This repo contains the 2 models for the SSoC project. The first one is a Leaky Intgerate and Fire (LIF) model, which is modeled using numpy and matplotlib in Python3. The second one is a simple 2-layer shallow neural net, it is made to model the given input table for XOR/XNOR output of 2 binary bits using a third binary input bit.
It is a basic Spiking Neuron model based on the following equations :
The observed results are plotted and saved as images in the Leaky Integrate and Fire Model folder. The current used for each testcase is mentioned below.
Input Values :
I. I = 2uA for 10ms <= t <= 75ms; I = 0Amp otherwise
II. I = 1.5uA for 0ms <= t <= 100ms
III. I = 5sin(ωt)uA where ω = 45deg/msec
IV. I = -2sin(ω1t) + 3sin(ω2t) + cos(ω3t)uA where ω1 = 45deg/msec, ω2 = 60 deg/msec, ω3 = 30 deg/msec
This is a basic model to predict the XOR/XNOR values of 2 binary single bit inputs based on a third bit (switch). It is a shallow network as the data is ordered and easy to classify.
| Bit 1 | Bit 2 | XNOR(0)/XOR(1) | Output |
|---|---|---|---|
| 1 | 1 | 0 | 1 |
| 1 | 0 | 0 | 0 |
| 0 | 1 | 0 | 0 |
| 0 | 0 | 0 | 1 |
| 1 | 1 | 1 | 0 |
| 1 | 0 | 1 | 1 |
| 0 | 1 | 1 | 1 |
| 0 | 0 | 1 | 0 |
Dev Churiwala