Biology subjects

Clark, B. A.

Publications and source records attributed to Clark, B. A..

3 recordsLinked to original sources

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↗

bioRxiv2024

A latent pool of neurons silenced by sensory-evoked inhibition can be recruited to enhance perception

Which patterns of neural activity in sensory cortex are relevant for perceptual decision-making? To address this question, we used simultaneous two-photon calcium imaging and targeted two-photon optogenetics to probe barrel cortex activity during a perceptual discrimination task. Head-fixed mice discriminated bilateral whisker deflections and reported decisions by licking left or right. Two-photon calcium imaging revealed sparse coding of contralateral and ipsilateral whisker input in layer 2/3 while most neurons did not show task-related activity. Activating small groups of pyramidal neurons using two-photon holographic photostimulation evoked a perceptual bias that scaled with the number of neurons photostimulated. This effect was dominated by the optogenetic activation of a small number of non-coding neurons, which did not show sensory or motor-related activity during task performance. Patterned photostimulation also revealed potent recruitment of cortical inhibition during sensory processing, which strongly and preferentially suppressed non-coding neurons. Our results provide a novel perspective on the circuit basis for the sparse coding model of somatosensory processing in which a pool of non-coding neurons, selectively suppressed by strong network inhibition during whisker stimulation, can be recruited to enhance perception. HighlightsO_LIAll-optical interrogation of barrel cortex during bilateral whisker discrimination C_LIO_LISparse coding of contralateral and ipsilateral whisker information C_LIO_LISelective sensory-evoked inhibition helps ensure sparse coding C_LIO_LIOptogenetic recruitment of stimulus non-coding neurons can aid perception C_LI

neuroscience↗

bioRxiv2024

A deep-learning strategy to identify cell types across species from high-density extracellular recordings

High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but dont reveal cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals, revealing the computational roles of neurons with distinct functional, molecular, and anatomical properties. We combine optogenetic activation and pharmacology using the cerebellum as a testbed to generate a curated ground-truth library of electrophysiological properties for Purkinje cells, molecular layer interneurons, Golgi cells, and mossy fibers. We train a semi-supervised deep-learning classifier that predicts cell types with greater than 95% accuracy based on waveform, discharge statistics, and layer of the recorded neuron. The classifiers predictions agree with expert classification on recordings using different probes, in different laboratories, from functionally distinct cerebellar regions, and across animal species. Our classifier extends the power of modern dynamical systems analyses by revealing the unique contributions of simultaneously-recorded cell types during behavior.

neuroscience↗