Binding events through the mutual synchronization of spintronic nano-neurons

Abstract

The brain naturally binds events from different sources in unique concepts. It is hypothesized that this process occurs through the transient mutual synchronization of neurons located in different regions of the brain when the stimulus is presented. This mechanism of 'binding through synchronization' can be directly implemented in neural networks composed of coupled oscillators. To do so, the oscillators must be able to mutually synchronize for the range of inputs corresponding to a single class, and otherwise remain desynchronized. Here we show that the outstanding ability of spintronic nano-oscillators to mutually synchronize and the possibility to precisely control the occurrence of mutual synchronization by tuning the oscillator frequencies over wide ranges allows pattern recognition. We demonstrate experimentally on a simple task that three spintronic nano-oscillators can bind consecutive events and thus recognize and distinguish temporal sequences. This work is a step forward in the construction of neural networks that exploit the non-linear dynamic properties of their components to perform brain-inspired computations.

Fig. 1. Binding temporal sequences through synchrony.

a Schematic of the experimental set-up with three spin-torque nano-oscillators electrically connected and coupled through the microwave currents they emit. b, c Microwave output emitted by the network of three coupled oscillators when they are not (b) and when they are (c) synchronized. d Schematic of the fictitious mouse to which four different categories of cheese are presented. Each category generates different activities in the three neurons of the mouse brain. e, f Sequence example of neuron spikes in the mouse brain in the presence of a piece of Cheshire (e) and Cheddar (f), respectively. g Inputs applied to the network, represented as the time of spikes for neurons 2 and 3. The spike of neuron 1 is set as the origin of the sequence and taken as zero. Each color corresponds to a different cheese category, and each data point corresponds to a different piece of cheese. h, i Ramps of current generated in the network upon application of the input spike sequences described in (e) and (f), corresponding to the presentation to the mouse of a piece of Cheshire and Cheddar, respectively, when the network is trained to recognize Cheddar.

Fig. 2. Three hardware spintronic nano-oscillators recognize Cheddar.

Response of the oscillator network trained to recognize Cheddar to spikes sequences generated in the mouse brain when a piece of Cheddar (a, b) or a different cheese (c–e) is presented. The spike sequences generate ramps of currents (top) which translate into variations of the oscillators frequencies (middle) and the network total emitted power (bottom). If a piece of Cheddar is presented (a, b), the ramps of current and their associated variations of frequencies lead to transient mutual synchronization of the three oscillators. This translates into an enhancement of the network total emitted power above the threshold shown in red dotted line (a, b, bottom) meaning recognition. If a different cheese is presented (c–e), the ramps of current do not give rise to the mutual synchronization of the three oscillators, and the total emitted power remains well below the threshold (c–e, bottom), meaning that the network distinguishes that the cheese presented is not Cheddar.