Nigel Stepp

Synaptic Plasticity Enables Adaptive Self-Tuning Critical Networks

During rest, the mammalian cortex displays spontaneous neural activity. Spiking of single neurons during rest has been described as irregular and asynchronous. In contrast, recent in vivo and in vitro population measures of spontaneous activity, using the LFP, EEG, MEG or fMRI suggest that the default state of the cortex is critical, manifested by spontaneous, scale-invariant, cascades of activity known as neuronal avalanches. Criticality keeps a network poised for optimal information processing, but this view seems to be difficult to reconcile with apparently irregular single neuron spiking. Here, we simulate a 10,000 neuron, deterministic, plastic network of spiking neurons. We show that a combination of short- and long-term synaptic plasticity enables these networks to exhibit criticality in the face of intrinsic, i.e. self-sustained, asynchronous spiking. Brief external perturbations lead to adaptive, long-term modification of intrinsic network connectivity through long-term excitatory plasticity, whereas long-term inhibitory plasticity enables rapid self-tuning of the network back to a critical state. The critical state is characterized by a branching parameter oscillating around unity, a critical exponent close to -3/2 and a long tail distribution of a self-similarity parameter between 0.5 and 1.

A negative group delay model for feedback-delayed manual tracking performance

We propose that feedback-delayed manual tracking performance is limited by fundamental constraints imposed by the physics of negative group delay. To test this hypothesis, the results of an experiment in which subjects demonstrate both reactive and predictive dynamics are modeled by a linear system with delay-induced negative group delay. Although one of the simplest real-time predictors conceivable, this model explains key components of experimental observations. Most notably, it explains the observation that prediction time linearly increases with feedback delay, up to a certain point when tracking performance deteriorates. It also explains the transition from reactive to predictive behavior with increasing feedback delay. The model contains only one free parameter, the feedback gain, which has been fixed by comparison with one set of experimental observations for the reactive case. Our model provides quantitative predictions that can be tested in further experiments.

The Muddle of Anticipation

In J. J. Gibsons classic paper “The Problem of Temporal Order in Stimulation and Perception” (1966a), he referred to the difficulties encountered when attempting a sharp distinction between memory and perception as “the muddle of memory.” Resolution of the muddle by J. J. Gibson proceeded by blurring the distinction itself. We develop the conjugate “muddle of anticipation” similarly by blurring the sharp distinction traditionally drawn between anticipation and perception. The subsequent redefinition of the problem is grounded in strong anticipation equated with anticipating synchronization—that which arises from a system itself via lawful regularities embedded in the systems ordinary mode of function. We identify the fit of strong anticipations properties to J. J. Gibsons ecological approach and in so doing introduce the possibility of a potentially deep connection between them, namely, that the coordination of perception with surroundings (direct perception) is a special case of strong anticipation.

Neuromorphic and early warning behavior-based authentication for mobile devices

Finding the balance between security, privacy, and usability for mobile authentication has been an active area of research for the past several years. Many researchers have taken advantage of the availability of multiple sensors on mobile devices and have used these data to train classifiers to authenticate users. For example, implicit authentication algorithms have been developed based on behavior patterns identified from a combination of sensors including location, co-location, application usage, biometric measurements, continuity of interaction between the user and the phone, and possession of the phone [1,2,3,4,5]. Furthermore, the onboard sensors of mobile devices have previously been used to identify users based on touch [6] and fusions of touch and speech inputs [7]. However, a system utilizing low-power onboard electronics for anomaly detection and user classification is lacking. Here, we report on the performance of two subsystems tested in a controlled use scenario to classify and authenticate users of a mobile device. The overall system utilizes two subsystems for anomaly detection and user classification: (1) a neuromorphic chip for continuous, low-power, online monitoring and classification, and (2) an early warning system (EWS) algorithm for longer duration time-series behavioral and biometric classification.

Criticality as a Set-Point for Adaptive Behavior in Neuromorphic Hardware.

Neuromorphic hardware are designed by drawing inspiration from biology to overcome limitations of current computer architectures while forging the development of a new class of autonomous systems that can exhibit adaptive behaviors. Several designs in the recent past are capable of emulating large scale networks but avoid complexity in network dynamics by minimizing the number of dynamic variables that are supported and tunable in hardware. We believe that this is due to the lack of a clear understanding of how to design self-tuning complex systems. It has been widely demonstrated that criticality appears to be the default state of the brain and manifests in the form of spontaneous scale-invariant cascades of neural activity. Experiment, theory and recent models have shown that neuronal networks at criticality demonstrate optimal information transfer, learning and information processing capabilities that affect behavior. In this perspective article, we argue that understanding how large scale neuromorphic electronics can be designed to enable emergent adaptive behavior will require an understanding of how networks emulated by such hardware can self-tune local parameters to maintain criticality as a set-point. We believe that such capability will enable the design of truly scalable intelligent systems using neuromorphic hardware that embrace complexity in network dynamics rather than avoiding it.

A Dynamical Systems Approach to Neuromorphic Computation of Conditional Probabilities

Here we describe a spiking neural network model for obtaining the conditional probability between two random variables from the synaptic weight between two corresponding neurons. Our method does not aim to mimic biologically plausible processes in the brain, but instead aims to replace slow Bayesian updating algorithms with highly parallel, simple computations by taking inspiration from Spike Timing Dependent Plasticity (STDP), a naturally occurring phenomena of neuronal synapses. The neuronal network we develop can operate in two different modes to both learn the joint distribution of variables, and read out conditional probabilities based on partial input. Furthermore, it relies on integer-valued neurons and synapses, making it amenable to hardware-based implementations. We demonstrate this capability of our novel probabilistic computation unit by encoding synthetically generated input data streams into spike trains and letting the synaptic weight of interest converge to the correct conditional probability.

Neuromorphic and Early Warning behavior-based authentication in common theft scenarios

With increased rates of smartphone theft over the past decade, mobile authentication systems that operate on a continual basis are a necessity to meet increasing demands for user privacy, device usage, and authentication accuracy. Rather than forcing end users to continually self-authenticate via password pins or through other means on a time-interval basis [2–7], a system that continuously authenticates users provides a more frictionless relationship between a users device and its physical security. Such a system, if effectively operating on a low-powered, unobtrusive, and secure basis, would make it viable for most consumer mobile devices. In this paper, we build upon our work in [1] to provide a novel authentication scheme that meets these requirements for a commonly adopted system. Our system, iSentinel, hopes to provide an unobtrusive, low-powered solution for detecting and responding to common theft scenarios by continuously authenticating mobile devices in use cases such as walking, texting, and driving.

A Formal Model for Autocatakinetic Systems

Self-organization as a concept has appeared in several arenas, including explanations of physical phenomena, biological systems, and intelligence. A guiding principle for self-organizing systems, especially at the level of intelligent systems, has not been settled upon. So-called autocatakinetic (ACK) systems attempt to provide such a principle through macroscopic thermodynamics but to date have not been formally defined. We attempt to extend ACK by developing a formal model commensurate with its defining properties.