In 1943, American neurophysiologist and cybernetician of the the University of Illinois, Chicago Warren McCulloch and psychologist Walter Pitts published a paper ‘A Logical Calculus of the Ideas Imminent in Nervous Activity’ describing the ‘McCulloch-Pits’ (MCP) neuron. It was the first mathematical model of a neural network.
They described brain functions in abstract terms, and showed that simple elements connected in a neural network can have immense computational power.
The paper received little attention. The ideas were applied by John von Neumann, Norbert Wiener and others.
MCP paper was the pioneer in the field of Artificial Intelligence (AI) and cognitive science. It is a core event in computer science and AI. The brain is considered a neural network and the mind is interpreted as a product of its functional properties.
Biological neuron takes an input signal (dendrite), processes it like CPU (soma), passes it through a cable like structure to other connected neurons (axon to synapse to other neuron’s dendrite). There is a lot more than this in the functioning of a biological neuron, but broadly what happens in our brain is that there is an input, there is processing, and there is an output. The sensory organs send the input to activate a neuron. The decision making is actually done by a couple of neurons.
Human brain consists of an interconnected network of 10 raised to 11 neurons (100 billion). The connections are complex.
The output of the processes is passed on to the next layers in a hierarchical manner. There is division of work. A neuron may perform a certain role to a certain stimulus. Each layer has its own role and responsibility. Some functions, e.g. face recognition, could involve many layers.
MCP designed a network consisting of nodes — a part that takes an input, and a part that makes a decision. The neuron learns Boolean functions — inputs are Boolean and the output is also boolean.
The MCP neuron is a binary neuron, which manifests either active or inactive states. Its activation is determined by the sum of inputs received by it. If the sum of the inputs is greater than a certain threshold, the neuron fires and remains active. If the sum of inputs is less than or equal to the threshold, the neuron remained inactive. The MCP neurons can represent any logical expression — logical function such as AND, OR and NOT could be implemented by the MCP neurons.
Brain simulation is problematic because of the complexity of its structure consisting of 100 billion neurons and 1000 trillion synaptic interconnections. Beside communication in the brain is not digital, it is electromechanical. There are inter-related timing and analogue components. Simulation of brain is beyond the technological reach today.
Neural networks roughly resemble the structure of the brain. The architecture is arranged into layers, and each layer has processing units called nodes. These are in turn connected to other nodes in the layers either above or below. The data fed into the lowest layer is passed on to the next layers. Artificial neural networks are fed with huge amount of data. These are designed to function like biological neural networks. However, brain’s functioning is much more complex.
Real neurons do not compute the output by summing up the weighted inputs. Real neurons do not remain on until the inputs alter. The output might encode information using pulse arrangements.
Dr. Licata published a paper in Journal of Computer Science and Biology. The paper questions whether artificial neural networks are good models for human minds.
According to Dr. Licata, they are not good models for human mind. However, it does not make them useless since they do computation in parallel.
Modern science has yet to distinguish between human mind and brain. Research is needed for the concept of consciousness. It is necessary to understand how thought emanates. Artificial feedback is unstable. Neurons in the brain which do thinking and planning have tree-like structures. It is not clear how the brain solves the credit assignment problem.
It is necessary to integrate research in neuroscience and AI, since the paper of MCP in 1943, there is very little integration of neuroscience and AI.