Welcome to Tipalo
Neurons are the basic unit of any biological brain, by building together higher forms of organization,
such as neural nets, specific brain areas and furthermore specialized single brains like the neocortex.
The functionality of every neuron is non-linear, as it will only fire within a certain period of time,
when the sum of positive entries minus the sum of negative entries will exceed the actual firing level.
Furthermore, neurons will fire only if they are needed for a very specific purpose,
which means only some neurons will fire at the same time, not all of them.
Every neuron within a biological neural net is connected with many others, but
they never fire in series, means they are not organized as lines and rows in a table.
Two additional important features of biological neural nets are the following:
1. the capacity of self-learning, meaning neural nets can add new connections and cut old ones
2. the capacity of plasticity, where the weight of a neural connection can change
Every neuron is a living cell within an multi-cellular organism, while always adapting to new conditions.
This implies math is never a viable option to describe the functionality of any neural net.
Tipalo GmbH - what we do
Taking the biological neural nets as a template for software development,
Tipalo has created a completely digital biomimetic model of the brain,
enabling hereby living machines, which can act fully autonomous,
being capable of self-learning, only by reacting with the environment.
This biological inspired model is digitally implemented in hard- and software, as
Time based pattern logic, programmed in VHDL for FPGA SiPs as neural nets,
which form together higher forms of organizational structures and hierarchies,
used as ANS - Artificial Nervous Systems, with own self-learning mechanism.
The basic underlying principle of our digital biomimetic model of the brain is as follows:
"Many small things, which react permanently with each other in a space, build together a bigger object."
Therefore, our model creates an own space and time flow, where objects can interact with each other,
while the self-learning mechanism is the result of the internal interaction between the neural nets.
Biologically inspired general-purpose AI --> a digital brain with an Artificial Nervous System
Hereby some thoughts about biological intelligence and biologically inspired engineering.
Intelligence can only exist in an environment as being part of an object with a certain given structure.
An object receives different types of signals from the outside world by using specific sensors, which will
transform these signals into internal information and send them further to the embedded body intelligence.
The intelligence will process the information and will generate a response to this stimuli by sending new
information to specialized body components, which on their side will cause a body reaction, forming a cycle.
This is the process we know today about biological intelligence, from insects via mammals to humans.
Some FACTS become obvious in this case:
1. There is an environment containing many objects of different size and complexity.
2. There are some objects, consisting of different connected components.
3. Inside certain objects there is a specialized component, which does intelligence.
4. The structure of intelligence is given by certain elements, which build a system as networks.
5. The purpose of every intelligence is the cooperation with all the other body components,
by performing its own specific tasks; means receive, process and send information.
Intelligence is a system of elements connected to other components of a certain given body.
Intelligence is always active, means the processing is real-time, all-the-time and stand-alone.
Intelligence adapts to new requirements, by developing its own specialty, namely to process information.
There is a time flow, during which the matter reacts and adapts itself. (= time based processing)
The result of the adaption is the self-organization to meet the requirements. (= specialty)
The periodical adaption to various new stimuli is called evolution.(= self-learning mechanism)