Bioinspired Computing
Lecture 5
Biological Neural Networks
Netta Cohen
Last week:
We introduced swarm intelligence.
We saw how many simple agents can follow simple rules
that allow them to collectively perform more complex
tasks.
Today...
Biological systems whose manifest function is information
processing: computation, thought, memory, communication
and control. We begin a dissection of a brain:
How different is a brain from an artificial computer?
How can we build and use artificial neural networks?
Investigating the brain
Imagine landing on an abandoned alien planet and finding
thousands of alien computers. You and your crew’s
mission is to find out how they work. What do you do?
Summon Scottie, your engineer
Summon Data - your software wiz
to disassemble the machines
into component parts, test each
part (electronically, optically,
chemically…), decode the
machine language, and study
how components are connected.
to connect to the input & output
ports of a machine, find a
language to communicate with it
& write computer programs to
test the system’s response by
measuring its speed, efficiency
& performance at different tasks.
Inputs
Input
program
part
#373a
Outputs
The
computer
Output
The brain as a computer
Higher level functions in animal behaviour
• Gathering data (sensation)
• Inferring useful structures in data (perception)
• Storing and recalling information (memory)
• Planning and guiding future actions (decision)
• Carrying out the decisions (behaviour)
• Learning consequences of these actions
Hardware functions and architectures
• 10 billion neurons in human cortex
• 10,000 synapses (connections) per neuron
• Machine language: 100mV, 1-2msec spikes (action potential)
• Specialised regions & pathways (visual, auditory, language…)
versus
The brain as a computer
Special task: program often
hard-coded into system.
Universal, general-purpose.
Software: general, user-supplied.
Hardware not hard: plastic,
rewiring.
Hardware is hard:
Only upgraded in discrete units.
No clear hierarchy. Bi-diretional
feedback up & down the system.
Obvious hierarchy: each
component has a specific function.
Unreliable components.
Parallelism, redundancy appear
to compensate.
Once burned in, circuits run
without failure for extended
lifetimes.
Output doesn’t always match
input: Internal state is important.
Input-output relations are welldefined.
Development & evolutionary
constraints are crucial.
Engineering design depends on
engineer. Function is not an issue.
Neuroscience pre-history
• 200 AD: Greek physician Galen hypothesises that nerves
carry signals back & forth between sensory organs & the brain.
• 17th century: Descartes suggests that nerve signals account
for reflex movements.
• 19th century: Helmholtz discovers the electrical nature of
these signals, as they travel down a nerve.
• 1838-9: Schleiden & Schwann systematically study plant &
animal tissue. Schwann proposes the theory of the cell (the
basic unit of life in all living things).
• Mid-1800s: anatomists map the structure of the brain.
but…
The microscopic composition of the brain remains elusive. A
raging debate surrounds early neuroscience research, until...
The neuron doctrine
Ramon y Cajal (1899)
1) Neurons are cells: distinct entities (or agents).
2) Inputs & outputs are received at junctions called synapses.
3) Input & output ports are distinct. Signals are uni-directional
from input to output.
Today, neurons (or nerve cells) are regarded as the basic
information processing unit of the nervous system.
Inputs
neuron
Outputs
The neuron as a transistor
• Both have well-defined inputs and outputs.
• Both are basic information processing units that comprise
computational networks.
If transistors can perform logical operations, maybe neurons
can too?
Neuronal function is typically modelled by a combination of
• a linear operation (sum over inputs) and
• a nonlinear one (thresholding).
This simple representation relies on Cajal’s concept of
input  neuron  output
Machine language
The basic “bit” of information is represented by neurons in
spikes. The cell is said to be either at rest or active. A spike
(action potential) is a strong, brief electrical pulse. Since
these action potentials are mostly identical, we can safely
refer to them as all-or-none signals.
Why Spikes?
Why don’t neurons use analog signals? One answer lies in
the network architecture: signals cover long distances (both
within the brain and throughout the body). Reliable
transmissions requires strong pulses.
Computation of a pyramidal neuron
soma
axon
Many inputs
(dendrites)
Single
all-or-none
output

From transistors to networks
We can now summarise our working principles:
• The basic computational unit of the brain is the neuron.
• The machine language is binary: spikes.
• Communication between neurons is via synapses.
However, we have not yet asked how information is
encoded in the brain, how it is processed in the brain, and
whether what goes on in the brain is really ‘computation’.
Information codes
Temporal code
Neural code
Rate code
Population code/
Distributed code
noise
Examples of both neural codes and distributed
representations have been found in the brain.
Example in the visual system: colour representation,
face recognition, orientation, motion detection, & more…
http://www.cs.stir.ac.uk/courses/31YF/Notes/Notes_NC.html
Information content
Example. A spike train produced by a neuron over an
interval of 100ms is recorded. Neurons can produce a spike
every 2ms.
Therefore, 51 different rates (individual code words) can be
produced by this neuron.
In contrast, if the neuron were using temporal coding, up to
250 different words could be represented.
In this sense, temporal coding is much more powerful.
Circuitry depends on neural code
Temporal codes rely on a noise-free signal transmission.
Thus, we would expect to find very few ‘redundant’ neurons
with co-varying outputs in that network. Accordingly, an
optimal temporal coding circuit might tend to eliminate
redundancy in the pattern of inputs to different neurons.
On the other hand, if neural information is carried by a
noisy rate-based code, then noise can be averaged out
over a population of neurons. Population coding schemes,
in which many neurons represent the same information,
would therefore be the norm in those networks.
Experiments on various brain systems find either coding
systems, and in some cases, combinations of temporal
and rate coding are found.
Neuronal computation
Having introduced neurons, neuronal circuits and even
information codes with well defined inputs and outputs, we
still have not mentioned the term computation. Is neuronal
computation anything like computer computation?
1 1 1 1 0 1
If read 1, write 0, go right, repeat.
If read 0, write 1, HALT!
If read •
, write 1, HALT!
In a computer program, variable have initial states, there are
possible transitions, and a program specifies the rules. The
same is true for machine language. To obtain an answer at
the end of a computation, the program must HALT.
Does the brain initialise variables? Does the brain ever halt?
Association
an example of bio-computation
One recasting of biological brain function in these
computational terms was proposed by John Hopfield in the
1980s as a model for associative memory.
Question: How does the brain associate some memory with
a given input?
Answer: The input causes the network to enter an initial
state. The state of the neural network then evolves until it
reaches some new stable state.
The new state is associated with the input state.
Association (cont.)
Trajectories in a
schematic
state space
Whatever initial condition is chosen, the system will follow a
well-defined route through state-space that is guaranteed to
always reach some stable point (i.e., pattern of activity)
Hopfield’s ideas were strongly motivated by existing theories
of self-organisation in neural networks. Today, Hopfield nets
are a successful example of bio-inspired computing (but no
longer believed to model computation in the brain).
Learning
No discussion of the brain, or nervous systems more generally is
complete without mention of learning.
•
•
•
•
What is learning?
How does a neural network ‘know’ what computation to perform?
How does it know when it gets an ‘answer’ right (or wrong)?
What actually changes as a neural network undergoes ‘learning’?
body
Sensory inputs
brain
Motor outputs
environment
Learning (cont.)
Learning can take many forms:
• Supervised learning
• Reinforcement learning
• Association
• Conditioning
• Evolution
At the level of neural networks, the best understood forms of
learning occur in the synapses, i.e., the strengthening and
weakening of connections between neurons. The brain uses its
own learning algorithms to define how connections should
change in a network.
Learning from experience
How do the neural networks form in the brain? Once
formed, what determines how the circuit might change?
In 1948, Donald Hebb, in his book, "The Organization of
Behavior", showed how basic psychological phenomena of
attention, perception & memory might emerge in the brain.
Hebb regarded neural networks as a collection of cells that
can collectively store memories. Our memories reflect our
experience.
How does experience affect neurons and neural networks?
How do neural networks learn?
Synaptic Plasticity
Definition of Learning: experience alters behaviour
The basic experience in neurons is spikes.
Spikes are transmitted between neurons through synapses.
Hebb suggested that connections in the brain change in
response to experience.
delay
Pre-synaptic cell
Post-synaptic cell
time
Hebbian learning: If the pre-synaptic cell causes the
post-synaptic cell to fire a spike, then the connection
between them will be enhanced. Eventually, this will
lead to a path of ‘least resistance’ in the network.
Today... From biology to information processing
At the turn of the 21st century, “how does it work”
remains an open question. But even the kernel of
understanding and simplified models we already have for
various brain function are priceless, in providing useful
intuition and powerful tools for bioinspired computation.
Next time... Artificial neural networks (part 1)
Focus on the simplest cartoon models of biological neural
nets. We will build on lessons from today to design simple
artificial neurons and networks that perform useful
computational tasks.