Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning.
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Sequential activity has been observed in multiple neuronal circuits across species, neural structures, and behaviors. It has been hypothesized that sequences could arise from learning processes. However, it is still unclear whether biologically plausible synaptic plasticity rules can organize neuronal activity to form sequences whose statistics match experimental observations. Here, we investigate temporally asymmetric Hebbian rules in sparsely connected recurrent rate networks and develop a theory of the transient sequential activity observed after learning. These rules transform a sequence of random input patterns into synaptic weight updates. After learning, recalled sequential activity is reflected in the transient correlation of network activity with each of the stored input patterns. Using mean-field theory, we derive a low-dimensional description of the network dynamics and compute the storage capacity of these networks. Multiple temporal characteristics of the recalled sequential activity are consistent with experimental observations. We find that the degree of sparseness of the recalled sequences can be controlled by nonlinearities in the learning rule. Furthermore, sequences maintain robust decoding, but display highly labile dynamics, when synaptic connectivity is continuously modified due to noise or storage of other patterns, similar to recent observations in hippocampus and parietal cortex. Finally, we demonstrate that our results also hold in recurrent networks of spiking neurons with separate excitatory and inhibitory populations.
Neural Networks, Computer
Published Version (Please cite this version)10.1073/pnas.1918674117
Publication InfoGillett, Maxwell; Pereira, Ulises; & Brunel, Nicolas (2020). Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning. Proceedings of the National Academy of Sciences of the United States of America, 117(47). pp. 29948-29958. 10.1073/pnas.1918674117. Retrieved from https://hdl.handle.net/10161/23343.
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Duke School of Medicine Distinguished Professor in Neuroscience
We use theoretical models of brain systems to investigate how they process and learn information from their inputs. Our current work focuses on the mechanisms of learning and memory, from the synapse to the network level, in collaboration with various experimental groups. Using methods fromstatistical physics, we have shown recently that the synapticconnectivity of a network that maximizes storage capacity reproducestwo key experimentally observed features: low connection proba
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