cell assembly

Precise spatio-temporal spike patterns in cortex and model

The cell assembly hypothesis postulates that groups of coordinated neurons form the basis of information processing. Here, we test this hypothesis by analyzing massively parallel spiking activity recorded in monkey motor cortex during a reach-to-grasp experiment for the presence of significant ms-precise spatio-temporal spike patterns (STPs). For this purpose, the parallel spike trains were analyzed for STPs by the SPADE method (Stella et al, 2019, Biosystems), which detects, counts and evaluates spike patterns for their significance by the use of surrogates (Stella et al, 2022 eNeuro). As a result we find STPs in 19/20 data sets (each of 15min) from two monkeys, but only a small fraction of the recorded neurons are involved in STPs. To consider the different behavioral states during the task, we analyzed the data in a quasi time-resolved analysis by dividing the data into behaviorally relevant time epochs. The STPs that occur in the various epochs are specific to behavioral context - in terms of neurons involved and temporal lags between the spikes of the STP. Furthermore we find, that the STPs often share individual neurons across epochs. Since we interprete the occurrence of a particular STP as the signature of a particular active cell assembly, our interpretation is that the neurons multiplex their cell assembly membership. In a related study, we model these findings by networks with embedded synfire chains (Kleinjohann et al, 2022, bioRxiv 2022.08.02.502431).

Cell assembly activation coordinated by rhythmic oscillation in the prefrontal-ventral striatum-hippocampal network

Network mechanisms underlying representational drift in area CA1 of hippocampus

Recent chronic imaging experiments in mice have revealed that the hippocampal code exhibits non-trivial turnover dynamics over long time scales. Specifically, the subset of cells which are active on any given session in a familiar environment changes over the course of days and weeks. While some cells transition into or out of the code after a few sessions, others are stable over the entire experiment. The mechanisms underlying this turnover are unknown. Here we show that the statistics of turnover are consistent with a model in which non-spatial inputs to CA1 pyramidal cells readily undergo plasticity, while spatially tuned inputs are largely stable over time. The heterogeneity in stability across the cell assembly, as well as the decrease in correlation of the population vector of activity over time, are both quantitatively fit by a simple model with Gaussian input statistics. In fact, such input statistics emerge naturally in a network of spiking neurons operating in the fluctuation-driven regime. This correspondence allows one to map the parameters of a large-scale spiking network model of CA1 onto the simple statistical model, and thereby fit the experimental data quantitatively. Importantly, we show that the observed drift is entirely consistent with random, ongoing synaptic turnover. This synaptic turnover is, in turn, consistent with Hebbian plasticity related to continuous learning in a fast memory system.

Making memories in mice

Understanding how the brain uses information is a fundamental goal of neuroscience. Several human disorders (ranging from autism spectrum disorder to PTSD to Alzheimer’s disease) may stem from disrupted information processing. Therefore, this basic knowledge is not only critical for understanding normal brain function, but also vital for the development of new treatment strategies for these disorders. Memory may be defined as the retention over time of internal representations gained through experience, and the capacity to reconstruct these representations at later times. Long-lasting physical brain changes (‘engrams’) are thought to encode these internal representations. The concept of a physical memory trace likely originated in ancient Greece, although it wasn’t until 1904 that Richard Semon first coined the term ‘engram’. Despite its long history, finding a specific engram has been challenging, likely because an engram is encoded at multiple levels (epigenetic, synaptic, cell assembly). My lab is interested in understanding how specific neurons are recruited or allocated to an engram, and how neuronal membership in an engram may change over time or with new experience. Here I will describe both older and new unpublished data in our efforts to understand memories in mice.

Dynamic organization of global cell assembly for working memory

COSYNE 2022

Cell assembly detection through the iterative search for recurrent synchrony