Mecanismos de codificación y procesamiento de información en redes basadas en firmas neuronales

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones. Fecha de lectura: 21-02-202

Strategies for creating new informational primitives in minds and machines

Open-endedness is an important goal for designing systems that can autonomously find new and expected solutions to combinatorically-complex and ill-defined problems. Classically, issues of open-ended generation of novelty in the universe have come under the rubric of the problem of emergence. We distinguish two modes of creating novelty: combinatoric (new combinations of existing primitive

Spike timing-dependent plasticity induces non-trivial topology in the brain.

We study the capacity of Hodgkin-Huxley neuron in a network to change temporarily or permanently their connections and behavior, the so called spike timing-dependent plasticity (STDP), as a function of their synchronous behavior. We consider STDP of excitatory and inhibitory synapses driven by Hebbian rules. We show that the final state of networks evolved by a STDP depend on the initial network configuration. Specifically, an initial all-to-all topology evolves to a complex topology. Moreover, external perturbations can induce co-existence of clusters, those whose neurons are synchronous and those whose neurons are desynchronous. This work reveals that STDP based on Hebbian rules leads to a change in the direction of the synapses between high and low frequency neurons, and therefore, Hebbian learning can be explained in terms of preferential attachment between these two diverse communities of neurons, those with low-frequency spiking neurons, and those with higher-frequency spiking neurons

Temporal Coding of Periodicity Pitch in the Auditory System: An Overview

This paper outlines a taxonomy of neural pulse codes and reviews neurophysiological evidence for interspike interval-based representations for pitch and timbre in the auditory nerve and cochlear nucleus. Neural pulse codes can be divided into channel-based codes, temporal-pattern codes, and time-of-arrival codes. Timings of discharges in auditory nerve fibers reflect the time structure of acoustic waveforms, such that the interspike intervals that are produced precisely convey information concerning stimulus periodicities. Population-wide inter-spike interval distributions are constructed by summing together intervals from the observed responses of many single Type I auditory nerve fibers. Features in such distributions correspond closely with pitches that are heard by human listeners. The most common all-order interval present in the auditory nerve array almost invariably corresponds to the pitch frequency, whereas the relative fraction of pitchrelated intervals amongst all others qualitatively corresponds to the strength of the pitch. Consequently, many diverse aspects of pitch perception are explained in terms of such temporal representations. Similar stimulus-driven temporal discharge patterns are observed in major neuronal populations of the cochlear nucleus. Population-interval distributions constitute an alternative time-domain strategy for representing sensory information that complements spatially organized sensory maps. Similar autocorrelation-like representations are possible in other sensory systems, in which neural discharges are time-locked to stimulus waveforms

Poker-DVS and MNIST-DVS. Their History, How They Were Made, and Other Details

This article reports on two databases for event-driven object recognition using a Dynamic Vision Sensor (DVS). The first, which we call Poker-DVS and is being released together with this article, was obtained by browsing specially made poker card decks in front of a DVS camera for 2–4 s. Each card appeared on the screen for about 20–30 ms. The poker pips were tracked and isolated off-line to constitute the 131-recording Poker-DVS database. The second database, which we call MNIST-DVS and which was released in December 2013, consists of a set of 30,000 DVS camera recordings obtained by displaying 10,000 moving symbols from the standard MNIST 70,000-picture database on an LCD monitor for about 2–3 s each. Each of the 10,000 symbols was displayed at three different scales, so that event-driven object recognition algorithms could easily be tested for different object sizes. This article tells the story behind both databases, covering, among other aspects, details of how they work and the reasons for their creation. We provide not only the databases with corresponding scripts, but also the scripts and data used to generate the figures shown in this article (as Supplementary Material).Junta de Andalucía TIC-6091Ministerio de Economía y Competitividad TEC2012-37868-C04-01European Union FP7-604102, H2020-64409

Peripheral and Central Mechanisms of Temporal Pattern Recognition

Encoding information into the timing patterns of action potentials, or spikes, is a strategy used broadly in neural circuits. This type of coding scheme requires downstream neurons to be sensitive to the temporal patterns of presynaptic inputs. Indeed, neurons with temporal filtering properties have been found in a wide range of sensory pathways. However, how such response properties arise was previously not well understood. The goal of my dissertation research has been to elucidate how temporal filtering by single neurons contributes to the behavioral ability to recognize timing patterns in communication signals. I have addressed this question using mormyrid weakly electric fish, which vary the time intervals between successive electric pulses to communicate. Fish detect these signals with sensory receptors in their skin. In the majority of species, these receptors fire a single spike in response to each electric pulse. Spiking receptors faithfully encode the interpulse intervals in communication signals into interspike intervals, which are then decoded by interval-selective midbrain neurons. Using in vivo intracellular recordings from awake fish during sensory stimulation, I found that short-term depression and temporal summation play important roles in establishing single-neuron interval selectivity. Moreover, the combination of short-term depression and temporal summation in the circuit resulted in greater diversity of interval tuning properties across the population of neurons, which would increase the population’s ability to detect temporally patterned communication signals. Indeed, I found that the responses of single interval-selective neurons were sensitive to subtle variation in the timing patterns of a specific communication display produced by different individuals. A subset of mormyrid species has sensory receptors that produce spontaneously oscillating potentials. How the electrosensory system of these species established sensitivity to temporally patterned communication signals was completely unknown. Using in vivo extracellular recordings, I demonstrated that these receptors encode sensory stimuli into phase resets, which is the first clear instance of information coding by oscillatory phase reset. Furthermore, the ongoing oscillations conferred enhanced sensitivity to fast temporal patterns that are only found in the communication signals of a large group of fish. Behavioral playback experiments provided further support for the hypothesis that oscillating receptors are specialized for detecting communication signals produced by a group of conspecifics, which is a novel role for a sensory receptor. These findings demonstrate that temporal pattern sensitivity, which was previously thought to be a central processing problem, can also arise from peripheral filtering through a novel oscillatory phase reset mechanism

The Effects of Radiation on Memristor-Based Electronic Spiking Neural Networks

In this dissertation, memristor-based spiking neural networks (SNNs) are used to analyze the effect of radiation on the spatio-temporal pattern recognition (STPR) capability of the networks. Two-terminal resistive memory devices (memristors) are used as synapses to manipulate conductivity paths in the network. Spike-timing-dependent plasticity (STDP) learning behavior results in pattern learning and is achieved using biphasic shaped pre- and post-synaptic spikes. A TiO2 based non-linear drift memristor model designed in Verilog-A implements synaptic behavior and is modified to include experimentally observed effects of state-altering, ionizing, and off-state degradation radiation on the device. The impact of neuron “death” (disabled neuron circuits) due to radiation is also examined. In general, radiation interaction events distort the STDP learning curve undesirably, favoring synaptic potentiation. At lower short-term flux, the network is able to recover and relearn the pattern with consistent training, although some pixels may be affected due to stability issues. As the radiation flux and duration increases, it can overwhelm the leaky integrate-and-fire (LIF) post-synaptic neuron circuit, and the network does not learn the pattern. On the other hand, in the absence of the pattern, the radiation effects cumulate, and the system never regains stability. Neuron-death simulation results emphasize the importance of non-participating neurons during the learning process, concluding that non-participating afferents contribute to improving the learning ability of the neural network. Instantaneous neuron death proves to be more detrimental for the network compared to when the afferents die over time thus, retaining the network’s pattern learning capability