Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Fung, C. C. A."'
Publikováno v:
Phys. Rev. E 92, 032908 (2015)
In continuous attractor neural networks (CANNs), spatially continuous information such as orientation, head direction, and spatial location is represented by Gaussian-like tuning curves that can be displaced continuously in the space of the preferred
Externí odkaz:
http://arxiv.org/abs/1502.03662
Autor:
Fung, C. C. Alan, Amari, S. -I.
Publikováno v:
Neural Computation 27 (3) 2015
Attractor models are simplified models used to describe the dynamics of firing rate profiles of a pool of neurons. The firing rate profile, or the neuronal activity, is thought to carry information. Continuous attractor neural networks (CANNs) descri
Externí odkaz:
http://arxiv.org/abs/1502.00127
Publikováno v:
Phys. Rev. E 92, 022801 (2015)
Anticipation is a strategy used by neural fields to compensate for transmission and processing delays during the tracking of dynamical information, and can be achieved by slow, localized, inhibitory feedback mechanisms such as short-term synaptic dep
Externí odkaz:
http://arxiv.org/abs/1409.2114
Publikováno v:
Front. Comput. Neurosci. 7:73. (2013)
Conventionally, information is represented by spike rates in the neural system. Here, we consider the ability of temporally modulated activities in neuronal networks to carry information extra to spike rates. These temporal modulations, commonly know
Externí odkaz:
http://arxiv.org/abs/1305.4125
Publikováno v:
Neural Comput. 24 (2012) 1147-1185
Experimental data have revealed that neuronal connection efficacy exhibits two forms of short-term plasticity, namely, short-term depression (STD) and short-term facilitation (STF). They have time constants residing between fast neural signaling and
Externí odkaz:
http://arxiv.org/abs/1104.0305
Publikováno v:
Advances in NIPS vol. 23 (2010)
Neuronal connection weights exhibit short-term depression (STD). The present study investigates the impact of STD on the dynamics of a continuous attractor neural network (CANN) and its potential roles in neural information processing. We find that t
Externí odkaz:
http://arxiv.org/abs/1009.2290
Publikováno v:
J. Phys.: Conf. Ser. (2009) 197: 012017
We introduce an analytically solvable model of two-dimensional continuous attractor neural networks (CANNs). The synaptic input and the neuronal response form Gaussian bumps in the absence of external stimuli, and enable the network to track external
Externí odkaz:
http://arxiv.org/abs/0910.1520
Publikováno v:
Neural Comput. 2010 22(3): 752-92
Understanding how the dynamics of a neural network is shaped by the network structure, and consequently how the network structure facilitates the functions implemented by the neural system, is at the core of using mathematical models to elucidate bra
Externí odkaz:
http://arxiv.org/abs/0808.2341
Publikováno v:
EPL (2008) 84: 18002
We investigate the dynamics of continuous attractor neural networks (CANNs). Due to the translational invariance of their neuronal interactions, CANNs can hold a continuous family of stationary states. We systematically explore how their neutral stab
Externí odkaz:
http://arxiv.org/abs/0801.4461
Autor:
Miyamoto, D., Hirai, D., Fung, C. C. A., Inutsuka, A., Odagawa, M., Suzuki, T., Boehringer, R., Adaikkan, C., Matsubara, C., Matsuki, N., Fukai, T., McHugh, T. J., Yamanaka, A., Murayama, M.
Publikováno v:
Science, 2016 Jun 01. 352(6291), 1315-1318.
Externí odkaz:
http://www.jstor.org/stable/24748097
