Stimulus-Response Circuits

From Mbscientific_wiki

Evolution of the Central Nervous System and the Brain

The central nervous system consists of the spinal chord (notochord in spineless animals) and the brain. It ties into the sensory nerves (touch, taste, smell, sight, sound.), the motor nerves (muscles), and the glandular nerves (hormone producers), among others. Effectively, the entire system is an integrated neural network. We are interested in the evolution of the central nervous system and specifically the functional (evolutionary) anatomy of the brain.

Instinctive functionality of biotic neural networks - stimulus-response

Single cells demonstrate stimulus/response behavior. We can view cell functions as internally directed, e.g. replication, metabolism, structural, transport, intra-cell signaling, defense and immunity. Or, cell functions can be externally directed, e.g. motor/sensor, cell-cell signaling. So the stimulus would be that of external functions which will then garner a response triggered via internal functions.

Say the stimulus would be an external input (smell of food chemical - Sm) triggering the sensory functions, then internal functions (intra-cell signaling) would trigger motor functions (motor response - Mo), propelling the movement of the cell towards the food source. (Fig A, smell and motor function are within the same cell). So, a single cell, say an amoeba senses food and moves towards it to eat ( A movie of an amoeba gobbling up algae cells (video at Youtube) ).

Now take the same stimulus-response behavior of a single cellular cluster and spread it to two differentiated cell clusters (Fig B), half handle stimulus (smell sensory neurons in red ), the rest handle the response (motor cells) .

Lets add some more complexity to the picture. Say our fictional creature (lets call it wikit) can smell food (SmF cells) and can small its enemy (SmE cells, which eats wikits). The sensory neurons will then pass the signal to a corresponding control cell cluster (C1E and C1F, which just relays the info to the motor cell cluster (Fig C, below). So if wikit smells food it'll fire the motor neurons in that direction, if it smells the enemy it'll fire away from it.

To make the figure simpler, lets combine the Smell cells SmF, SmE to a single cluster Sm (Fig D). Lets give the Attractor neurons SmF a firing value range of 0 to +SmMax and the Suppressor Neurons a value range of -SmMax to 0. Then we can combine the controller cell cluster (C1, Fig D). All C1 has to do is to integrate (add) the firing value ranges of Sm cluster and pass the result to Mo (motor) neurons. So, if the value is positive fire toward source of Sm with the proportional intensity and if Sm is negative fire away from the source, i.e. fire towards the food source and away from the enemy source.

Next, lets add some more complexity to the stimulus-response nature of our wikit. Lets give it taste sensors. It'll work just as the smell sensor-control-motor cluster did. In figure E, you'll see a parallel track for taste sensor cluster (Ta, in red ) and a corresponding control cell cluster, labeled C1,2, that is control cell cluster layer 1, track 2. And the smell control cluster becomes labeled as layer 1, track 1.

One sees an immediate problem. There is no integration between firing of C1,1 and C1,2. The motor neuron could go haywire. So in Fig F we'll add a second control layer C2 to integrate the signals of layer 1, in this case C1,1 and C1,2. It'll add the positive and negative signals and just as before proportionally fire the motor neurons towards and away from the source.

But we have another problem. The firing values are all of equal weight. In nature the firing values are dynamically weight adjusted. This requires a feedback that is either positive or negative. Say the wikit smells food and enemy, with food being the stronger signal. It inches towards the source; then wham the enemy pops up and takes out a chunk of wikit butt. Then the pain neurons will scream, sending a strong signal to the suppressor smell sensor-controller connection, amplifying those connections, thereby giving a weight, a multiplier to the enemy signal. So the next time the wikit smells food and enemy of equal magnitudes, the controller weighs the enemy signal proportionally, dwarfing the food signal and passing the information up the control chain to get the hell out of there pronto.

Another example would be comparing the smell and taste signals. Say the smell signal is long range, the taste short range. So the reward of the taste signal would be quick, sending a positive signal to the attractor taste sensor-control synapse (and/or taste/smell integrator synapse), amplifying it positively. So the next time the wikit tastes food nearby, with no suppressor signals around, it'll go for it like a bat out of hell.

So the feedback loops are a way of dynamic learning in nature, positive behavior in reinforced, negative behavior suppressed.

In nature feedback sensor-controls might take various forms, could be pain like the above example, could be pleasure (particularly yummy food, abundant food); it might be any number of things. So instead of cluttering our drawing with a whole permutation of feedback nodes (as important as they are), I'll just indicate them generally (Fig G below).

Already our wikit stimulus-response neural net can execute a fair bit of logic, and some learned behavior. Depending on the dynamically weight adjusted smell and taste values the wikit brain integrates them and decides on what to do. It can do AND/OR/NOT logic on an analog range of weighed attractor/suppressor signals.

Now lets look at some animals in order of complexity to see the evolution of the nervous system at work. The earliest neural nets we see are in the earliest morphologies. Here are drawings of a sea anemone and a sea star. What you see is a simple stimulus-response network without much control circuitry in between. There is no brain formation (source: http://www.emc.maricopa.edu/faculty/farabee/BIOBK/BioBookNERV.html).