Nearly extensive sequential memory lifetime achieved by coupled nonlinear neurons

Many cognitive processes rely on the ability of the brain to hold sequences of events in short-term memory. Recent studies have revealed that such memory can be read out from the transient dynamics of a network of neurons. However, the memory performance of such a network in buffering past information has only been rigorously estimated in networks of linear neurons. When signal gain is kept low, so that neurons operate primarily in the linear part of their response nonlinearity, the memory lifetime is bounded by the square root of the network size. In this work, I demonstrate that it is possible to achieve a memory lifetime almost proportional to the network size, "an extensive memory lifetime", when the nonlinearity of neurons is appropriately utilized. The analysis of neural activity revealed that nonlinear dynamics prevented the accumulation of noise by partially removing noise in each time step. With this error-correcting mechanism, I demonstrate that a memory lifetime of order $N/\log N$ can be achieved.Comment: 21 pages, 5 figures, the manuscript has been accepted for publication in Neural Computatio

Learning the sound inventory of a complex vocal skill via an intrinsic reward

Reinforcement learning (RL) is thought to underlie the acquisition of vocal skills like birdsong and speech, where sounding like one’s “tutor” is rewarding. However, what RL strategy generates the rich sound inventories for song or speech? We find that the standard actor-critic model of birdsong learning fails to explain juvenile zebra finches’ efficient learning of multiple syllables. However, when we replace a single actor with multiple independent actors that jointly maximize a common intrinsic reward, then birds’ empirical learning trajectories are accurately reproduced. The influence of each actor (syllable) on the magnitude of global reward is competitively determined by its acoustic similarity to target syllables. This leads to each actor matching the target it is closest to and, occasionally, to the competitive exclusion of an actor from the learning process (i.e., the learned song). We propose that a competitive-cooperative multi-actor RL (MARL) algorithm is key for the efficient learning of the action inventory of a complex skill

Spatial representation of temporal information through spike timing dependent plasticity

We suggest a mechanism based on spike time dependent plasticity (STDP) of synapses to store, retrieve and predict temporal sequences. The mechanism is demonstrated in a model system of simplified integrate-and-fire type neurons densely connected by STDP synapses. All synapses are modified according to the so-called normal STDP rule observed in various real biological synapses. After conditioning through repeated input of a limited number of of temporal sequences the system is able to complete the temporal sequence upon receiving the input of a fraction of them. This is an example of effective unsupervised learning in an biologically realistic system. We investigate the dependence of learning success on entrainment time, system size and presence of noise. Possible applications include learning of motor sequences, recognition and prediction of temporal sensory information in the visual as well as the auditory system and late processing in the olfactory system of insects.Comment: 13 pages, 14 figures, completely revised and augmented versio

A Bluetooth-Low-Energy Sensor Node for Acoustic Monitoring of Small Birds

Animals can generate sounds that serve a wide range of vital functions such as to defend themselves or their territories, to attract a partner, to maintain contact with other members of their social group, and to help themselves and their partner/group during navigation. Ethologists are interested in recording and analyzing these sounds, many of which are vocalizations. Advances in sensing and wireless technology permit today acoustic data acquisition and transmission in a wireless manner. In many applications, the wireless sensor needs to be placed on the animal's body and should be unobtrusive, light-weight, small, and long-lasting. This paper presents the design and development of an ultra-low power miniaturized and lightweight wireless sensor node for monitoring captive zebra finches. The node is designed to be worn with minimal effort by small-sized birds to collect, process, and send/receive data to/from a remote host via Bluetooth Low-Energy. The main feature of the developed node is the capability to stream compressed or uncompressed audio and temperature data continuously. Multiple nodes can monitor several birds simultaneously and acquire and transmit high-quality audio streams, one for each bird, with low audio interference. Due to the combination of low-power hardware and software techniques and technologies, the 1.4 g node achieves a lifetime of up to 24 h at 4 kHz sampling rate on a single zinc-air battery. Experimental results on birds confirm the functionality of the developed wireless node and the lifetime benefits of compression