Robustness of Neural Activity Dynamics in the Medial Entorhinal Cortex

Abstract

Biological systems exhibit considerable heterogeneity in their constitutive components and encounter stochasticity across all scales of analysis. Therefore, central questions that span all biological systems are: (a) How does the system manifest robustness in the face of parametric variability and stochasticity? (b) What are the mechanisms used by disparate biological systems to maintain the robustness of physiological outcomes? In this thesis, we chose the mammalian medial entorhinal cortex (MEC) as the model system to systematically study the principles that govern functional robustness across different scales of analysis. At the neuronal level, we assessed the impact of heterogeneities in channel properties on the robustness of cellular-scale physiology of MEC stellate cells and cortical interneurons. We demonstrated that the expression of cellular-scale degeneracy, wherein disparate combinations of molecular scale parameters (e.g., ion channels) yielded similar characteristic physiological properties (e.g., firing rate). These analyses and observations underscored the role of degeneracy as a mechanism to achieve functional robustness in cellular-scale activity despite widespread heterogeneities in the underlying molecular scale properties. An important cellular scale signature of MEC stellate cells is their ability to manifest peri-threshold intrinsic oscillations. Although different theoretical frameworks have been proposed to explain these oscillatory patterns, these frameworks do not jointly account for heterogeneities in intrinsic properties of stellate cells and stochasticity in ion-channel and synaptic physiology. In this thesis, using a combination of theoretical, computational, and electrophysiological methods, we argue for heterogeneous stochastic bifurcations as a unifying framework that fully explains peri-threshold activity patterns in MEC stellate cells. We also provide quantitative evidence for stochastic resonance, involving an optimal noise that improves system performance, as a mechanism to enhance robustness of intrinsic peri-threshold oscillations. At the network level, we chose a well-characterized function of the MEC involving grid-patterned activity generation in a 2D continuous attractor network (CAN) model of the MEC. We quantitatively addressed questions on the impact of distinct forms of biological heterogeneities on the functional stability of grid-patterned activity generation in these models. We showed that increasing degrees of biological heterogeneities progressively disrupted the emergence of grid-patterned activity and resulted in progressively large perturbations in low-frequency neural activity. We postulated that suppressing low-frequency perturbations could ameliorate the disruptive impact of biological heterogeneities on grid-patterned activity. As a physiologically relevant means to suppress low-frequency activity, we introduced intrinsic neuronal resonance either by adding an additional high-pass filter (phenomenological) or by incorporating a slow negative feedback loop (mechanistic) into our model neurons. Strikingly, 2D CAN models with resonating neurons were resilient to the incorporation of heterogeneities and exhibited stable grid-patterned firing. We extended these findings to one-dimensional CAN models built of heterogeneous conductance-based excitatory and inhibitory neuronal models. We found that slow negative feedback loops, introduced by HCN channels that are naturally endowed with slow restorative properties, stabilized activity propagation in heterogeneous 1D CAN models. Together, these findings established slow negative feedback loops as a mechanism to enhance functional robustness in heterogeneous neural networks. Together, the analyses presented in different parts of this thesis emphasize the need to account for all forms of neural-circuit heterogeneities and stochasticity in assessing robustness of biological function across scales. The findings presented here highlight degeneracy, stochastic resonance, and negative feedback loops as powerful generalized principles and mechanisms that could drive robustness in biological systems across different scales.