an example of a few neurons in hippocampal subregion CA1 using 1-photon in vivo imaging.

Optogenetics

Optogenetics allow targeted dissection of neural circuits. Using viral transfections with cell-type specificity (e.g. targetting inhibitory interneurons only). From Etter et al., 2023b.

In vivo electrophysiology

Example of a 16 channels silicon probe recording in the hippocampus during a sharp-wave ripple event. The darker layer corresponds to the pyramidal cell layer (corresponding local field potential signal in green). From Etter et al., 2019.

Ex vivo electrophysiology

Whole cell patch clamp electrophysiology of a hippocampal mossy cell, which gives access to synaptic and intracellular mechanisms. Image: Etter et al., 2014.

Blake Richards (McGill University/MILA): bioplausible deep learning learning
Daniel Aharoni (UCLA): miniscope hardware and software development
Massimo Avoli (McGill University): therapeutical uses of optogenetics in epilepsy
Keith Murai (McGill University): gene expression in interneurons

Affiliations (past and present) and funding agencies

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