Teaching signals

Teaching signals
How do neurons teach neurons?

Aggregate-label learning. For many decades, research on learning in neural networks, in particular with spiking neurons, has been obscured by the dogma that learning requires temporally precise teaching signals that instruct each neuron at which points in time it should modify its responses. We now know that this dogma is wrong. We have discovered that models of spiking neurons can perform previously unimaginable learning tasks without any time resolved teaching signals (Gütig, 2016). Already binary teaching signals, “aggregate-labels”, without temporal information, that merely instruct neurons to elicit fewer or more output spikes, enable neurons to discover reward predictive features within long streams of noise. Overcoming the dogma of temporally precise teaching signals, i.e. solving the fundamental temporal credit assignment problem (Sutton, 1988), has opened a vast field of new possibilities how learning neurons can interact with the external world and how neurons can teach neurons.

Fig. 2
(A) Network architecture, grey: input layer; blue: student layer; red: teacher unit. (B) Voltage traces of 10 self-supervised student neurons after learning. Despite no external feedback, all student neurons discover and learn to respond to the blue input feature. Modified from Gütig (2016).

Self-supervised learning

​In a first step into this field of possibilities, we have discovered the concept of neural “self-supervision”: Feeding back simple projections of neural activity, such as its mean, as a teaching signal to groups of neurons allows simple neural networks to discover distributed ensembles of features even if their occurrences are rare and widely distributed across space and time (Fig. 2). In this part of the project we will test the neurobiological implementations of aggregate-label learning and neural self-supervision and hunt for their traces in human and animal learning behaviors. This part of our project also has a crucial theoretical component within which will combine and extend our theoretical advances to realize supervised and unsupervised learning, side-by-side, within deep multi-layer networks of spiking neurons.

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Power in Numbers




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