Circuits constructed from identified Aplysia neurons exhibit multiple patterns of persistent activity
1Solid State and Quantum Physics Research Department, AT&T Bell Laboratories, Murray Hill, New Jersey 07974.
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Summary
Two-neuron circuits in Aplysia demonstrate bistable activity, switching between states with external input. This research explores neuronal network models and control mechanisms in neural circuits.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Cellular Neuroscience
Background:
- Neuronal circuits exhibit persistent activity crucial for memory and decision-making.
- Understanding the fundamental properties of bistable neuronal networks is essential for deciphering complex neural computations.
Purpose of the Study:
- To investigate the conditions under which simple neuronal circuits can generate bistable activity.
- To analyze how feedback connections and individual neuron properties contribute to network stability.
- To explore the potential for external inputs to control these bistable states.
Main Methods:
- Construction and in vitro analysis of two-neuron circuits using identified neurons from Aplysia abdominal ganglion.
- Coculturing specific neuron pairs (L10-LUQ with inhibitory connections, L7-L12 with excitatory connections).
- Recording and analysis of neuronal firing patterns and network output states under varying injected currents.
Main Results:
- Both circuits exhibited two stable patterns of persistent activity (bistability), switchable by brief external input.
- Inhibitory L10-LUQ circuit showed alternating activity states, while excitatory L7-L12 circuit showed quiescent and continuously firing states.
- Bistability depended on neuronal nonlinearities and feedback, with a defined range of background currents for stability.
Conclusions:
- Simple two-neuron circuits can replicate bistable output states hypothesized for larger neuronal populations.
- The findings provide insights into how in vivo circuits generate stable activity patterns and how higher centers may exert control.
- This work validates theoretical models of neuronal network dynamics using experimental preparations.