Sentence examples for in a recurrent network from inspiring English sources
Similarly, sequential activity could be generated in a recurrent network model with dominantly excitatory synaptic interactions between temporally co-active neurons and reciprocal inhibition on temporally distant neurons76.
Our model simulates the activity-dependent synthesis and diffusion of nitric oxide in a recurrent network model of integrate-and-fire neurons.
The first study to show that asynchronous irregular ongoing activity in a recurrent network facilitates propagation of both synchronous spiking and asynchronous firing rates in an embedded feedforward network.
Other work has emphasized the potential role of dynamic synapses in working memory processes, examining the effects of dynamic synaptic augmentation and rapid Hebbian plasticity in a recurrent network framework [25].
This highlights the fact that the effects of DA on intrinsic membrane excitability and synaptic connections between various cell-types in a recurrent network cannot be easily predicted from observations of each of these components in isolation.
In a recurrent network architecture [44], only the activity of V1 neurons with receptive fields corresponding to the retinal locations of the figure outlines would be influenced, and would thus diminish the ability to detect feedback effects on processing if one examined a given early visual area in its entirety.
To understand better the nature of the mixed signals in FEF, we trained a recurrent network model to integrate only one of two noisy input streams.
We constructed a recurrent network, in which 100 neurons were synaptically connected in an all-to-all fashion (Figure 12A).
Another approach consisted in precomputing the connectivity of a recurrent network according to the principles underlying the Hebbian rule [16].
In [8], we show that a recurrent network of biophysically detailed neurons with sigmoidal activation curves can be robustly tuned by dual homeostasis to act as an integrator.
Fig. 6 Firing rate variance ν vs. synaptic strength g in an excitatory recurrent network.
