Klaus M. Stiefel

An FPGA Implementation of a Polychronous Spiking Neural Network with Delay Adaptation

We present an FPGA implementation of a re-configurable, polychronous spiking neural network with a large capacity for spatial-temporal patterns. The proposed neural network generates delay paths de novo, so that only connections that actually appear in the training patterns will be created. This allows the proposed network to use all the axons (variables) to store information. Spike Timing Dependent Delay Plasticity is used to fine-tune and add dynamics to the network. We use a time multiplexing approach allowing us to achieve 4096 (4k) neurons and up to 1.15 million programmable delay axons on a Virtex 6 FPGA. Test results show that the proposed neural network is capable of successfully recalling more than 95% of all spikes for 96% of the stored patterns. The tests also show that the neural network is robust to noise from random input spikes.

Synthesis of neural networks for spatio-temporal spike pattern recognition and processing

The advent of large scale neural computational platforms has highlighted the lack of algorithms for synthesis of neural structures to perform predefined cognitive tasks. The Neural Engineering Framework (NEF) offers one such synthesis, but it is most effective for a spike rate representation of neural information, and it requires a large number of neurons to implement simple functions. We describe a neural network synthesis method that generates synaptic connectivity for neurons which process time-encoded neural signals, and which makes very sparse use of neurons. The method allows the user to specify—arbitrarily—neuronal characteristics such as axonal and dendritic delays, and synaptic transfer functions, and then solves for the optimal input-output relationship using computed dendritic weights. The method may be used for batch or online learning and has an extremely fast optimization process. We demonstrate its use in generating a network to recognize speech which is sparsely encoded as spike times.

The claustrum’s proposed role in consciousness is supported by the effect and target localization of Salvia divinorum

This article brings together three findings and ideas relevant for the understanding of human consciousness: (I) Crick’s and Koch’s theory that the claustrum is a “conductor of consciousness” crucial for subjective conscious experience. (II) Subjective reports of the consciousness-altering effects the plant Salvia divinorum, whose primary active ingredient is salvinorin A, a κ-opioid receptor agonist. (III) The high density of κ-opioid receptors in the claustrum. Fact III suggests that the consciousness-altering effects of S. divinorum/salvinorin A (II) are due to a κ-opioid receptor mediated inhibition of primarily the claustrum and, additionally, the deep layers of the cortex, mainly in prefrontal areas. Consistent with Crick and Koch’s theory that the claustrum plays a key role in consciousness (I), the subjective effects of S. divinorum indicate that salvia disrupts certain facets of consciousness much more than the largely serotonergic hallucinogen lysergic acid diethylamide (LSD). Based on this data and on the relevant literature, we suggest that the claustrum does indeed serve as a conductor for certain aspects of higher-order integration of brain activity, while integration of auditory and visual signals relies more on coordination by other areas including parietal cortex and the pulvinar.

Proposed evolutionary changes in the role of myelin

Myelin is the multi-layered lipid sheet periodically wrapped around neuronal axons. It is most frequently found in vertebrates. Myelin allows for saltatory action potential (AP) conduction along axons. During this form of conduction, the AP travels passively along the myelin-covered part of the axon, and is recharged at the intermittent nodes of Ranvier. Thus, myelin can reduce the energy load needed and/or increase the speed of AP conduction. Myelin first evolved during the Ordovician period. We hypothesize that myelins first role was mainly energy conservation. During the later “Mesozoic marine revolution,” marine ecosystems changed toward an increase in marine predation pressure. We hypothesize that the main purpose of myelin changed from energy conservation to conduction speed increase during this Mesozoic marine revolution. To test this hypothesis, we optimized models of myelinated axons for a combination of AP conduction velocity and energy efficiency. We demonstrate that there is a trade-off between these objectives. We then compared the simulation results to empirical data and conclude that while the data are consistent with the theory, additional measurements are necessary for a complete evaluation of the proposed hypothesis.

The Green's function formalism as a bridge between single- and multi-compartmental modeling

Neurons are spatially extended structures that receive and process inputs on their dendrites. It is generally accepted that neuronal computations arise from the active integration of synaptic inputs along a dendrite between the input location and the location of spike generation in the axon initial segment. However, many application such as simulations of brain networks use point-neurons—neurons without a morphological component—as computational units to keep the conceptual complexity and computational costs low. Inevitably, these applications thus omit a fundamental property of neuronal computation. In this work, we present an approach to model an artificial synapse that mimics dendritic processing without the need to explicitly simulate dendritic dynamics. The model synapse employs an analytic solution for the cable equation to compute the neuron’s membrane potential following dendritic inputs. Green’s function formalism is used to derive the closed version of the cable equation. We show that by using this synapse model, point-neurons can achieve results that were previously limited to the realms of multi-compartmental models. Moreover, a computational advantage is achieved when only a small number of simulated synapses impinge on a morphologically elaborate neuron. Opportunities and limitations are discussed.

Temporal order detection and coding in nervous systems

This letter discusses temporal order coding and detection in nervous systems. Detection of temporal order in the external world is an adaptive function of nervous systems. In addition, coding based on the temporal order of signals can be used as an internal code. Such temporal order coding is a subset of temporal coding. We discuss two examples of processing the temporal order of external events: the auditory location detection system in birds and the visual direction detection system in flies. We then discuss how somatosensory stimulus intensities are translated into a temporal order code in the human peripheral nervous system. We next turn our attention to input order coding in the mammalian cortex. We review work demonstrating the capabilities of cortical neurons for detecting input order. We then discuss research refuting and demonstrating the representation of stimulus features in the cortex by means of input order. After some general theoretical considerations on input order detection and coding, we conclude by discussing the existing and potential use of input order coding in neuromorphic engineering.

Physiological Dynamics in Demyelinating Diseases: Unraveling Complex Relationships through Computer Modeling

Despite intense research, few treatments are available for most neurological disorders. Demyelinating diseases are no exception. This is perhaps not surprising considering the multifactorial nature of these diseases, which involve complex interactions between immune system cells, glia and neurons. In the case of multiple sclerosis, for example, there is no unanimity among researchers about the cause or even which system or cell type could be ground zero. This situation precludes the development and strategic application of mechanism-based therapies. We will discuss how computational modeling applied to questions at different biological levels can help link together disparate observations and decipher complex mechanisms whose solutions are not amenable to simple reductionism. By making testable predictions and revealing critical gaps in existing knowledge, such models can help direct research and will provide a rigorous framework in which to integrate new data as they are collected. Nowadays, there is no shortage of data; the challenge is to make sense of it all. In that respect, computational modeling is an invaluable tool that could, ultimately, transform how we understand, diagnose, and treat demyelinating diseases.

Tool use by four species of Indo-Pacific sea urchins

We compared the covering behavior of four sea urchin species, Tripneustes gratilla, Pseudoboletia maculata, Toxopneutes pileolus, and Salmacis sphaeroides found in the waters of Malapascua Island, Cebu Province and Bolinao, Panagsinan Province, Philippines. Specifically, we measured the amount and type of covering material on each urchin, and, in several cases, the recovery of debris cover after stripping the animal of its cover. We found that Tripneustes gratilla and Salmacis sphaeroides have a higher preference for plant material, especially sea-grass, compared to Pseudoboletia maculata and Toxopneutes pileolus, which prefer to cover themselves with coral rubble and other calcified material. Only for Toxopneutes pileolus did we find a decrease in cover with depth, confirming previous work that the covering behavior serves UV protection. We found no dependence of particle size on either species or urchin size, but we observed that larger urchins carried more and heavier debris. We observed a transport mechanism of debris onto the echinoid body surface utilizing a combination of tube feet and spines. The transport speed of individual debris items varied between species. We compare our results to previous studies of urchin covering behavior, comment on the phylogeny of urchin covering behavior and discuss the interpretation of this behavior as animal tool use.

Neurocomputation and Coding in the Claustrum: Comparisons with the Pulvinar

We first outline one of the most pressing problems in the psychology of perception, the binding problem. This term describes the conundrum that different features associated with an object (color, shape, motion direction, sound, etc.) are encoded in different brain regions, while the object is nevertheless perceived as one. We outline several coding strategies which could solve the binding problem, namely grandmother neurons, self-organizing assemblies, and conducted assemblies. n nWe then make a case for the claustrum as a structure conducting assemblies, based on anatomical, electrophysiological, psychological and neuropharmacological results. We contrast the properties of the claustrum with other brain structures and propose that the pulvinar is also an integration center, one particularly important for sensory binding. The claustrum appears to be involved in higher-level binding functions. We conclude by suggesting future directions for elucidating the computational role of the claustrum.We first outline one of the most pressing problems in the psychology of perception, the binding problem. This term describes the conundrum that different features associated with an object (color, shape, motion direction, sound, etc.) are encoded in different brain regions, while the object is nevertheless perceived as one. We outline several coding strategies which could solve the binding problem, namely grandmother neurons, self-organizing assemblies, and conducted assemblies. We then make a case for the claustrum as a structure conducting assemblies, based on anatomical, electrophysiological, psychological and neuropharmacological results. We contrast the properties of the claustrum with other brain structures and propose that the pulvinar is also an integration center, one particularly important for sensory binding. The claustrum appears to be involved in higher-level binding functions. We conclude by suggesting future directions for elucidating the computational role of the claustrum.

Fish Diversity as a Function of Depth and Body Size

We analyze the number of marine fish species as a function of fish body size and occurrence depth. For this purpose, we analyze the FishBase database. We compare these data to predictions of fish species numbers derived from the neutral theory of biodiversity combined with well-established ecological scaling laws, and measured oceanic biomass data. We consider several variants of these scaling laws, and we find that more large fish species exist compared to the prediction, which is especially true for elasmobranchs, possibly due to their overwhelmingly predatory niches. We find species numbers decreasing with occurrence depth somewhat quicker than our predictions based on the decrease of the number of individuals with depth indicates. This is especially true for the elasmobranchs. This is unsurprising, since the individuals versus depth data did not specifically determine elasmobranch biomass, and since sharks are known to be limited to depths < 3,000 m. Finally, we discuss how a reduced rate of speciation in larger animals could explain why large species are rare, in spite of the advantages of large body sizes outlined in Cope’s rule.