Fundamental processes such as associative learning, rate calculation and decision making crucially rely on estimation and reproduction of time intervals. These processes are disrupted in Parkinson's disease, Huntington's disease, and Schizophrenia. This project will further our understanding of the disruptions of cognitive processes in these disorders by investigating neural activity in the neural substrates showed by imaging studies to be dysregulated in these disorders. The major objective of this proposal is to study the neural representation of temporal information in the seconds-to-minutes range using a multi-level approach involving electrophysiology, behavior, and computational modeling. The neural representation of temporal information will be investigated by multi-site, multi-electrode neural recordings; the behavioral representation of time will be examined in freely behaving rats performing interval timing tasks; the computational elements and operations required to perform these behavioral tasks will be investigated by computer modeling. First, at the electrophysiology level, the investigator will examine the neural representation of temporal information by multi-site, ensemble recordings in a network of substrates including striatum, frontal cortex, substantia nigra, and the hippocampus in freely behaving normal rats as well as in rats in which some of these substrates are temporarily inactivated. Notably, from the couple of electrophysiological studies currently addressing the substrates of interval timing, one was performed by our research group. Second, rats' behavior will be examined while performing two behavioral tasks (involving time estimation and reproduction) in which Parkinson, Huntington, and Schizophrenic patients are known to be impaired. Third, at the computational level the investigator will study a biologically plausible model of interval timing and will compare the predictions of the model with data collected in the behavioral studies. The project will help elucidate the behavioral and neural coding of time, and our understanding of the role of specific neural substrates in disorders like Parkinson, Huntington, and Schizophrenia. The results of the project can be used to devise behavioral measures for the early assessment of such disorders, and to understand the cognitive processes disrupted when these substrates are dysfunctional. This information is crucial to assessing the value/efficiency of potential treatment strategies in animal models of such disorders. ? ? ?
| Buhusi, Catalin V; Reyes, Marcelo B; Gathers, Cody-Aaron et al. (2018) Inactivation of the Medial-Prefrontal Cortex Impairs Interval Timing Precision, but Not Timing Accuracy or Scalar Timing in a Peak-Interval Procedure in Rats. Front Integr Neurosci 12:20 |
| Oprisan, Sorinel A; Buhusi, Mona; Buhusi, Catalin V (2018) A Population-Based Model of the Temporal Memory in the Hippocampus. Front Neurosci 12:521 |
| Buhusi, Catalin V; Oprisan, Sorinel A; Buhusi, Mona (2018) Biological and Cognitive Frameworks for a Mental Timeline. Front Neurosci 12:377 |
| Buhusi, Catalin V; Oprisan, Sorinel A; Buhusi, Mona (2016) Clocks within Clocks: Timing by Coincidence Detection. Curr Opin Behav Sci 8:207-213 |
| Oprisan, Sorinel A; Dix, Steven; Buhusi, Catalin V (2014) Phase resetting and its implications for interval timing with intruders. Behav Processes 101:146-53 |
| Oprisan, Sorinel A; Buhusi, Catalin V (2014) What is all the noise about in interval timing? Philos Trans R Soc Lond B Biol Sci 369:20120459 |
| Reyes, Marcelo Bussotti; Buhusi, Catalin V (2014) What is learned during simultaneous temporal acquisition? An individual-trials analysis. Behav Processes 101:32-7 |
| Buhusi, Catalin V; Matthews, Alexander R (2014) Effect of distracter preexposure on the reset of an internal clock. Behav Processes 101:72-80 |
| Oprisan, Sorinel A; Buhusi, Catalin V (2013) How noise contributes to time-scale invariance of interval timing. Phys Rev E Stat Nonlin Soft Matter Phys 87:052717 |
| Buhusi, Catalin V; Oprisan, Sorinel A (2013) Time-scale invariance as an emergent property in a perceptron with realistic, noisy neurons. Behav Processes 95:60-70 |
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