An investigation of Hebbian phase sequences as assembly graphs.
Hebb proposed that synapses between neurons that fire synchronously are strengthened, forming cell assemblies and phase sequences. The former, on a shorter scale, are ensembles of synchronized cells that function transiently as a closed processing system; the latter, on a larger scale, correspond to the sequential activation of cell assemblies able to represent percepts and behaviors. Nowadays, the recording of large neuronal populations allows for the detection of multiple cell assemblies. Within Hebb's theory, the next logical step is the analysis of phase sequences. Here we detected phase sequences as consecutive assembly activation patterns, and then analyzed their graph attributes in relation to behavior. We investigated action potentials recorded from the adult rat hippocampus and neocortex before, during and after novel object exploration (experimental periods). Within assembly graphs, each assembly corresponded to a node, and each edge corresponded to the temporal sequence of consecutive node activations. The sum of all assembly activations was proportional to firing rates, but the activity of individual assemblies was not. Assembly repertoire was stable across experimental periods, suggesting that novel experience does not create new assemblies in the adult rat. Assembly graph attributes, on the other hand, varied significantly across behavioral states and experimental periods, and were separable enough to correctly classify experimental periods (Naïve Bayes classifier; maximum AUROCs ranging from 0.55 to 0.99) and behavioral states (waking, slow wave sleep, and rapid eye movement sleep; maximum AUROCs ranging from 0.64 to 0.98). Our findings agree with Hebb's view that assemblies correspond to primitive building blocks of representation, nearly unchanged in the adult, while phase sequences are labile across behavioral states and change after novel experience. The results are compatible with a role for phase sequences in behavior and cognition.
Brain Institute, Federal University of Rio Grande do Norte Natal, Brazil.
Daniel G
DG
Almeida-Filho
Brain Institute, Federal University of Rio Grande do Norte Natal, Brazil.
Vitor
V
Lopes-dos-Santos
Circuit Dynamics and Computation Laboratory, Champalimaud Neuroscience Programme Lisbon, Portugal ; Universitary Center of Rio Grande do Norte Natal, Brazil.
Nivaldo A P
NA
Vasconcelos
Physics Department, Federal University of Bahia Salvador, Brazil.
José G V
JG
Miranda
Brain Institute, Federal University of Rio Grande do Norte Natal, Brazil.
Adriano B L
AB
Tort
Brain Institute, Federal University of Rio Grande do Norte Natal, Brazil.
Sidarta
S
Ribeiro
D000200:Action Potentials / Q000502:physiology; D000818:Animals; D001499:Bayes Theorem; D006624:Hippocampus / Q000502:physiology*; D007858:Learning / Q000502:physiology*; D008297:Male; D019579:Neocortex / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D051381:Rats; D020318:Rats, Long-Evans; D013569:Synapses / Q000502:physiology*
cell assembly; graph; learning and memory; phase sequence; sleep