ePoster
Share ePoster
Scan or copy the public World Wide URL.
CELL-TYPE SPECIFIC SENSING AND CONTROL OF FIRING RATE STATISTICS
University of Cambridge
Presenter and authors
Presenter
Andrea Ramirez Hincapie
University of Cambridge
Co-authors
Timothy O'Leary
Abstract
We demonstrate that mean Calcium concentration alone is sufficient to produce a simple readout of higher-order statistics of spiking activity. Using conductance based models under various input conditions, we show time-averaged intracellular Calcium concentration strongly correlates with firing rate mean and variance in individual neurons. Using these inferred relationships, we demonstrate a simple Calcium-based feedback loop that compensates for disturbances in the input statistics to jointly regulate firing rate mean and variance.
We show that cell-type specific mixtures of membrane ionic conductances lead to classes of homeostatic "personalities'' that respond distinctly to changes in input statistics. Neurons can tune intrinsic parameters such as maximal conductances to modulate the implicit relationship between rate statistics. Therefore, the pairs of correlates that determine feasible statistics and the homeostatic response of a cell are not fixed. Our results provide a parsimonious account of sensing and control mechanisms tied together by cellular identity.
References:| 1. Hengen, K. B., Lambo, M. E., Van Hooser, S. D., Katz, D. B., & Turrigiano, G. G. (2013). Firing Rate Homeostasis in Visual Cortex of Freely Behaving Rodents. Neuron, 80(2) | 5. Helmchen, F., Imoto, K., & Sakmann, B. (1996). Ca2+ buffering and action potential-evoked Ca2+ signaling in dendrites of pyramidal neurons. Biophysical Journal, 70(2), 1069–1081. |
| 2. Wen, W., & Turrigiano, G. G. (2024). Keeping Your Brain in Balance: Homeostatic Regulation of Network Function. Annual Review of Neuroscience, 47(1), 41–61 | 6. Stemmler, M., & Koch, C. (1999). How voltage-dependent conductances can adapt to maximize the information encoded by neuronal firing rate. Nature Neuroscience, 2(6), 521–527 |
| 3. Turrigiano, G. G. (2008). The Self-Tuning Neuron: Synaptic Scaling of Excitatory Synapses. Cell, 135(3), 422–435 | 7. Davis, G. W. (2006). HOMEOSTATIC CONTROL OF NEURAL ACTIVITY: From Phenomenology to Molecular Design. Annual Review of Neuroscience, 29(1), 307–323. |
| 4. Desai, N. S., Rutherford, L. C., & Turrigiano, G. G. (1999). Plasticity in the intrinsic excitability of cortical pyramidal neurons. Nature Neuroscience, 2(6), 515–520 | 8. Cannon, J., & Miller, P. (2017). Stable Control of Firing Rate Mean and Variance by Dual Homeostatic Mechanisms. Journal of Mathematical Neuroscience, 7, 1. |