A Foundational Theory for Decentralized Sensory Learning

In both neuroscience and artificial intelligence, popular functional frameworks and neural network formulations operate by making use of extrinsic error measurements and global learning algorithms. Through a set of conjectures based on evolutionary insights on the origin of cellular adaptive mechanisms, we reinterpret the core meaning of sensory signals to allow the brain to be interpreted as a negative feedback control system, and show how this could lead to local learning algorithms without the need for global error correction metrics. Thereby, a sufficiently good minima in sensory activity can be the complete reward signal of the network, as well as being both necessary and sufficient for biological learning to arise. We show that this method of learning was likely already present in the earliest unicellular life forms on earth. We show evidence that the same principle holds and scales to multicellular organisms where it in addition can lead to division of labour between cells. Available evidence shows that the evolution of the nervous system likely was an adaptation to more effectively communicate intercellular signals to support such division of labour. We therefore propose that the same learning principle that evolved already in the earliest unicellular life forms, i.e. negative feedback control of externally and internally generated sensor signals, has simply been scaled up to become a fundament of the learning we see in biological brains today. We illustrate diverse biological settings, from the earliest unicellular organisms to humans, where this operational principle appears to be a plausible interpretation of the meaning of sensor signals in biology, and how this relates to current neuroscientific theories and findings.

Rich dynamics caused by known biological brain network features resulting in stateful networks

The mammalian brain could contain dense and sparse network connectivity structures, including both excitatory and inhibitory neurons, but is without any clearly defined output layer. The neurons have time constants, which mean that the integrated network structure has state memory. The network structure contains complex mutual interactions between the neurons under different conditions, which depend on the internal state of the network. The internal state can be defined as the distribution of activity across all individual neurons across the network. Therefore, the state of a neuron/network becomes a defining factor for how information is represented within the network. Towards this study, we constructed a fully connected (with dense/sparse coding strategies) recurrent network comprising of both excitatory and inhibitory neurons, driven by pseudo-random inputs of varying frequencies. In this study we assessed the impact of varying specific intrinsic parameters of the neurons that enriched network state dynamics, such as initial neuron activity, amount of inhibition in combination with thresholded neurons and conduction delays. The impact was assessed by quantifying the changes in mutual interactions between the neurons within the network for each given input. We found such effects were more profound in sparsely connected networks than in densely connected networks. However, also densely connected networks could make use of such dynamic changes in the mutual interactions between neurons, as a given input could induce multiple different network states.