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PubMed2002

The computational modeling of analogy-making.

Our ability to see a particular object or situation in one context as being 'the same as' another object or situation in another context is the essence of analogy-making. It encompasses our ability to explain new concepts in terms of already-familiar ones, to emphasize particular aspects of situations, to generalize, to characterize, to explain or describe new phenomena, to serve as a basis for how to act in unfamiliar surroundings, and to understand many types of humor. Within this framework, the importance of analogy-making in modeling cognition becomes clear.

Journal Article↗
PubMed1993

Pulse stream VLSI circuits and systems: the EPSILON neural network chipset.

An analogue CMOS VLSI neural processing chip has been designed and fabricated. The device employs "pulse-stream" neural state signalling and is capable of computing some 360 million synaptic connections per second. In addition to basic characterisation results, the performance of the chip in solving "real-world" problems is also demonstrated. The experience gained from the development of this device has resulted in the design of a second "pulse-stream" chip with improved performance and features. It is anticipated that this second device will be integrated into a standard bus-based system and find early application in robotic control.

Computers, Analog↗
PubMed1999

Fast digital simulation of spiking neural networks and neuromorphic integration with SPIKELAB.

We present a simulation environment called SPIKELAB which incorporates a simulator that is able to simulate large networks of spiking neurons using a distributed event driven simulation. Contrary to a time driven simulation, which is usually used to simulate spiking neural networks, our simulation needs less computational resources because of the low average activity of typical networks. The paper addresses the speed up using an event driven versus a time driven simulation and how large networks can be simulated by a distribution of the simulation using already available computing resources. It also presents a solution for the integration of digital or analogue neuromorphic circuits into the simulation process.

Action Potentials↗
PubMed2004

Does computation provide a model for creativity? An epistemological perspective in neuroscience.

In 1939 Alan Turing, a major scholar in the field of mechanical computation, described a system whose computational power was beyond that of a discrete, finite state machine (Turing Machine). The composition of this system was likely the first example of what is now called an hybrid computational system. Since then, development of neural networks and brain automata has made aware that forms of computation might exist that are likely to go beyond Turing's limits. Natural systems, like the central nervous system in Mammals and man, are likely to use such a type of computation, especially to perform highly integrating activities, like feedback controls and mental creative processes. The latter are usually understood as processes that involve infinitary procedures, ending up in a complex information network, the computational maps, in which both digital, Turing-like computation and continuous, analog forms of calculus are expected to occur. Pictorial representation may be a fruitful example, mostly metaphorical, to analyze the use of this hybrid forms of computation by higher order computational maps, and the possible role of these types of computational processes in painting creativity is briefly analyzed in comparing 15th vs 16th century Renaissance Art. An open challenge for neuroscience in the 21st century is to clarify whether a hybrid neural learning network might represent a reasonable clue to scientifically interpret the theme of "creativity".

Art↗
PubMed2002

Genotype-phenotype mapping: genes as computer programs.

The effects of genes on phenotype are mediated by processes that are typically unknown but whose determination is desirable. The conversion from gene to phenotype is not a simple function of individual genes, but involves the complex interactions of many genes; it is what is known as a nonlinear mapping problem. A computational method called genetic programming allows the representation of candidate nonlinear mappings in several possible trees. To find the best model, the trees are 'evolved' by processes akin to mutation and recombination, and the trees that more closely represent the actual data are preferentially selected. The result is an improved tree of rules that represent the nonlinear mapping directly. In this way, the encoding of cellular and higher-order activities by genes is seen as directly analogous to computer programs. This analogy is of utility in biological genetics and in problems of genotype-phenotype mapping.

Biological Evolution↗
PubMed2002

Scalable hybrid computation with spikes.

We outline a hybrid analog-digital scheme for computing with three important features that enable it to scale to systems of large complexity: First, like digital computation, which uses several one-bit precise logical units to collectively compute a precise answer to a computation, the hybrid scheme uses several moderate-precision analog units to collectively compute a precise answer to a computation. Second, frequent discrete signal restoration of the analog information prevents analog noise and offset from degrading the computation. And, third, a state machine enables complex computations to be created using a sequence of elementary computations. A natural choice for implementing this hybrid scheme is one based on spikes because spike-count codes are digital, while spike-time codes are analog. We illustrate how spikes afford easy ways to implement all three components of scalable hybrid computation. First, as an important example of distributed analog computation, we show how spikes can create a distributed modular representation of an analog number by implementing digital carry interactions between spiking analog neurons. Second, we show how signal restoration may be performed by recursive spike-count quantization of spike-time codes. And, third, we use spikes from an analog dynamical system to trigger state transitions in a digital dynamical system, which reconfigures the analog dynamical system using a binary control vector; such feedback interactions between analog and digital dynamical systems create a hybrid state machine (HSM). The HSM extends and expands the concept of a digital finite-state-machine to the hybrid domain. We present experimental data from a two-neuron HSM on a chip that implements error-correcting analog-to-digital conversion with the concurrent use of spike-time and spike-count codes. We also present experimental data from silicon circuits that implement HSM-based pattern recognition using spike-time synchrony. We outline how HSMs may be used to perform learning, vector quantization, spike pattern recognition and generation, and how they may be reconfigured.

Action Potentials↗