 Award ID(s):
 1810758
 Publication Date:
 NSFPAR ID:
 10324574
 Journal Name:
 11th Innovations in Theoretical Computer Science Conference (ITCS 2020)
 Sponsoring Org:
 National Science Foundation
More Like this

Biological neural computation is inherently asynchronous due to large variations in neuronal spike timing and transmission delays. Sofar, most theoretical work on neural networks assumes the synchronous setting where neurons fire simultaneously in discrete rounds. In this work we aim at understanding the barriers of asynchronous neural computation from an algorithmic perspective. We consider an extension of the widely studied model of synchronized spiking neurons [Maass, Neural Networks 97] to the asynchronous setting by taking into account edge and node delays.  Edge Delays: We define an asynchronous model for spiking neurons in which the latency values (i.e., transmission delays) of non selfloop edges vary adversarially over time. This extends the recent work of [Hitron and Parter, ESA'19] in which the latency values are restricted to be fixed over time. Our first contribution is an impossibility result that implies that the assumption that selfloop edges have no delays (as assumed in Hitron and Parter) is indeed necessary. Interestingly, in real biological networks selfloop edges (a.k.a. autapse) are indeed free of delays, and the latter has been noted by neuroscientists to be crucial for network synchronization. To capture the computational challenges in this setting, we first consider the implementation of amore »

We consider the task of measuring time with probabilistic threshold gates implemented by bioinspired spiking neurons. In the model of spiking neural networks, network evolves in discrete rounds, where in each round, neurons fire in pulses in response to a sufficiently high membrane potential. This potential is induced by spikes from neighboring neurons that fired in the previous round, which can have either an excitatory or inhibitory effect. Discovering the underlying mechanisms by which the brain perceives the duration of time is one of the largest open enigma in computational neuroscience. To gain a better algorithmic understanding onto these processes, we introduce the neural timer problem. In this problem, one is given a time parameter t, an input neuron x, and an output neuron y. It is then required to design a minimum sized neural network (measured by the number of auxiliary neurons) in which every spike from x in a given round i, makes the output y fire for the subsequent t consecutive rounds.We first consider a deterministic implementation of a neural timer and show that Θ(logt)(deterministic) threshold gates are both sufficient and necessary. This raised the question of whether randomness can be leveraged to reduce the number ofmore »

We initiate the study of biologicallyinspired spiking neural networks from the perspective of streaming algorithms. Like computers, human brains face memory limitations, which pose a significant obstacle when processing large scale and dynamically changing data. In computer science, these challenges are captured by the wellknown streaming model, which can be traced back to Munro and Paterson `78 and has had significant impact in theory and beyond. In the classical streaming setting, one must compute a function f of a stream of updates 𝒮 = {u₁,…,u_m}, given restricted singlepass access to the stream. The primary complexity measure is the space used by the algorithm. In contrast to the large body of work on streaming algorithms, relatively little is known about the computational aspects of data processing in spiking neural networks. In this work, we seek to connect these two models, leveraging techniques developed for streaming algorithms to better understand neural computation. Our primary goal is to design networks for various computational tasks using as few auxiliary (noninput or output) neurons as possible. The number of auxiliary neurons can be thought of as the "space" required by the network. Previous algorithmic work in spiking neural networks has many similarities with streaming algorithms.more »

Evolution has honed predatory skills in the natural world where localizing and intercepting fastmoving prey is required. The current generation of robotic systems mimics these biological systems using deep learning. Highspeed processing of the camera frames using convolutional neural networks (CNN) (frame pipeline) on such constrained aerial edgerobots gets resourcelimited. Adding more compute resources also eventually limits the throughput at the frame rate of the camera as frameonly traditional systems fail to capture the detailed temporal dynamics of the environment. Bioinspired event cameras and spiking neural networks (SNN) provide an asynchronous sensorprocessor pair (event pipeline) capturing the continuous temporal details of the scene for highspeed but lag in terms of accuracy. In this work, we propose a target localization system combining eventcamera and SNNbased highspeed target estimation and framebased camera and CNNdriven reliable object detection by fusing complementary spatiotemporal prowess of event and frame pipelines. One of our main contributions involves the design of an SNN filter that borrows from the neural mechanism for egomotion cancelation in houseflies. It fuses the vestibular sensors with the vision to cancel the activity corresponding to the predator's selfmotion. We also integrate the neuroinspired multipipeline processing with taskoptimized multineuronal pathway structure in primates andmore »

Abstract Dynamic community detection provides a coherent description of network clusters over time, allowing one to track the growth and death of communities as the network evolves. However, modularity maximization, a popular method for performing multilayer community detection, requires the specification of an appropriate null network as well as resolution and interlayer coupling parameters. Importantly, the ability of the algorithm to accurately detect community evolution is dependent on the choice of these parameters. In functional temporal networks, where evolving communities reflect changing functional relationships between network nodes, it is especially important that the detected communities reflect any state changes of the system. Here, we present analytical work suggesting that a uniform null network provides improved sensitivity to the detection of small evolving communities in temporal networks with positive edge weights bounded above by 1, such as certain types of correlation networks. We then propose a method for increasing the sensitivity of modularity maximization to state changes in nodal dynamics by modelling selfidentity links between layers based on the selfsimilarity of the network nodes between layers. This method is more appropriate for functional temporal networks from both a modelling and mathematical perspective, as it incorporates the dynamic nature of network nodes.more »