Abstract
Neural systems display rich short-term dynamics at various levels, e.g., spike-frequency adaptation (SFA) at the single-neuron level, and short-term facilitation (STF) and depression (STD) at the synapse level. These dynamical features typically cover a broad range of time scales and exhibit large diversity in different brain regions. It remains unclear what is the computational benefit for the brain to have such variability in short-term dynamics. In this study, we propose that the brain can exploit such dynamical features to implement multiple seemingly contradictory computations in a single neural circuit. To demonstrate this idea, we use continuous attractor neural network (CANN) as a working model and include STF, SFA and STD with increasing time constants in its dynamics. Three computational tasks are considered, which are persistent activity, adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, and hence cannot be implemented by a single dynamical feature or any combination with similar time constants. However, with properly coordinated STF, SFA and STD, we show that the network is able to implement the three computational tasks concurrently. We hope this study will shed light on the understanding of how the brain orchestrates its rich dynamics at various levels to realize diverse cognitive functions.
Highlights
The brain performs computations by updating its internal states in response to external inputs
Based on continuous attractor neural network (CANN), we investigate how coordination of spike-frequency adaptation (SFA) in the neuronal dynamics and short-term plasticity (STP) in the synapse dynamics [which is further divided into short-term facilitation (STF) and short-term depression (STD)] enables a CANN to implement three different tasks, which are persistent activity, adaptation, and anticipative tracking
It can be checked that without these dynamical features, a CANN can hold a continuous family of Gaussian-shape stationary states in the absence of external drive (Iext = 0), when the global inhibition strength k is below a critical value kc ≡ ρ(J0fmin)2/(32π a2) (Fung et al, 2010)
Summary
The brain performs computations by updating its internal states in response to external inputs. Synapses, and circuits are the fundamental units for implementing brain functions. Neurons interact with each other to enhance or depress their responses. The topology of neuronal connection pattern shapes the overall population activity. The dynamics of individual neurons, the efficacy of synapses, and the network structure jointly determine the dynamical behavior of a neural system in response to external inputs which determine/restrict the computations a neural system can perform. Understanding the dynamical properties of neural systems and their roles in neural computations is at the core of using mathematical models to elucidate brain functions (Herz et al, 2006)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
