Neural Sequence Generation Using Spatiotemporal Patterns of Inhibition

Abstract

Stereotyped sequences of neural activity are thought to underlie reproducible behaviors and cognitive processes ranging from memory recall to arm movement. One of the most prominent theoretical models of neural sequence generation is the synfire chain, in which pulses of synchronized spiking activity propagate robustly along a chain of cells connected by highly redundant feedforward excitation. But recent experimental observations in the avian song production pathway during song generation have shown excitatory activity interacting strongly with the firing patterns of inhibitory neurons, suggesting a process of sequence generation more complex than feedforward excitation. Here we propose a model of sequence generation inspired by these observations in which a pulse travels along a spatially recurrent excitatory chain, passing repeatedly through zones of local feedback inhibition. In this model, synchrony and robust timing are maintained not through redundant excitatory connections, but rather through the interaction between the pulse and the spatiotemporal pattern of inhibition that it creates as it circulates the network. These results suggest that spatially and temporally structured inhibition may play a key role in sequence generation.

Unstable neurons underlie a stable learned behavior

“…Local blockade of inhibition releases the sparse firing cells from inhibition and they begin to fire at multiple times in song 17 . Building on these observations, a recent modeling study suggested that inhibitory interneuron dynamics can increase the robustness of HVC dynamics to added noise 35 . These models can be made more precise in future experiments as additional cell-type specific information is measured.…”

Hidden neural states underlie canary song syntax

“…However, there are also clues that HVC does not contain all the information required to select a phrase transition -since more neurons correlate to the sequence's past than to its future it is possible that sequence information in HVC is lost, as expected from stochastic transitions. The source of residual stochasticity in HVC could be intrinsic to the dynamics of HVC -for example the "noise" terms commonly added in sequence generating models 13,38,39 or may enter downstream as well documented noise sources in the basal ganglia also converge on pre-motor cortical areas downstream of HVC. These early findings require further investigation of the neural dynamics during flexible transitions and may provide a tractable model for studying stochastic cognitive functions -mechanisms in working memory and sensory-motor integration that remain extremely challenging to quantify in most spontaneous behaviors in mammals.…”

Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning

“…Comparison with previous models. A large body of work has explored the generation and development of sequential activity in network models (11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(41)(42)(43). While many of these models focus on the reproduction of temporally sparse synfire chain activity, few address the growing number of observations that (outside of specialized neural systems such as Nucleus HVC) sequences are rarely consistent with such a simplified description.…”

“…In Nucleus HVC of zebra finch, highly-precise sequential activity is present during song production, where many neurons fire only a single short burst during a syllable (9). In primate motor cortex, single neurons are typically active throughout a whole reach movement, but with heterogeneous and rich dynamics (10).Numerous models have explored how networks with specific synaptic connectivity can generate sequential activity (11)(12)(13)(14)(15)(16)(17)(18)(19)(20). These models span a wide range of single neuron models, from binary to spiking, and a wide range of synaptic connectivities.…”