Timing as an intrinsic property of neural networks: evidence from in vivo and in vitro experiments.
The discrimination and production of temporal patterns on the scale of hundreds of milliseconds are critical to sensory and motor processing. Indeed, most complex behaviours, such as speech comprehension and production, would be impossible in the absence of sophisticated timing mechanisms. Despite the importance of timing to human learning and cognition, little is known about the underlying mechanisms, in particular whether timing relies on specialized dedicated circuits and mechanisms or on general and intrinsic properties of neurons and neural circuits. Here, we review experimental data describing timing and interval-selective neurons in vivo and in vitro. We also review theoretical models of timing, focusing primarily on the state-dependent network model, which proposes that timing in the subsecond range relies on the inherent time-dependent properties of neurons and the active neural dynamics within recurrent circuits. Within this framework, time is naturally encoded in populations of neurons whose pattern of activity is dynamically changing in time. Together, we argue that current experimental and theoretical studies provide sufficient evidence to conclude that at least some forms of temporal processing reflect intrinsic computations based on local neural network dynamics.
Departments of Neurobiology and Psychology, and Integrative Center for Learning and Memory, University of California, , 695 Young Drive, Gonda, Los Angeles, CA 90095, USA.
Anubhuti
A
Goel
Dean V
DV
Buonomano
D001683:Biological Clocks / Q000502:physiology*; D003071:Cognition / Q000502:physiology*; D006801:Humans; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009473:Neuronal Plasticity / Q000502:physiology; D009474:Neurons / Q000378:metabolism*; D013997:Time Factors
neural dynamics; short-term plasticity; state-dependent network model; temporally selective neurons