The centrality of population-level factors to network computation is demonstrated by a versatile approach for training spiking networks
Neural activity is often described in terms of population-level factors extracted from the responses of many neurons. Factors provide a lower-dimensional description with the aim of shedding light on network computations. Yet, mechanistically, computations are performed not by continuously valued factors but by interactions among neurons that spike discretely and variably. Models provide a means of bridging these levels of description. We developed a general method for training model networks of spiking neurons by leveraging factors extracted from either data or firing-rate-based networks. In addition to providing a useful model-building framework, this formalism illustrates how reliable and continuously valued factors can arise from seemingly stochastic spiking. Our framework establishes procedures for embedding this property in network models with different levels of realism. The relationship between spikes and factors in such networks provides a foundation for interpreting (and subtly redefining) commonly used quantities such as firing rates.
The butterfly strikes back: neurons doing 'network' computation
We live in the age of the network: Internet social neural ecosystems. This has become one of the main metaphors for how we think about complex systems. This view also dominates the account of brain function. The role of neuronsdescribed by Cajal as the "butterflies of the soul" has become diminished to leaky integrate-and-fire point objects in many models of neural network computation. It is perhaps not surprising that networkexplanations of neural phenomena use neurons as elementary particles andascribe all their wonderful capabilities to their interactions in a network. In the network view the Connectome defines the brain and the butterflies have no role. In this talk I'd like to reclaim some key computations from the networkand return them to their rightful place at the cellular and subcellular level. I'll start with a provocative look at potential computational capacity ofdifferent kinds of brain computation: network vs. subcellular. I'll then consider different levels of pattern and sequence computationwith a glimpse of the efficiency of the subcellular solutions. Finally I propose that there is a suggestive mapping between entire nodesof deep networks to individual neurons. This in my view is how we can walk around with 1.3 litres and 20 watts of installed computational capacity still doing far more than giant AI server farms.