Pogodin, R. | bioRxiv Science

bioRxiv2026

Increased activity without changes in coupling between somata and apical dendrites during visual mismatches

Leading theories of cortical function posit that sensory processing involves a comparison between top-down predictions and bottom-up sensory data. Tests of these theories have been hampered by experimental challenges related to simultaneously recording top-down and bottom-up signals in response to sensory stimuli that either do or do not match internally-generated predictions. Here, we conduct dual-plane 2-photon calcium imaging near-simultaneously from distal apical dendrites and somata of pyramidal neurons in each of 4 different regions of mouse visual cortex while the mice are presented with visual sequences that can either match or mismatch previously habituated visual stimuli. Specifically, we examine the responses in the distal apical dendrites and cell bodies of pyramidal neurons while mice view either movie clips that reverse unexpectedly or Gabor patch sequences that have orientations that do not match habituated patterns. We find that in response to unexpected reversals in natural movies, both the apical dendrites and somata show increased activity that decreases over repeated exposures, consistent with a prediction error response. However, we do not find any clear evidence for increased activity in response to mismatches in Gabor sequences, beyond possible release from adaptation effects. We additionally use volumetric images to connect dendritic and somatic compartments and examine the coupling between connected dendrites and cell bodies in layer 5 pyramidal neurons. We do not find evidence for changes in coupling in response to mismatch stimuli, either for natural movies or Gabor sequences. Our results are consistent across four regions of visual cortex. We also observe that increased responses to reversals in natural movies are stronger in layer 2/3 than layer 5 pyramidal neurons. Altogether, these data suggest that visual cortex is sensitive to deviations from previously-experienced naturalistic inputs. Our data do not suggest that this mismatch sensitivity extends to artificial stimuli, nor do they support theories of prediction error causing changes in the coupling between apical dendrites and somata.

neuroscience↗

bioRxiv2024

Brain-like learning with exponentiated gradients

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 Dales 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 optimisation theory, we present an alternative learning algorithm, exponentiated gradient (EG), that respects Dales Law and produces log-normal weights, without losing the power of learning with gradients. We also 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.

neuroscience↗