Download Activity of Spiking Neurons Stimulated by External Signals of

Activity of Spiking Neurons Stimulated by External
Signals of Different Wave
Natalie Sanghvi, Natacha Gueorguieva
[email protected]
Computer Science Department
Spiking neuron systems gained increasing interest in recent years because they
represent spatio-temporal relations within simulated systems, unlike the spatial simple
neuron models found in artificial neural systems. They are also closer to biophysical
models of neurons, synapses, and related elements and their synchronized firing of
neuronal assemblies could serve the brain as a code for feature binding and pattern
segmentation.
The human brain consists of a large number of neurons that are interconnected
with each other. On average, each neuron is connected to other neurons through about 10
000 synapses. The brain network of neurons forms a massively parallel information
processing system. This contrasts with conventional computers, in which a single
processor executes a sequential series of instructions.
A typical neuron consists of dendrites, soma and axon. Dendrites receive and
deliver signals and act like an “input device”. Soma is the “central processing unit” that
generates a signal if the total input exceeds a certain threshold (about -30 mV) and the
axon transmits the signals to other neurons. Synapses are the contact points for
transferring information between neurons and facilitate the connection between axons
and dendrites. The pulses or spikes (also called action potentials) last about 1-2 ms in
amplitude of 100 mV.
The neuron sends out spikes of electrical activity through the axon (the output and
conducting structure), which can split into thousands of branches. At the end of each
branch, a synapse converts the activity from the axon into electrical effects that inhibit or
excite activity on the contacted (target) neuron. When a neuron receives excitatory input
that is sufficiently large compared with its inhibitory input, it sends a spike of electrical
activity (an action potential) down its axon. This spiking event is also called
depolarization, and it is followed by a refractory period, during which the neuron is
unable to fire.
In this research we investigate the integrate-and-fire model (I&F) which is based
on the idea that the neuron adds and subtracts excitatory and inhibitory inputs until it
reaches a threshold, at which point it fires a single impulse or action potential. The goal is
to perform spiking-neuron simulations with external input signals of different wave:
sinusoidal, two combined absolute sinusoidal signals, square and step wave signals and to
analyze the timing and number of spikes of membrane potential.