Shahab Bakhtiari

Cornelius Crijnen

Doctorat - UdeM

Ozhan Dehghani

Visiteur de recherche indépendant

hafez.ghaemi@umontreal.ca

Hafez Ghaemi

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Eilif Benjamin Muller

Site web

Google Scholar

Aidan Sirbu

Maîtrise recherche - McGill

Superviseur⋅e principal⋅e :

Liza Sycheva

Maîtrise recherche - UdeM

Charlotte Volk

Maîtrise recherche - McGill

Superviseur⋅e principal⋅e :

Publications

Asymmetric stimulus representations bias visual perceptual learning

Pooya Laamerad

Asmara Awada

Christopher C. Pack

The primate visual cortex contains various regions that exhibit specialization for different stimulus properties, such as motion, shape, and… (voir plus) color. Within each region there is often further specialization, such that particular stimulus features, such as horizontal and vertical orientations, are overrepresented. These asymmetries are associated with well-known perceptual biases, but little is known about how they influence visual learning. Most theories would predict that learning is optimal, in the sense that it is unaffected by these asymmetries. But other approaches to learning would result in specific patterns of perceptual biases. To distinguish between these possibilities, we trained human observers to discriminate between expanding and contracting motion patterns, which have a highly asymmetrical representation in visual cortex. Observers exhibited biased percepts of these stimuli, and these biases were affected by training in ways that were often suboptimal. We simulated different neural network models and found that a learning rule that involved only adjustments to decision criteria, rather than connection weights, could account for our data. These results suggest that cortical asymmetries influence visual perception and that human observers often rely on suboptimal strategies for learning.

2024-01-29

Journal of Vision (publié)

doi.org

Energy efficiency as a normative account for predictive coding

2022-12-09

Patterns (publié)

doi.org

The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning

Patrick J Mineault

Timothy P. Lillicrap

Christopher C. Pack

The visual system of mammals is comprised of parallel, hierarchical specialized pathways. Different pathways are specialized in so far as th… (voir plus)ey use representations that are more suitable for supporting specific downstream behaviours. In particular, the clearest example is the specialization of the ventral (“what”) and dorsal (“where”) pathways of the visual cortex. These two pathways support behaviours related to visual recognition and movement, respectively. To-date, deep neural networks have mostly been used as models of the ventral, recognition pathway. However, it is unknown whether both pathways can be modelled with a single deep ANN. Here, we ask whether a single model with a single loss function can capture the properties of both the ventral and the dorsal pathways. We explore this question using data from mice, who like other mammals, have specialized pathways that appear to support recognition and movement behaviours. We show that when we train a deep neural network architecture with two parallel pathways using a self-supervised predictive loss function, we can outperform other models in fitting mouse visual cortex. Moreover, we can model both the dorsal and ventral pathways. These results demonstrate that a self-supervised predictive learning approach applied to parallel pathway architectures can account for some of the functional specialization seen in mammalian visual systems.

openreview.net

Parallel inference of hierarchical latent dynamics in two-photon calcium imaging of neuronal populations

Luke Y. Prince

Colleen J Gillon

Dynamic latent variable modelling has provided a powerful tool for understanding how populations of neurons compute. For spiking data, such … (voir plus)latent variable modelling can treat the data as a set of point-processes, due to the fact that spiking dynamics occur on a much faster timescale than the computational dynamics being inferred. In contrast, for other experimental techniques, the slow dynamics governing the observed data are similar in timescale to the computational dynamics that researchers want to infer. An example of this is in calcium imaging data, where calcium dynamics can have timescales on the order of hundreds of milliseconds. As such, the successful application of dynamic latent variable modelling to modalities like calcium imaging data will rest on the ability to disentangle the deeper- and shallower-level dynamical systems’ contributions to the data. To-date, no techniques have been developed to directly achieve this. Here we solve this problem by extending recent advances using sequential variational autoencoders for dynamic latent variable modelling of neural data. Our system VaLPACa (Variational Ladders for Parallel Autoencoding of Calcium imaging data) solves the problem of disentangling deeper- and shallower-level dynamics by incorporating a ladder architecture that can infer a hierarchy of dynamical systems. Using some built-in inductive biases for calcium dynamics, we show that we can disentangle calcium flux from the underlying dynamics of neural computation. First, we demonstrate with synthetic calcium data that we can correctly disentangle an underlying Lorenz attractor from calcium dynamics. Next, we show that we can infer appropriate rotational dynamics in spiking data from macaque motor cortex after it has been converted into calcium fluorescence data via a calcium dynamics model. Finally, we show that our method applied to real calcium imaging data from primary visual cortex in mice allows us to infer latent factors that carry salient sensory information about unexpected stimuli. These results demonstrate that variational ladder autoencoders are a promising approach for inferring hierarchical dynamics in experimental settings where the measured variable has its own slow dynamics, such as calcium imaging data. Our new, open-source tool thereby provides the neuroscience community with the ability to apply dynamic latent variable modelling to a wider array of data modalities.

2021-03-08

bioRxiv (prépublication)

doi.org

Your head is there to move you around: Goal-driven models of the primate dorsal pathway

Patrick J Mineault