A neuronal device for the control of multi-step computations

Connection Science, 2012

Recent research shows that some brain areas perform more than one task and the switching times between them are incompatible with learning and that parts of the brain are controlled by other parts of the brain, or are “recycled”, or are used and reused for various purposes by other neural circuits in different task categories and cognitive domains. All this is conducive to the notion of “programming in the brain”. In this paper, we describe a programmable neural architecture, biologically plausible on the neural level, and we implement, test, and validate it in order to support the programming interpretation of the above-mentioned phenomenology. A programmable neural network is a fixed-weight network that is endowed with auxiliary or programming inputs and behaves as any of a specified class of neural networks when its programming inputs are fed with a code of the weight matrix of a network of the class. The construction is based on the “pulling out” of the multiplication between synaptic weights and neuron outputs and having it performed in “software” by specialised multiplicative-response fixed subnetworks. Such construction has been tested for robustness with respect to various sources of noise. Theoretical underpinnings, analysis of related research, detailed construction schemes, and extensive testing results are given.

2008

Abstract—The latest advances in the gridbrain agent brain model are presented. The gridbrain models brains as networks of computational components. The components are used as building blocks for computation, and provide base functionalities like: input/output, boolean logic, arithmetic, clocks and memory. The multi-grid architecture as a way to process variable sized information from different sensory channels is addressed.

Brain-inspired computing structures,technologies, and methods offer innovative approaches to the future of computing. From the lowest level of neuron devices to the highest abstraction of consciousness, the brain drives new ideas (literally and conceptually) in computer design and operation. This paper interrelates three levels of brain inspired abstractions including intelligence, abstract graph data structures, and neuron operation and interconnection. An abstract machine architecture is presented from which a lower bound on resource requirements for intelligence is to be derived. At the lowest level a new use of cellular automata architecture is discussed that mimics the fine-grain locality of action and high degree interconnectivity of neurons and their structures. Graph structures serve as a brain inspired intermediary abstraction between these two as the neocortex is organized as a directed graph. This paper shows how all of the pieces tie together and opens a new way of considering future computing structures through brain inspired concepts.

2012

DRAFT – Please do not quote or share without permission We argue that neural processes are neither analog nor digital computations; they constitute a third kind of computation. Analog computation is the processing of continuous signals; digital computation is the processing of strings of digits. But current neuroscientific evidence indicates that typical neural signals, such as spike rates, are graded like continuous signals but are constituted by discrete elements (spikes); thus typical neural signals are neither continuous signals nor strings of digits. It follows that neural computation is sui generis. This has three important consequences. First, understanding neural computation requires a specially designed mathematical theory (or theories) rather than the mathematical theories of analog or digital computation. Second, several popular views about neural computation turn out to be incorrect. Third, computational theories of cognition that rely on non-neural notions of computatio...

Papers in Physics, 2000

We describe the operation of a neuronal device which embodies the computational principles of the "paper-and-pencil" machine envisioned by Alan Turing. The network is based on principles of cortical organization. We develop a plausible solution to implement pointers and investigate how neuronal circuits may instantiate the basic operations involved in assigning a value to a variable (i.e., x = 5), in determining whether two variables have the same value and in retrieving the value of a given variable to be accessible to other nodes of the network. We exemplify the collective function of the network in simplified arithmetic and problem solving (blocks-world) tasks.

Journal of Intelligent & …, 2002

A novel way to simulate Turing Machines (TMs) by Artificial Neural Networks (ANNs) is proposed. We claim that the proposed simulation is in agreement with the correct interpretation of Turing's analysis of computation; compatible with the current approaches to analyze cognition as an interactive agent-environment process; and physically realizable since it does not use connection weights with unbounded precision. A full description of an implementation of a universal TM into a recurrent sigmoid ANN focusing on the TM finite state control is given, leaving the tape, an infinite resource, as an external non-intrinsic feature. Also, motivated by the results on the limit of what can actually be computed by ANNs when noise is taken into account, we introduce the notion of Definite Turing Machine and investigate some of its properties.

2018

This paper presents the self-organized neuromorphic architecture named SOMA. The objective is to study neural-based self-organization in computing systems and to prove the feasibility of a self-organizing hardware structure. Considering that these properties emerge from large scale and fully connected neural maps, we will focus on the definition of a self-organizing hardware architecture based on digital spiking neurons that offer hardware efficiency. From a biological point of view, this corresponds to a combination of the so-called synaptic and structural plasticities. We intend to define computational models able to simultaneously self-organize at both computation and communication levels, and we want these models to be hardware-compliant, fault tolerant and scalable by means of a neuro-cellular structure.

Proceedings of the International Conference on Neuromorphic Systems 2022

Neuromorphic computing is a non-von Neumann computing paradigm that performs computation by emulating the human brain. Neuromorphic systems are extremely energy-efficient and known to consume thousands of times less power than CPUs and GPUs. They have the potential to drive critical use cases such as autonomous vehicles, edge computing and internet of things in the future. For this reason, they are sought to be an indispensable part of the future computing landscape. Neuromorphic systems are mainly used for spike-based machine learning applications, although there are some non-machine learning applications in graph theory, differential equations, and spike-based simulations. These applications suggest that neuromorphic computing might be capable of generalpurpose computing. However, general-purpose computability of neuromorphic computing has not been established yet. In this work, we prove that neuromorphic computing is Turing-complete and therefore capable of general-purpose computing. Specifically, we present a model of neuromorphic computing, with just two neuron parameters (threshold and leak), and two synaptic parameters (weight and delay). We devise neuromorphic circuits for computing all the µ-recursive functions (i.e., constant, successor and projection functions) and all the µ-recursive operators (i.e., composition, primitive recursion and minimization operators). Given that the µ-recursive functions and operators are precisely the ones that can be computed using a Turing machine, this work establishes the Turing-completeness of neuromorphic computing.

Proceedings of the National Academy of Sciences, 2013

The quest to implement intelligent processing in electronic neuromorphic systems lacks methods for achieving reliable behavioral dynamics on substrates of inherently imprecise and noisy neurons.

Minds and Machines, 2003

HAVA T. SIEGELMANN University of Massachusetts at Amherst, Department of Computer Science, Amherst, MA 01003, USA; E-mail: hava@cs.umass.edu Abstract. “Neural computing” is a research field based on perceiving the human brain as an in-formation ...