Consciousness as an Emergent Property

Consciousness as an Emergent Property

As a pedagogical example to aid in understanding the emergent property of consciousness, consider an isolated network of many millions of neurons, somehow supplied with nutrients and oxygen adequate for metabolic functions.

The network of neurons is connected by synapses, each neuron having approximately 10,000 synaptic inputs. Whenever a neuron has sufficient input signals in a ~2 millisecond time window, the neuron fires, injecting a signal into the network. This injected signal joins millions of other signals propagating in the network, following pathways via the most efficacious synapses. Signals circulate in the network continuously, with each neuron in either a quiescent state below 5 Hz or an active state between about 5 Hz and 100 Hz.

A pulse in the active state lasts ~4 ms, implying that a neuron can fire at a maximum rate of ~250 Hz. Network neurons possess intrinsic oscillatory properties, causing them to fire stochastically at a few Hertz even in the quiescent state. When a neuron happens to be along an active neural signal pathway, the neuron will fire between approximately 5 Hz and 100 Hz together with other neurons along the multi-circuit pathway.

The neural network is degenerate, redundant, and robust. No individual neuron is required to complete a pathway. Stochastic properties of neurons cause the signal flows on active pathways to be composites of multiple individual neuronal links that may "flicker on and off" on the verge of conduction.

Sensory Input Signal

Let a sensory signal be injected into this network. The signal will follow a pattern of pathways via greatest synaptic efficacies.

The pattern of pathways followed by the input signal corresponds most closely to the pattern of synaptic efficacies that have been established in the network by genetics and prior experience.

The pattern of network activity conforms to the pattern of the input signal.

As a vague metaphor, consider that the input signal propagates through the neural network, settling into "its most comfortable fit" via the highest synaptic efficacies, "hand in glove fashion." This propagation of the input signal via its closest synaptic fit activates a specific memory, and concomitantly, the dynamic core of consciousness.

Dynamic Core of Consciousness

If some means is available for detecting this pattern of neural activity, this corresponds to perception. The pattern of synaptic efficacies of the network corresponds to memory. If some means is available for convolving the perception and the memory, the neural activity involved in this process corresponds to the dynamic core of consciousness.

Synaptic Plasticity and Memory

The signal produces changes in the synaptic efficacies via plasticity in synapses along the input signal pathways.

The input signal pattern locks-in spatially and temporally to a pattern of synaptic efficacies that conforms to the input signal’s pattern. The input signal produces some changes in the efficacies of the synapses. These changes in synaptic efficacies, which can sometimes be larger and sometimes smaller, and can sometimes fade and nearly disappear or be overwhelmed by other changes, constitute the mechanism of memory.

Computers Cannot Be Conscious

A portion of the activity pattern in the network corresponds to the emergent property of consciousness. The activity pattern of consciousness, called the dynamic core, is ever changing on a millisecond-by-millisecond basis. The emergent property of consciousness is associated with a biological network of neurons, which has evolved from elementary molecular beginnings over billions of years. Although computer simulations can aid in providing insight for understanding the mechanisms of consciousness, non-biological computers can never possess the property of consciousness.

A Caveat for Caution

This pedagogical example is simplistic and theoretical. Don't try to push it too far. Just try to understand that consciousness is an emergent property of the neural activity of the dynamic core subset of neural activity.