Robust Working Memory in an Asynchronously Spiking Neural Network Realized with Neuromorphic VLSI

We demonstrate bistable attractor dynamics in a spiking neural network implemented with neuromorphic VLSI hardware. The on-chip network consists of three interacting populations (two excitatory, one inhibitory) of leaky integrate-and-fire (LIF) neurons. One excitatory population is distinguished by strong synaptic self-excitation, which sustains meta-stable states of “high” and “low”-firing activity. Depending on the overall excitability, transitions to the “high” state may be evoked by external stimulation, or may occur spontaneously due to random activity fluctuations. In the former case, the “high” state retains a “working memory” of a stimulus until well after its release. In the latter case, “high” states remain stable for seconds, three orders of magnitude longer than the largest time-scale implemented in the circuitry. Evoked and spontaneous transitions form a continuum and may exhibit a wide range of latencies, depending on the strength of external stimulation and of recurrent synaptic excitation. In addition, we investigated “corrupted” “high” states comprising neurons of both excitatory populations. Within a “basin of attraction,” the network dynamics “corrects” such states and re-establishes the prototypical “high” state. We conclude that, with effective theoretical guidance, full-fledged attractor dynamics can be realized with comparatively small populations of neuromorphic hardware neurons

Increased Readiness for Adaptation and Faster Alternation Rates Under Binocular Rivalry in Children

Binocular rivalry in childhood has been poorly investigated in the past. Information is scarce with respect to infancy, and there is a complete lack of data on the development of binocular rivalry beyond the first 5–6 years of age. In this study, we are attempting to fill this gap by investigating the developmental trends in binocular rivalry in pre-puberty. We employ a classic behavioral paradigm with orthogonal gratings, and introduce novel statistical measures (after Pastukhov and Braun) to analyze the data. These novel measures provide a sensitive tool to estimate the impact of the history of perceptual dominance on future alternations. We found that the cumulative history of perceptual alternations has an impact on future percepts, and that this impact is significantly stronger and faster in children than in adults. Assessment of the “cumulative history” and its characteristic time-constant helps us to take a look at the adaptive states of the visual system under multi-stable perception, and brings us closer to establishing a possible developmental scenario of binocular rivalry: a greater and faster relative contribution of neural adaptation is found in children, and this increased readiness for adaption seems to be associated with faster alternation rates

Multi-stable perception balances stability and sensitivity

Supplemental information http://www.frontiersin.org/Computational_Neuroscience/10.3389/fncom.2013.00017/abstractWe report that multi-stable perception operates in a consistent, dynamical regime, balancing the conflicting goals of stability and sensitivity. When a multi-stable visual display is viewed continuously, its phenomenal appearance reverses spontaneously at irregular intervals. We characterized the perceptual dynamics of individual observers in terms of four statistical measures: the distribution of dominance times (mean and variance) and the novel, subtle dependence on prior history (correlation and time-constant). The dynamics of multi-stable perception is known to reflect several stabilizing and destabilizing factors. Phenomenologically, its main aspects are captured by a simplistic computational model with competition, adaptation, and noise. We identified small parameter volumes (3% of the possible volume) in which the model reproduced both dominance distribution and history-dependence of each observer. For 21 of 24 data sets, the identified volumes clustered tightly (15% of the possible volume), revealing a consistent “operating regime” of multi-stable perception. The “operating regime” turned out to be marginally stable or, equivalently, near the brink of an oscillatory instability. The chance probability of the observed clustering was <0.02. To understand the functional significance of this empirical “operating regime,” we compared it to the theoretical “sweet spot” of the model. We computed this “sweet spot” as the intersection of the parameter volumes in which the model produced stable perceptual outcomes and in which it was sensitive to input modulations. Remarkably, the empirical “operating regime” proved to be largely coextensive with the theoretical “sweet spot.” This demonstrated that perceptual dynamics was not merely consistent but also functionally optimized (in that it balances stability with sensitivity). Our results imply that multi-stable perception is not a laboratory curiosity, but reflects a functional optimization of perceptual dynamics for visual inference.Alexander Pastukhov, Joachim Haenicke, and Jochen Braun: BMBF Bernstein Network, EU FP7-269459. Gustavo Deco: BFU2007-61710, Consolider Ingenio 2010, FP7 Brainsync, ITN Codde. Antoni Guillamon: MICINN/FEDER MTM2009-06973 and CUR-DIUE 2009SGR-859. Pedro E. García-Rodríguez: BFU2007-61710