BIOLOGICAL NEURONS
Dr. Zaheeruddin
Professor
Department of Electrical Engineering
Faculty of Engineering and Technology
Jamia Millia Islamia (A Central University)
New Delhi-110025
Email: Zaheeruddin@jmi.ac.in
Mobile: 9910170032
Fig. 1. A simplified view of a biological neuron
The Human Brain
Part of the central nervous system
Contains of the order of 1010 neurons
Each can activate in approximately 5ms
Each connects to the order of 104 other
neurons
Giving 1014 connections
Electro-Chemical Actions of Neuron
– A synapse is a biochemical device which converts a pre-synaptic
electrical signal into a chemical signal and then back into a post-
synaptic electrical signal.
– The input pulse train has its amplitude modified by parameters stored
in the synapse. The nature of this modification depends on the type
of the synapse, which can be either inhibitory or excitatory.
• The postsynaptic signals are aggregated and transferred along the
dendrites to the nerve cell body.
• The cell body generates the output neuronal signal, a spike, which is
transferred along the axon to the synaptic terminals of other neurons.
The frequency of firing of a neuron is proportional to the total synaptic
activities and is controlled by the synaptic parameters (weights).
• The pyramidal cell can receive 104 synaptic inputs and it can fan-out
the output signal to thousands of target cells — the connectivity
difficult to achieve in the artificial neural networks.
Electro-Chemical Actions of Neuron
Signals move from neuron to neuron via electrochemical reactions.
The synapses release a chemical transmitter which enters the
dendrite. This raises or lowers the electrical potential of the cell body.
The soma sums the inputs it receives and once a threshold level is
reached an electrical impulse is sent down the axon (often known as
firing). These impulses eventually reach synapses and the cycle
continues.
Synapses which raise the potential within a cell body are called
excitatory.
Synapses which lower the potential are called inhibitory.
It has been found that synapses exhibit plasticity.
This means that long-term changes in the strengths of the
connections can be formed depending on the firing patterns of other
neurons.
This is thought to be the basis for learning in our brains.
Brain plasticity
At the early stage of the human brain development (the
first two years from birth) about 1 million synapses (hard-
wired connections) are formed per second.
Synapses are then modified through the learning process
(plasticity of a neuron).
In an adult brain plasticity may be accounted for by the
above two mechanisms: creation of new synaptic
connections between neurons, and modification of
existing synapses.
Neuron as Information Processing System
Synapses
Gap between adjacent neurons across which
chemical signals are transmitted: input
Dendrites
Receive synaptic contacts from other
neurons: connection
Cell body / soma
Metabolic centre of the neuron: processing
Axon
Long narrow process that extends from body:
output
Neural Processing
Input (Synapse)
Via the synapse
Converts a presynaptic electrical signal (from
an axon) into a postsynaptic chemical signal
Communication channel between neurons
Received by dendrite
Plasticity in the brain results from changes to
synapses: connection strength
Neural Processing
Processing (Soma)
Integration of postsynaptic signals
May be excitatory or inhibitory
Firing of neuron is determined by combination
of all postsynaptic signals
If the sum of the excitatory and inhibitory
signals is greater than the threshold then an
action potential is generated
Neural Processing
Output (Axon)
Action potential is an electrical signal that is
conducted down the axon
Short term electrical spike
From Human Neurons to Artificial Neurons
The neuron model
Model of Neural Unit
x0
x1 w1 w0
x2 w2
s
. .
+ g(s) y
. .
xn wn
Inputs Weights Summation Activation Output
Components of Neural Unit
The general artificial neuron model has the following
components:: (The subscript i indicates the i-th input or
components
weight..)
weight
1. A set of inputs, xi
2. A set of weights, wi
3. A summing Unit, s
4. A threshold / bias
bias,, weight w0 with a bias value of x0
5. An activation function, g
6. Neuron output, y
Basic characteristics of biological neurons
Biological neurons, the basic building blocks of the brain,
are slower than silicon logic gates.
gates.
The neurons operate in milliseconds which is about six
orders of magnitude slower than the silicon gates
operating in the nanosecond rangerange..
The brain makes up for the slow rate of operation with
two factors
factors::
a huge number of nerve cells (neurons) and
interconnections between them.them.
The number of neurons is estimated to be in the range of
1010 with 1014 synapses (interconnections).
(interconnections).
The brain is very energy efficient
efficient.. It consumes only
about 10-16 joules per operation per second, comparing
with 10-6 J/
J/oper
oper · sec for a digital computer.
computer.
