The successful applicant will work on a multidisciplinary collaborative project aiming to determine the importance of cortical engram cells in memory formation and storage and probe the role of cortical memory engrams in the generation and retrieval of a sensory-based memory. The project as a whole combines computational modeling, electrophysiology, calcium imaging techniques, and molecular and behavioral experiments. First, the biophysical properties of engrams will be identified in a cortical area of interest, and their functional role will be unraveled in vivo. Then, computational modeling will be used to determine the role of engram cells during memory recall. This project is a collaboration between the Florey Institute of Neuroscience and Mental Health in Melbourne, Australia (Prof. L. Palmer), and the University of Dublin, Ireland (Prof. T. Ryan).

Neural circuits exhibit complex activity patterns, both spontaneously and in response to external stimuli. Information encoding and learning in neural circuits depend on the ability of time-varying stimuli to control spontaneous network activity. In particular, variability arising from the sensitivity to initial conditions of recurrent cortical circuits can limit the information conveyed about the sensory input. Spiking and firing rate network models can exhibit such sensitivity to initial conditions that are reflected in their dynamic entropy rate and attractor dimensionality computed from their full Lyapunov spectrum. I will show how chaos in both spiking and rate networks depends on biophysical properties of neurons and the statistics of time-varying stimuli. In spiking networks, increasing the input rate or coupling strength aids in controlling the driven target circuit, which is reflected in both a reduced trial-to-trial variability and a decreased dynamic entropy rate. With sufficiently strong input, a transition towards complete network state control occurs. Surprisingly, this transition does not coincide with the transition from chaos to stability but occurs at even larger values of external input strength. Controllability of spiking activity is facilitated when neurons in the target circuit have a sharp spike onset, thus a high speed by which neurons launch into the action potential. I will also discuss chaos and controllability in firing-rate networks in the balanced state. For these, external control of recurrent dynamics strongly depends on correlations in the input. This phenomenon was studied with a non-stationary dynamic mean-field theory that determines how the activity statistics and the largest Lyapunov exponent depend on frequency and amplitude of the input, recurrent coupling strength, and network size. This shows that uncorrelated inputs facilitate learning in balanced networks. The results highlight the potential of Lyapunov spectrum analysis as a diagnostic for machine learning applications of recurrent networks. They are also relevant in light of recent advances in optogenetics that allow for time-dependent stimulation of a select population of neurons.

Modular structures in myriad forms — genetic, structural, functional — are ubiquitous in the brain. While modularization may be shaped by genetic instruction or extensive learning, the mechanisms of module emergence are poorly understood. Here, we explore complementary mechanisms in the form of bottom-up dynamics that push systems spontaneously toward modularization. As a paradigmatic example of modularity in the brain, we focus on the grid cell system. Grid cells of the mammalian medial entorhinal cortex (mEC) exhibit periodic lattice-like tuning curves in their encoding of space as animals navigate the world. Nearby grid cells have identical lattice periods, but at larger separations along the long axis of mEC the period jumps in discrete steps so that the full set of periods cluster into 5-7 discrete modules. These modules endow the grid code with many striking properties such as an exponential capacity to represent space and unprecedented robustness to noise. However, the formation of discrete modules is puzzling given that biophysical properties of mEC stellate cells (including inhibitory inputs from PV interneurons, time constants of EPSPs, intrinsic resonance frequency and differences in gene expression) vary smoothly in continuous topographic gradients along the mEC. How does discreteness in grid modules arise from continuous gradients? We propose a novel mechanism involving two simple types of lateral interaction that leads a continuous network to robustly decompose into discrete functional modules. We show analytically that this mechanism is a generic multi-scale linear instability that converts smooth gradients into discrete modules via a topological “peak selection” process. Further, this model generates detailed predictions about the sequence of adjacent period ratios, and explains existing grid cell data better than existing models. Thus, we contribute a robust new principle for bottom-up module formation in biology, and show that it might be leveraged by grid cells in the brain.

Pathogenic variants in HCN1 are associated with severe developmental and epileptic encephalopathies (DEE). We have engineered the Hcn1 M294L heterozygous knock-in (Hcn1M294L) mouse which is a homolog of the de novo HCN1 M305L recurrent pathogenic variant. The mouse recapitulates the phenotypic features of patients including having spontaneous seizures and a learning deficit. In this talk I will present experimental work that probes the molecular and cellular mechanisms underlying hyper-excitability in the mouse model. This will include testing the efficacy of currently available antiepileptic drugs and a novel precision medicine approach. I will also briefly touch on how disease biology can give insights into the biophysical properties of HCN channels.

Recent human gene discovery efforts show that gain-of-function (GOF) variants in the KCNT1gene, which encodes a Na+-activated K+ channel subunit, cause severe epilepsies and other neurodevelopmental disorders. Although the impact of these variants on the biophysical properties of the channels is well characterized, the mechanisms that link channel dysfunction to cellular and network hyperexcitability and human disease are unknown. Furthermore, precision therapies that correct channel biophysics in non-neuronal cells have had limited success in treating human disease, highlighting the need for a deeper understanding of how these variants affect neurons and networks. To address this gap, we developed a new mouse model with a pathogenic human variant knocked into the mouse Kcnt1gene. I will discuss our findings on the in vivo phenotypes of this mouse, focusing on our characterization of epileptiform neural activity using electrophysiology and widefield Ca++imaging. I will also talk about our investigations at the synaptic, cellular, and circuit levels, including the main finding that cortical inhibitory neurons in this model show a reduction in intrinsic excitability and action potential generation. Finally, I will discuss future directions to better understand the mechanisms underlying the cell-type specific effects, as well as the link between the cellular and network level effects of KCNT1 GOF.

Organisms sense light for purposes that range from recognizing objects to synchronizing activity with environmental cycles. What mechanisms serve these diverse tasks? This seminar will examine the specializations of two cell types. First are the foveal cone photoreceptors. These neurons are used by primates to see far greater detail than other mammals, which lack them. How do the biophysical properties of foveal cones support high-acuity vision? Second are the melanopsin retinal ganglion cells, which are conserved among mammals and essential for processes that include regulation of the circadian clock, sleep, and hormone levels. How do these neurons encode light, and is encoding customized for animals of different niches? In pursuing these questions, a broad goal is to learn how various levels of biological organization are shaped to behavioural needs.