Biomedical Engineering Reference
In-Depth Information
1. The activity of the neuron is an “all-or-none” process.
2. A certain fixed number of synapses must be excited within the period of latent
addition in order to excite a neuron at any time, and this number is independent
of previous activity and position of the neuron.
3. The only significant delay within the nervous system is synaptic delay.
4. The activity of any inhibitory synapse absolutely prevents excitation of the
neuron at that time.
5. The structure of the net does not change with time.
The McCulloch-Pitts neuron is a binary device (two stable states of a neuron).
Each neuron has a fixed threshold, and every neuron has excitatory and inhibitory
synapses, which are inputs of the neuron. But if the inhibitory synapse is active, the
neuron cannot turn on. If no inhibitory synapses are active, the neuron adds its
synaptic inputs. If the sum exceeds or equals the threshold, the neuron becomes
active. So the McCulloch-Pitts neuron performs simple threshold logic.
The central result of their paper is that the network of the simplest McCulloch-
Pitts neurons can realize any finite complex logical expression and compute any
arithmetic or logical function. It was the first connectionist model.
2.3 Hebb Theory
Hebb tried to work out the general theory of behavior [
6
]. The problem of under-
standing behavior is the problem of understanding the total action of the nervous
system, and vice versa. He attempted to bridge the gap between neurophysiology
and psychology. Perception, learning in perception, and assembly formation were
the main themes in his scientific investigations. Experiments had shown perceptual
generalization. The repeated stimulation of specific receptors will lead to the
formation of an “assembly” of association-area cells which can act briefly as a
closed system. The synaptic connections between neurons become well-developed.
Every assembly corresponds to any image or any concept. The idea that an image is
presented by not just one neuron but by an assembly is fruitful. Any concept may
have different meanings. Its content may vary depending on the context. Only the
central core of the concept whose activity may dominate in the system as a whole
can be almost unchangeable. The possible presentation of an image or concept with
one neuron deprives this concept of its features and characteristics. The presenta-
tion with a neuron assembly makes possible a concept or image description with
all features and characteristics. These features can be influenced by the context of
the situation where the concept is used. For example, we create the model of the
concept “building” (Fig.
2.1
). We can observe the building from different positions.
A perceived object (building) consists of a number of perceptual elements. We can
see many windows or a door.
But from different positions there are walls and a roof of this building. In an
assembly that is the model of the concept “building,” a set of neurons corresponds
