Posts

Making Dronesophila Part 1

Image
       E mulating the Drosophila nervous system was step one which I did successfully on a single laptop using functional groups. This works VERY well.   The functional groups I created were: Neuron Group    Synapse Count central                          1455116 optic_lobes                1217922 visual_projection      268547 AN                                    115292 Kenyon_Cell                92875 ALPN                          88302 CX                                    85831 medulla_intrinsic      81621 visual_centrifugal      76532 DN                                    58402 L1-5                          53746 ALLN                          34872 MBIN                          26501 bilateral                          25724 mechanosensory      19246 MBON                          18529 olfactory                          17689 LHCENT                12565 LHLN                          11217 gustatory                          7950 visual     
Post a Comment
Read more

Use Cases for the emulation of the Drosophila nervous system

 I am often asked, especially by VC types, what can you do with animal emulations? What they are really asking is how can this be applied and how can we make money? I will admit, emulating a nervous system is like telling people you are developing a new computer operating system. There are thousands of things you can run on an operating system so asking for the One thing, is analogous to asking what is the one thing you can run on your new OS that stands apart from all others? There is no One thing but thousands of things.  If I emulated a complete human level system, what is the One thing it can do that would exceed all others? The human brain is capable of thousands and thousands of things it can do. I think you get the picture.  The One thing that I have seen with all animal nervous system emulations is generalized intelligence. The one thing that current AI cannot accomplish which is being able to quickly adapt to any given situation and as we climb the evolutionary scale, we see
Post a Comment
Read more

How do we use the Connectome for AGI?

Image
Using the connectome for AGI is the tricky part of the process. Tricky because it does take some thought behind how to associate sensory neurons to real world concepts and how to read the motor output in order to determine the results. To do this we break down the process simply as Sensory Input > Cortical Processing > Motor Output ( > Sensory Processing > Cortical Processing > ...). Sensory Processing Each connectome has a set of sensory neurons. When we create our AGI, we connect weights to the sensory neurons based on how we perceive the sensory input on a particular sensory neuron. As an example, if we want our AGI system to avoid particular situations, we connect that sensory input to sensory neurons that would cause the connectome to sense pain or unpleasant conditions. If we want our AGI system to want or like a particular input, we connect that sensory input to sensory neurons that would cause pleasant conditions in the connectome. Using Robotics makes this proce
Post a Comment
Read more

What is General Intelligence?

Image
There is a lot of confusion published about Artificial General Intelligence (AGI) so I felt I should weigh in on the subject since I am a serious AGI researcher and I have witnessed AGI first hand. A LOT of people equate the meaning of AGI to be equal to human level intelligence. Let me state emphatically that AGI is not about human intelligence although humans possess general intelligence. Also mice, birds, cats, dogs, and most of the animal kingdom possesses general intelligence. General intelligence is not unique to humans. One of the best ways to describe general intelligence is the tea (also known as the coffee) problem. I could fairly easily build a robot that could make me a cup of tea in my kitchen. It would do everything from get the items needed to dipping a tea bag into the cup after it poured in hot water. Then if I took this same robot over to your house and told it to make tea, using conventional AI, it would fail miserably due to the fact that the robot would have no ide
Post a Comment
Read more

Obstacles and How to Overcome for Connectomic AI

Working with animal connectomes to emulate and create neurorobotic working systems has a number of issues that are surmountable but need to be considered. 1) Finding Connectomic data Data is still shy on the public domain. C elegans is the only full connectome available I am aware of. There are a number of other partial data sets and I would assume a number of data sets are in private hands. In addition, large data sets have a huge number of orphan and missing neuron and synaptic connections. How to overcome Going to conferences and making personal connections would most likely get access to the number of private data sets. In addition, having the backing of a major corporation could also assist in opening doors to private data sets. Providing funds to researchers could also create data sets specific to the data needs of emulations. I was part of a proposal that estimated roughly $30,000 USD would get us the ant connectome dataset so this would be in the neighborhood we would want to
Post a Comment
Read more

Inhibitory Nodes

Image
     I 've long conjectured that the reason we have an inhibitory network in our brains is to prevent an epileptic burst and it seems that this thought has some truth. In my early simulations of neurons, I found quickly that excitatory networks would get to a tipping point where they would just start firing uncontrollably; i.e. every neuron (node) would be firing as fast as it could and the output would end up being a jumbled mess of motor neurons all firing, all the time. I even continue to experience this sometimes in more true emulations due to the timing or the number of neurons aren't in a correct balance so that the inhibitory neurons aren't in a great enough strength to overcome the excitation.  In AI/Deep Learning (et al), inhibitory networks don't exist. The excitatory network is refined by the error factors and outcomes are created by numeric voting. DL tunes an excitatory network by weighted factors.  There is a delicate balance between the excitatory network
2 comments
Read more

Emulasim of a Neuron

Image
    J ust when you need it, I created a new acronym = Emulasim. I define it as not a true emulation but a step up from a simulation like halfway between simulate and emulate. I believe AGI falls somewhere in this category. From my own experiments, I emulate the connectome from given datasets and do a halfway job of emulating the neuron. So today I want to discuss Neuron emulasim. In my C elegans experiments and other lower forms like Ciona, I used a simple model of the neuron. The attributes are: The artificial neuron accumulates weights where weights are defined as the number of synapses and gap junctions that are connected between the presynaptic and postsynaptic neurons; e.g. if Neuron A has 3 synaptic connections to Neuron B, when A fires, it sends a message to B of a weight of 3. A specified threshold must be met within a specified time frame in order for the neuron to fire. If the threshold is not met in a specified time frame (e.g. 200ms) then the neuron hyperpolarizes by either
Post a Comment
Read more