scholarly journals Neural Field Models for Latent State Inference: Application to Large-Scale Neuronal Recordings

2019 ◽  
Author(s):  
M. E. Rule ◽  
D. Schnoerr ◽  
M. H. Hennig ◽  
G. Sanguinetti

AbstractLarge-scale neural recordings are becoming increasingly better at providing a window into functional neural networks in the living organism. Interpreting such rich data sets, however, poses fundamental statistical challenges. The neural field models of Wilson, Cowan and colleagues remain the mainstay of mathematical population modeling owing to their interpretable, mechanistic parameters and amenability to mathematical analysis. We developed a method based on moment closure to interpret neural field models as latent state-space point-process models, making mean field models amenable to statistical inference. We demonstrate that this approach can infer latent neural states, such as active and refractory neurons, in large populations. After validating this approach with synthetic data, we apply it to high-density recordings of spiking activity in the developing mouse retina. This confirms the essential role of a long lasting refractory state in shaping spatio-temporal properties of neonatal retinal waves. This conceptual and methodological advance opens up new theoretical connections between mathematical theory and point-process state-space models in neural data analysis.SignificanceDeveloping statistical tools to connect single-neuron activity to emergent collective dynamics is vital for building interpretable models of neural activity. Neural field models relate single-neuron activity to emergent collective dynamics in neural populations, but integrating them with data remains challenging. Recently, latent state-space models have emerged as a powerful tool for constructing phenomenological models of neural population activity. The advent of high-density multi-electrode array recordings now enables us to examine large-scale collective neural activity. We show that classical neural field approaches can yield latent statespace equations and demonstrate inference for a neural field model of excitatory spatiotemporal waves that emerge in the developing retina.

2019 ◽  
Author(s):  
Scott Linderman ◽  
Annika Nichols ◽  
David Blei ◽  
Manuel Zimmer ◽  
Liam Paninski

AbstractModern recording techniques enable large-scale measurements of neural activity in a variety of model organisms. The dynamics of neural activity shed light on how organisms process sensory information and generate motor behavior. Here, we study these dynamics using optical recordings of neural activity in the nematode C. elegans. To understand these data, we develop state space models that decompose neural time-series into segments with simple, linear dynamics. We incorporate these models into a hierarchical framework that combines partial recordings from many worms to learn shared structure, while still allowing for individual variability. This framework reveals latent states of population neural activity, along with the discrete behavioral states that govern dynamics in this state space. We find stochastic transition patterns between discrete states and see that transition probabilities are determined by both current brain activity and sensory cues. Our methods automatically recover transition times that closely match manual labels of different behaviors, such as forward crawling, reversals, and turns. Finally, the resulting model can simulate neural data, faithfully capturing salient patterns of whole brain dynamics seen in real data.


1997 ◽  
Vol 272 (2) ◽  
pp. R532-R540 ◽  
Author(s):  
K. Ota ◽  
T. Katafuchi ◽  
A. Takaki ◽  
T. Hori

The single neuron activity in the anteroventral region of the third ventricle (AV3V) was extracellularly recorded in urethan and alpha-chloralose anesthetized rats. Electrical stimulation of the medial preoptic area (mPOA) and the paraventricular nucleus (PVN) revealed a reciprocal neural connection between the AV3V and these hypothalamic nuclei with an ipsilateral preponderance. All the AV3V neurons, which were antidromically activated by the stimulation of the mPOA or the PVN, altered their activity after the systemic injection of interleukin (IL)-1beta. On the other hand, only about 60% of the AV3V neurons that showed orthodromic responses were affected by IL-1beta. In seven of nine AV3V neurons that were electrophysiologically identified to send their axons to the mPOA or the PVN, the recombinant human IL-1beta-induced excitation and inhibition were attenuated by a local application of sodium salicylate through multibarreled micropipettes. These results suggest that the AV3V neurons alter their activity in response to the blood-borne IL-1beta, at least in part, through a local synthesis of prostanoids and then send the information to the mPOA and PVN.


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