The switching and learning behavior of an octopus cell implemented on FPGA.

A dendrocentric backpropagation spike timing-dependent plasticity learning rule has been derived based on temporal logic for a single octopus neuron. It receives parallel spike trains and collectively adjusts its synaptic weights in the range [0, 1] during training. After the training phase, it spikes in reaction to event signaling input patterns in sensory streams. The learning and switching behavior of the octopus cell has been implemented in field-programmable gate array (FPGA) hardware. The application in an FPGA is described and the proof of concept for its application in hardware that was obtained by feeding it with spike cochleagrams is given; also, it is verified by performing a comparison with the pre-computed standard software simulation results.

Periodicity Pitch Perception Part III: Sensibility and Pachinko Volatility.

Neuromorphic computer models are used to explain sensory perceptions. Auditory models generate cochleagrams, which resemble the spike distributions in the auditory nerve. Neuron ensembles along the auditory pathway transform sensory inputs step by step and at the end pitch is represented in auditory categorical spaces. In two previous articles in the series on periodicity pitch perception an extended auditory model had been successfully used for explaining periodicity pitch proved for various musical instrument generated tones and sung vowels. In this third part in the series the focus is on octopus cells as they are central sensitivity elements in auditory cognition processes. A powerful numerical model had been devised, in which auditory nerve fibers (ANFs) spike events are the inputs, triggering the impulse responses of the octopus cells. Efficient algorithms are developed and demonstrated to explain the behavior of octopus cells with a focus on a simple event-based hardware implementation of a layer of octopus neurons. The main finding is, that an octopus' cell model in a local receptive field fine-tunes to a specific trajectory by a spike-timing-dependent plasticity (STDP) learning rule with synaptic pre-activation and the dendritic back-propagating signal as post condition. Successful learning explains away the teacher and there is thus no need for a temporally precise control of plasticity that distinguishes between learning and retrieval phases. Pitch learning is cascaded: At first octopus cells respond individually by self-adjustment to specific trajectories in their local receptive fields, then unions of octopus cells are collectively learned for pitch discrimination. Pitch estimation by inter-spike intervals is shown exemplary using two input scenarios: a simple sinus tone and a sung vowel. The model evaluation indicates an improvement in pitch estimation on a fixed time-scale.

Periodicity Pitch Perception.

This study presents a computational model to reproduce the biological dynamics of "listening to music." A biologically plausible model of periodicity pitch detection is proposed and simulated. Periodicity pitch is computed across a range of the auditory spectrum. Periodicity pitch is detected from subsets of activated auditory nerve fibers (ANFs). These activate connected model octopus cells, which trigger model neurons detecting onsets and offsets; thence model interval-tuned neurons are innervated at the right interval times; and finally, a set of common interval-detecting neurons indicate pitch. Octopus cells rhythmically spike with the pitch periodicity of the sound. Batteries of interval-tuned neurons stopwatch-like measure the inter-spike intervals of the octopus cells by coding interval durations as first spike latencies (FSLs). The FSL-triggered spikes synchronously coincide through a monolayer spiking neural network at the corresponding receiver pitch neurons.

Modeling Pitch Perception With an Active Auditory Model Extended by Octopus Cells.

Pitch is an essential category for musical sensations. Models of pitch perception are vividly discussed up to date. Most of them rely on definitions of mathematical methods in the spectral or temporal domain. Our proposed pitch perception model is composed of an active auditory model extended by octopus cells. The active auditory model is the same as used in the Stimulation based on Auditory Modeling (SAM), a successful cochlear implant sound processing strategy extended here by modeling the functional behavior of the octopus cells in the ventral cochlear nucleus and by modeling their connections to the auditory nerve fibers (ANFs). The neurophysiological parameterization of the extended model is fully described in the time domain. The model is based on latency-phase en- and decoding as octopus cells are latency-phase rectifiers in their local receptive fields. Pitch is ubiquitously represented by cascaded firing sweeps of octopus cells. Based on the firing patterns of octopus cells, inter-spike interval histograms can be aggregated, in which the place of the global maximum is assumed to encode the pitch.

Modeling the formation process of grouping stimuli sets through cortical columns and microcircuits to feature neurons.

A computational model of a self-structuring neuronal net is presented in which repetitively applied pattern sets induce the formation of cortical columns and microcircuits which decode distinct patterns after a learning phase. In a case study, it is demonstrated how specific neurons in a feature classifier layer become orientation selective if they receive bar patterns of different slopes from an input layer. The input layer is mapped and intertwined by self-evolving neuronal microcircuits to the feature classifier layer. In this topical overview, several models are discussed which indicate that the net formation converges in its functionality to a mathematical transform which maps the input pattern space to a feature representing output space. The self-learning of the mathematical transform is discussed and its implications are interpreted. Model assumptions are deduced which serve as a guide to apply model derived repetitive stimuli pattern sets to in vitro cultures of neuron ensembles to condition them to learn and execute a mathematical transform.

Synaptic behavior and STDP of asymmetric nanoscale memristors in biohybrid systems.

We fabricate and characterize asymmetric memristors which show a very strong single-sided hysteresis. When biased in one direction there is hysteresis and in the opposite direction there is a lack of hysteresis. We demonstrate that this apparent lack is actually hysteresis on a much faster time-scale. We further demonstrate that this form of asymmetric behavior correlates very well to the asymmetric structure and function of an actual synapse. The asymmetric memristor device presented here is necessary to correctly implement spike-timing-dependent-plasticity STDP in mixed memristor/neuron hybrid systems as an artificial synapse. These devices show the required characteristics for implementing the asymmetric form of long-term potentiation (LTP) and long-term depression (LTD) of a synapse between two neurons, where symmetric memristor devices do not. Signals from a presynaptic neuron are sent via its axon across the synapse to the dendrite of a postsynaptic neuron. Postsynaptic neuron signals sent to subsequent neurons have an influence on the strength of any further presynaptic neuron signals received by the postsynaptic neuron across the synapse. These signals are grouped into spike triplets within the framework of STDP and, as we experimentally show here, can be implemented with asymmetric memristors, not standard symmetric memristors.

An auditory model based strategy for cochlear implants.

A physiological and computational model of the human auditory system has been fitted in a signal processing strategy for cochlear implants (CIs). The aim of the new strategy is to obtain more natural sound in CIs by better mimicking the human auditory system. The new strategy was built in three independent stages as proposed in [6]. First a basilar membrane motion model was substituted by the filterbank commonly used in commercial strategies. Second, an inner hair cell model was included in a commercial strategy while maintaining the original filterbank. Third, both the basilar membrane motion and the inner-hair cell model were included in the commercial strategy. This paper analyses the properties and presents results obtained with CI recipients for each algorithm designed.