Jonathan H. Cornford

The paper introduces a plasticity-switching recurrent neural network (psRNN) that models synaptic state-dependent plasticity and shows it improves learning efficiency in cognitive tasks.

This paper introduces exponentiated gradient (EG) as a learning algorithm for artificial neural networks that better aligns with biological principles than gradient descent.

This paper investigates how incorporating Dale's Law into recurrent neural networks affects learning, finding that spectral properties of the weight matrix are more critical than sign constraints for performance.

This study shows that theta-burst activity in entorhinal cortex afferents induces Hebbian LTP in neurogliaform interneurons, increasing disynaptic inhibition of CA1 pyramidal neurons and shifting excitation-inhibition balance.

Splitting network units into excitatory and inhibitory populations need not cost learning: initialize inhibition to centre and standardize excitatory activity, and scale inhibitory updates by Fisher information.

This study uses CRISPRa to increase Kv1.1 expression in mouse hippocampal neurons, reducing seizures and cognitive deficits in a temporal lobe epilepsy model.

This study shows that dendritic NMDA receptors in parvalbumin neurons enable cooperative recruitment of interneurons, strengthening and stabilizing principal cell assemblies.

This study shows that overexpressing KCC2 in principal neurons prevents PV+ interneurons from promoting seizures during seizure onset.

This paper develops a non-integrating lentiviral gene therapy for refractory focal epilepsy, showing reduced seizure frequency in a rat model.