Reservoir of neurons with adaptive time constants: a hybrid model for robust motor-sensory temporal processing

Abstract

The ability to precisely quantify time on the scale ofhundreds of milliseconds is critical towards the proces-sing of complex sensory and motor patterns. However,the natures of neural mechanisms for temporal proces-sing (at this scale) in the brain are mostly unknown.Based on experimental data (psychophysics, cell cul-tures, electrophysiology) and theoretical studies, it is lar-gely debated whetherdedicated circuits or intrinsicmechanisms of neural circuits underlie the timing pro-cess [1]. One specific type of timing model, namelystate-dependent networks (SDN) [2], shows that time isencoded in the temporal patterns of activity of neuralpopulations and emerges from the internal dynamics ofrecurrent networks. This can be achieved without theneed of dedicated timing units. However, such intrinsicmodels in their present form have difficulty accountingfor crossmodal transfer [1]. In contrast, recent experi-mental evidence indicates that medial premotor corticalneurons of behaving monkeys show specific intervaltuning across modalities (auditory and visual) [3]. In thiswork we propose a hybrid model, making the hypothesisthat dedicated interval tuning mechanisms of individualneurons augment the intrinsic dynamics of large recur-rent networks (dynamic reservoir). Using a networkmodel of rate-coded neurons starting with random initi-alization of synaptic connections, we propose a learningrule based on local active information storage (LAIS) [4]to adapt neuronal time constants with respect to theinput stimuli to the network. Measured at each spatio-temporal location of the reservoir, LAIS gives a prob-abilistic measure of the amount of information in theprevious state of the neuron that is relevant in predict-ing the next state. Interestingly high LAIS regions in thenetwork correlate to significant events in time (intervals)of the driving stimulus. Furthermore, we combine thiswith mutual information driven intrinsic plasticityscheme in order to stabilize chaotic activity in the net-work. Incoming input drives the network which, in turn,is connected to readout neurons (Figure 1.A) that dis-play the learned behavior for temporally dependent sen-sory motor tasks. Reservoir-to-output connections canbe adapted using both supervised and reward modulatedlearning rules. Using single and multiple interval discri-mination tasks, we show that our network reproduces(across modalities) a linear increase in temporal variabil-ity with increase in interval duration. This correlation isalso observed in experimental data [3]. Furthermore wedemonstrate that our dedicated timing mechanism com-plements the inherent transient dynamics of the net-work by successfully learning complex time dependentmotor behaviors; like handwriting generation (Figure 1.Band C), locomotion pattern transformation and temporalmemory tasks. In essence, our hybrid model demon-stratesthattimecanbeencodedbyacombinationofdedicated and intrinsic mechanisms with the possibilityto ‘learn’ the temporal structure of incoming stimuli [5].