RINZEL, J. | Search bioRxiv

bioRxiv2026

A disinhibition-based model of CA1 place field sequences

Because of the paucity of pattern-generating recurrent excitation in the CA1 hippocampal region, place field sequences are assumed to be inherited from upstream regions. Alternatively, disinhibition may play a role in shaping pyramidal cell activity. We propose a model of CA1 activity using one layer of excitatory cells and one layer of inhibitory cells, with local excitatory-to-inhibitory connectivity, broad inhibitory-to-excitatory connectivity without recurrent excitatory connections, and broad inhibitory-inhibitory connections. The layers need not follow a topographic organization; instead they can represent sequences of place cells structured by local inhibition. Our mathematical model of CA1 neurons with data-inspired connectivity demonstrates place fields sharpened by disinhibition, mimicking a recently discovered decrease in inhibitory input within place fields and matching in vivo patterns of disinhibition. Mathematical analysis of our model suggests the importance of inhibitory-to-excitatory connections in control of place cell firing rate and sparsity. We also demonstrate that a disinhibition-based mechanism of pattern generation is more flexible without recurrent excitation than the recurrent excitation-rich CA3-like model. Finally, we demonstrate disinhibition as an efficient mechanism for internally-generated sequential activity, where the speed of sequential change depends on both input strength and inhibitory-to-excitatory connection strength. Overall, our model suggests that theta time-scale disinhibition is a foundational mechanism of self-organized sequential activity in the CA1 region, and, possibly, in other networks.

neuroscience↗

bioRxiv2021

A Biophysical Counting Mechanism for Keeping Time

The ability to estimate and produce appropriately timed responses is central to many behaviors including speaking, dancing, and playing a musical instrument. A classical framework for estimating or producing a time interval is the pacemaker-accumulator model in which pulses of a pacemaker are counted and compared to a stored representation. However, the neural mechanisms for how these pulses are counted remains an open question. The presence of noise and stochasticity further complicate the picture. We present a biophysical model of how to keep count of a pacemaker in the presence of various forms of stochasticity using a system of bistable Wilson-Cowan units asymmetrically connected in a one-dimensional array; all units receive the same input pulses from a central clock but only one unit is active at any point in time. With each pulse from the clock, the position of the activated unit changes thereby encoding the total number of pulses emitted by the clock. This neural architecture maps the counting problem into the spatial domain, which in turn translates count to a time estimate. We further extend the model to a hierarchical structure to be able to robustly achieve higher counts.

neuroscience↗