P022 Pattern mismatch detection by transient EI imbalance on single neurons: Experiments and multiscale models.
Authors:Aditya Asopa1andUpinder S. Bhalla1*
1NCBS-TIFR, Bangalore, India
*Email: bhalla@ncbs.res.in Introduction
Changes in repetitive stimuli typically signal important sensory events, and many organisms exhibit mismatch detection through behavioral and physiological responses.Mismatch detection is a fundamental sensory and computational function, bringing attention and neural resources to bear on novel inputs. Previous work[1,2] suggests that sensory adaptation mediated by short-term plasticity (STP) may be a mechanism for mismatch detection, however this does not factor in details of excitatory-inhibitory (EI) balance, network connectivity, and time-courses of E and I inputs.
Methods We performed optogenetic stimulation of CA3 pyramidal neurons in acute mouse hippocampal brain slice to provide precise spatial and temporal patterns of activity as proxies for input ensembles. We monitored E and I synaptic input in postsynaptic CA1 pyramidal neurons using voltage clamp at I and E reversal potentials to separate the respective contributions. We used time and space patterns to parameterize a multiscale model of CA3 neurons, interneurons, and hundreds of synaptic boutons with independent stochastic chemical signaling controlling synaptic release onto a postsynaptic CA1 neuron. Simulations were performed using the MOOSE simulator[3]. Results We parameterized the model in three stages. First, we built a Ca2+-triggered 4-step presynaptic release model which (with different parameters) could be applied both to E and I synapses using voltage-clamp recordings over a burst. Second, we fit CA1 neuronal and synaptic properties to burst synaptic input at different frequencies. Third, we fit CA1 readouts of Poisson trains of optical patterned input at CA3, to constrain network parameters. This model predicted that transitions in spatially patterned input sequences, such as AAAABBBBCCCC, could be detected by the network. We confirmed this experimentally. Finally, we showed that spiking CA1 neurons had even sharper mismatch tuning and could detect pattern transitions between theta bursts. Discussion EI balance controls neuronal excitability both across time-scales, and across strength and patterns of input[4]. To this we add the dimension of plasticity at short-time-scales (~100 ms) relevant for mismatch detection[1] and sensory sampling coupled to the theta rhythm. We provide an experimentally tuned open-sourced resource of a CA3-CA1 model of input-output relationships down to the molecular level, which is lightweight enough to run on a laptop at only ~20x real time. We propose that a transient tilt in EI balance is a more nuanced, biochemically and biophysically based mechanism for mismatch detection, and accounts for numerous observations of timing, intensity, and circuit configurations.
Acknowledgements AA and USB are at NCBS-TIFR which receives the support of the Department of Atomic Energy, Government of India, under Project Identification No. RTI 4006. The study received funding from SERB Grant CRG/2022/003135-G. References 1: https://doi.org/10.1016/j.clinph.2008.11.029 2: https://doi.org/10.1111/j.1469-8986.2005.00256.x 3: https://doi.org/10.3389/neuro.11.006.2008 4: https://doi.org/10.7554/eLife.43415
