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Saturday, July 11
 

5:20pm ADT

Keynote 1: Bratislav Misic
Saturday July 11, 2026 5:20pm - 6:20pm ADT

Saturday July 11, 2026 5:20pm - 6:20pm ADT
Ballroom B1
 
Sunday, July 12
 

9:10am ADT

Keynote 2: Mac Shine, "The Neurobiological Basis of Consciousness"
Sunday July 12, 2026 9:10am - 10:10am ADT
Understanding the neural basis of consciousness requires mechanistic accounts that span multiple scales of brain organisation. Yet most existing theoretical frameworks operate at the macroscale, offering systems-level predictions without prescribing the cellular and circuit-level mechanisms that implement them. Here I argue that the multiscale architecture of the thalamocortical system offers a principled solution to this problem. Drawing on theoretical and computational work from our group, I show how the core/matrix organisation of the thalamus, in combination with the nonlinear dendritic integration properties of L5B pyramidal neurons, generates the conditions necessary for both the global state of consciousness and the specific contents of experience. A biophysical microcircuit model, extended to a corticothalamic neural mass framework, reproduces key empirical phenomena across multiple perturbation regimes - including anaesthesia, optogenetic manipulation, and pharmacological intervention - and makes predictions at the macroscale that are consistent with leading theoretical accounts.


Sunday July 12, 2026 9:10am - 10:10am ADT
Ballroom B1
 
Monday, July 13
 

9:10am ADT

Keynote 3: Blake Richards, "Exponentiated gradients support effective learning in biologically relevant scenarios"
Monday July 13, 2026 9:10am - 10:10am ADT
Computational neuroscience relies on gradient descent (GD) for training artificial neural network (ANN) models of the brain. The advantage of GD is that it is effective at learning difficult tasks. However, it produces ANNs that are a poor phenomenological fit to biology, making them less relevant as models of the brain. Specifically, it violates Dale’s law, by allowing synapses to change from excitatory to inhibitory, and leads to synaptic weights that are not log-normally distributed, contradicting experimental data. Here, starting from first principles of optimization theory, I will present an alternative learning algorithm, exponentiated gradient (EG), that respects Dale’s Law and produces log-normal weights, without losing the power of learning with gradients. We show that in biologically relevant settings EG outperforms GD, including learning from sparsely relevant signals and dealing with synaptic pruning. Altogether, our results show that EG is a superior learning algorithm for modelling the brain with ANNs.


Monday July 13, 2026 9:10am - 10:10am ADT
Ballroom B1
 
Tuesday, July 14
 

2:10pm ADT

Keynote 4
Tuesday July 14, 2026 2:10pm - 3:20pm ADT

Tuesday July 14, 2026 2:10pm - 3:20pm ADT
Ballroom B1
 
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