The results of this simulation are illustrated in w2_ous.gif. Equation 2 in the *.ws file is referred as ‘associative’ because the voltages of both the pre- and postsynaptic cells are considered in calculating dW/dt (i.e., the change in synaptic weight). As the integrate-and-fire features of SNNAP are refined, this requirement may be relaxed.Įquation 2, in *.ws. The present version of SNNAP must ‘see’ at least one HH-type cell. SNNAP was originally designed to simulation HH-type neurons and synaptic connections. The goals of the present simulation are to illustrate two aspects of neural networks with integrate-and-fire cells and weighted synapses:Īny network must contain at least one HH-type neuron and this neuron must contain at least voltage-dependent conductance with activation and inactivation functions. The results of this simulation are illustrated in ws1_ous.gif WS2 The HH-type cell makes a synaptic connection with gi_6 and a treatment that elicits activity in the HH cell ultimately leads to some activity in gi_6 and gi_7. This equation has no plasticity and the synaptic weight is a constant.
The gi_6 to gi_7 weighted synapse uses equation 1 of the *.ws file. gi_6, in turn, makes a synaptic connection with gi_7.cell. The rate of firing is controlled by several factors including the “membrane capacitance” of the gi- type cell, the noise level (see \examples\integrate_fire\ws7), the threshold.
Initially, a stimulus is applied to the gi-type cell ‘gi_6’, elicits firing. (Note, modulatory synapses are depicted filled boxes and chemical synapses as open triangles.) In addition, weighted synapses (ws) are depicted as open boxes. Note that HH and gi types of cells are depicted using different symbols. The structure of the network is illustrated in ws1_ntw.jpg. In addition, this simulation illustrates the function of *.ws equation 1. The goal of the present simulation is to illustrate how to implement a hybrid neural network that contains both Hodgkin-Huxley (HH) type neurons and Integrate-and-Fire (gi) cells. In addition, the simulations illustrate the different ‘learning rules’ that govern the weights of ‘synapses’ among integrate-and-fire cells. This subdirectory contains several simulations that illustrate the capacity of SNNAP to simulate networks containing both Hodgkin-Huxley-type neurons and integrate-and-fire type cells.
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