August 5, 2017

Richards BA, Frankland PW, “The Persistence and Transience of Memory”, Neuron, 94 (6), 1071-1074

The predominant focus in the neurobiological study of memory has been on remembering (persistence). However, recent studies have considered the neurobiology of forgetting (transience). Here we draw parallels between neurobiological and computational mechanisms underlying transience. We propose that it is the interaction between persistence and transience that allows for intelligent decision-making in dynamic, noisy environments. Specifically, we argue that transience (1) enhances flexibility, by reducing the influence of outdated information on memory-guided decision-making, and (2) prevents overfitting to specific past events, thereby promoting generalization. According to this view, the goal of memory is not the transmission of information through time, […]
January 31, 2017

Raimondo JV, Richards BA, Woodin MA, “Neuronal chloride and excitability — the big impact of small changes”, Current Opinion in Neurobiology, 43, 35-42

Synaptic inhibition is a critical regulator of neuronal excitability, and in the mature brain the majority of synaptic inhibition is mediated by Cl−-permeable GABAA receptors. Unlike other physiologically relevant ions, Cl− is dynamically regulated, and alterations in the Cl−gradient can have significant impact on neuronal excitability. Due to changes in the neuronal Cl− concentration, GABAergic transmission can bidirectionally regulate the induction of excitatory synaptic plasticity and gate the closing of the critical period for monocular deprivation in visual cortex. GABAergic circuitry can also provide a powerful restraining mechanism for the spread of excitation, however Cl− extrusion mechanisms can become overwhelmed […]
January 31, 2017

Guerguiev J, Lillicrap TP, Richards BA, “Towards deep learning with segregated dendrites”

Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep learning algorithm that utilizes multi-compartment neurons might help us to understand how the neocortex optimizes cost functions. Like neocortical pyramidal neurons, neurons in our model receive sensory information and higher-order feedback in electrotonically segregated compartments. Thanks to this segregation, neurons in different layers of the network can coordinate synaptic weight updates. As a result, the network learns to categorize images better than a […]