Neural network dynamics of temporal processing
- Author(s): Hardy, Nicholas
- Advisor(s): Buonomano, Dean V
- et al.
Time is centrally involved in most tasks the brain performs. However, the neurobiological mechanisms of timing remain a mystery. Signatures of temporal processing related to sensory and motor behavior have been observed in several brain regions and behavioral contexts. This activity is often complex, representing time in the activity of large populations of neurons. A major question is whether this observed activity is generated by a specialized clock in the brain or whether it is arises locally via the emergent dynamics of neural networks. State dependent theories of timing argue for the latter: neural activity evolving over time produces a trajectory of network states that can encode temporal information.
In this work, I examine the role of network dynamics in encoding temporal information. Combining mathematical models, in vitro neural recordings, and human psychophysics, the studies presented here describe potential network level mechanisms for timing in the brain. Chapter 2 presents research examining sequential activity observed in brain regions characterized by recurrent connectivity. This study describes a theoretical mechanism that recurrent neural networks may use to autonomously produce sequential activity and encode temporal information. Next, Chapter 3 examines the mechanisms of producing the same complex movement at a variety of speeds, a fundamental feature of motor timing. This study combines theoretical and psychophysical studies to predict and test a novel feature of motor timing: temporal accuracy improves with speed, termed the Weber-speed effect. Finally, Chapter 4 examines how cortical neural networks encode temporal information. Using organotypic slice cultures, this study demonstrates that the cortex processes temporal input patterns in a state dependent manner, supporting theoretical predictions. Taken together, the results of this work strongly support state dependent theories of timing, providing insight into the neural basis temporal processing.