How neuronal network circuit attributes influence neural activity, coding, and learning

Monday, June 14 at 11:30am (PDT)
Monday, June 14 at 07:30pm (BST)
Tuesday, June 15 03:30am (KST)

SMB2021 SMB2021 Follow Monday (Tuesday) during the "MS02" time block.
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Cheng Ly (Virginia Commonwealth University, United States), Pamela Pyzza (Kenyon College, United States)


The complexities of neural sensory systems currently cannot be elucidated by experiments alone. The detailed circuit electrophysiology at the cellular level, as well as large-scale images require contemporary mathematics and computation to gain further insights into how they function. This mini-symposium brings together a broad group of researchers who will discuss their approaches to using applied mathematics and computation to understand experimental data collected from neural recordings under healthy and/or pathological conditions. The researchers will focus on topics range from detailing circuit mechanisms of neuron spike variability, neuronal connectivity, olfaction, to population activity in relation to neural coding.

Paulina Volosov

(Hillsdale College, United States)
"How to Use Minimal Information to Reconstruct Neuronal Networks"
We investigate the relationship between functional and architectural connectivity in the cerebral cortex by means of network reconstruction via time-delayed spike-train correlation. We begin by reconstructing the entire network, and then we sample the matrix randomly and use the tool of matrix completion to fill-in the rest of the network. To be more experimentally valid, we next examine a small “slice” or submatrix of the network and determine how much information we can deduce about the whole network from this small piece. An examination of the spectral properties of connectivity matrices forms a major part of this analysis.

Michelle Craft

(Virginia Commonwealth University, United States)
"Analyzing the differences in olfactory bulb spiking with ortho- and retronasal stimulation"
Olfaction is a key sense for many cognitive and behavioral tasks, and is particularly unique because odors can naturally enter the nasal cavity from the front or rear, i.e., ortho- and retro-nasal, respectively. Yet little is known about the differences in coordinated spiking in the olfactory bulb (a key odor processing center) with ortho versus retro stimulation, let alone how these different modes of olfaction may alter coding of odors. We simultaneously record many cells in rat olfactory bulb to assess the differences in spiking statistics, and develop a biophysical olfactory bulb network model to study the reason for these differences. Using theoretical and computational methods, we find that the olfactory bulb transfers input statistics differently for retro stimulation relative to ortho stimulation. Furthermore, our models show that the temporal profile of inputs is crucial for capturing our data and is thus a distinguishing feature between ortho and retro stimulation, even at the olfactory bulb. Understanding the spiking dynamics of the olfactory bulb with both ortho and retro stimulation is a key step for ultimately understanding how the brain codes odors with different modes of olfaction.

Andrea Barreiro

(Southern Methodist University, United States)
"Cell assembly detection in low firing-rate spike train data"
Cell assemblies, defined as groups of neurons forming temporal spike coordination, are thought to be fundamental units supporting major cognitive functions. Detecting cell assemblies is challenging since they can occur at a range of time scales and with a range of precisions, from synchronous spikes to co-variations in firing rate. A recently published cell assembly detection (CAD) algorithm (Russo and Durstewitz, 2017) addresses this ambiguity in time scale and precision; however, it is limited to spike trains with a relatively high number of total spikes, a condition which is frequently not met by the low temporal resolution data produced by calcium imaging. We first show how the CAD method can be modified to apply to sparse spike train data. This allows us to detect assemblies in calcium imaging data of neuronal activity in the CA1 region of the hippocampus, a brain region critical for encoding and generalizing contextual memories, during contextual fear conditioning training and tests. We found that assemblies in hippocampus play a role in encoding and retrieving contextual memories. In particular, there exists a group of assemblies whose exploratory activities predict the animal’s ability to distinguish different contexts. Moreover, the mechanisms for processing contextual information are different between two genetically distinct strains of mice that are included in the experiments.

Wilten Nicola

(University of Calgary, Canada)
"One-shot learning of spike-sequences in the hippocampus using theta-oscillations"
The hippocampus is capable of rapidly learning incoming information, even if that information is only observed once. Further, this information can be replayed in a compressed format during Sharp Wave Ripples (SPW-R). We leveraged state-of-the-art techniques in training recurrent spiking networks to demonstrate how primarily interneuron networks can: 1) generate internal theta sequences to bind externally elicited spikes in the presence of septal inhibition, 2) compress learned spike sequences in the form of a SPW-R when septal inhibition is removed, 3) generate and refine gamma-assemblies during SPW-R mediated compression, and 4) regulate the inter-ripple-interval timing between SPW-R’s in ripple clusters. From the fast time scale of neurons to the slow time scale of behaviors, interneuron networks and theta oscillations serve as the scaffolding for one-shot learningby replaying, refining, and regulating spike sequences.

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Virtual conference of the Society for Mathematical Biology, 2021.