2016/01/28 by Brian DePasquale, Mark M. Churchland, DePasquale, Brian +3
Biochemistry, Genetics and Molecular Biology · Engineering · Neuroscience · Physics and Astronomy · #Advanced Memory and Neural Computing #FOS: Biological sciences #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC) #q-bio.NC #stochastic dynamics and bifurcation
paper · pdf · doi:10.48550/arxiv.1601.07620
17 pages, 6 figures
arxiv created 2016/01/28 · openalex publication_date 2016/01/28 · arxiv updated 2016/01/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recurrent neural networks are powerful tools for understanding and modeling computation and representation by populations of neurons. Continuous-variable or "rate" model networks have been analyzed and applied extensively for these purposes. However, neurons fire action potentials, and the discrete nature of spiking is an important feature of neural circuit dynamics. Despite significant advances, training recurrently connected spiking neural networks remains a challenge. We present a procedure for training recurrently connected spiking networks to generate dynamical patterns autonomously, to produce complex temporal outputs based on integrating network input, and to model physiological data. Our procedure makes use of a continuous-variable network to identify targets for training the inputs to the spiking model neurons. Surprisingly, we are able to construct spiking networks that duplicate tasks performed by continuous-variable networks with only a relatively minor expansion in the number of neurons. Our approach provides a novel view of the significance and appropriate use of "firing rate" models, and it is a useful approach for building model spiking networks that can be used to address important questions about representation and computation in neural systems.