A framework for plasticity implementation on the SpiNNaker neural architecture.

Many of the precise biological mechanisms of synaptic plasticity remain elusive, but simulations of neural networks have greatly enhanced our understanding of how specific global functions arise from the massively parallel computation of neurons and local Hebbian or spike-timing dependent plasticity rules. For simulating large portions of neural tissue, this has created an increasingly strong need for large scale simulations of plastic neural networks on special purpose hardware platforms, because synaptic transmissions and updates are badly matched to computing style supported by current architectures. Because of the great diversity of biological plasticity phenomena and the corresponding diversity of models, there is a great need for testing various hypotheses about plasticity before committing to one hardware implementation.

Neuromorphic log-domain silicon synapse circuits obey bernoulli dynamics: a unifying tutorial analysis.

The field of neuromorphic silicon synapse circuits is revisited and a parsimonious mathematical framework able to describe the dynamics of this class of log-domain circuits in the aggregate and in a systematic manner is proposed. Starting from the Bernoulli Cell Formalism (BCF), originally formulated for the modular synthesis and analysis of externally linear, time-invariant logarithmic filters, and by means of the identification of new types of Bernoulli Cell (BC) operators presented here, a generalized formalism (GBCF) is established. The expanded formalism covers two new possible and practical combinations of a MOS transistor (MOST) and a linear capacitor.

Uncertainty in perception and the Hierarchical Gaussian Filter.

In its full sense, perception rests on an agent's model of how its sensory input comes about and the inferences it draws based on this model. These inferences are necessarily uncertain. Here, we illustrate how the Hierarchical Gaussian Filter (HGF) offers a principled and generic way to deal with the several forms that uncertainty in perception takes.

Early somatosensory processing in individuals at risk for developing psychoses.

Human cortical somatosensory evoked potentials (SEPs) allow an accurate investigation of thalamocortical and early cortical processing. SEPs reveal a burst of superimposed early (N20) high-frequency oscillations around 600 Hz. Previous studies reported alterations of SEPs in patients with schizophrenia.

Regional Fluid-Attenuated Inversion Recovery (FLAIR) at 7 Tesla correlates with amyloid beta in hippocampus and brainstem of cognitively normal elderly subjects.

Background: Accumulation of amyloid beta (Aβ) may occur during healthy aging and is a risk factor for Alzheimer Disease (AD). While individual Aβ-accumulation can be measured non-invasively using Pittsburgh Compund-B positron emission tomography (PiB-PET), Fluid-attenuated inversion recovery (FLAIR) is a Magnetic Resonance Imaging (MRI) sequence, capable of indicating heterogeneous age-related brain pathologies associated with tissue-edema. In the current study cognitively normal elderly subjects were investigated for regional correlation of PiB- and FLAIR intensity.

PyNCS: a microkernel for high-level definition and configuration of neuromorphic electronic systems.

Neuromorphic hardware offers an electronic substrate for the realization of asynchronous event-based sensory-motor systems and large-scale spiking neural network architectures. In order to characterize these systems, configure them, and carry out modeling experiments, it is often necessary to interface them to workstations. The software used for this purpose typically consists of a large monolithic block of code which is highly specific to the hardware setup used.

Configurable analog-digital conversion using the neural engineering framework.

Efficient Analog-Digital Converters (ADC) are one of the mainstays of mixed-signal integrated circuit design. Besides the conventional ADCs used in mainstream ICs, there have been various attempts in the past to utilize neuromorphic networks to accomplish an efficient crossing between analog and digital domains, i.e.

Learning and stabilization of winner-take-all dynamics through interacting excitatory and inhibitory plasticity.

Winner-Take-All (WTA) networks are recurrently connected populations of excitatory and inhibitory neurons that represent promising candidate microcircuits for implementing cortical computation. WTAs can perform powerful computations, ranging from signal-restoration to state-dependent processing. However, such networks require fine-tuned connectivity parameters to keep the network dynamics within stable operating regimes.

Individual differences in bodily freezing predict emotional biases in decision making.

Instrumental decision making has long been argued to be vulnerable to emotional responses. Literature on multiple decision making systems suggests that this emotional biasing might reflect effects of a system that regulates innately specified, evolutionarily preprogrammed responses. To test this hypothesis directly, we investigated whether effects of emotional faces on instrumental action can be predicted by effects of emotional faces on bodily freezing, an innately specified response to aversive relative to appetitive cues.

Sequential activity in asymmetrically coupled winner-take-all circuits.

Understanding the sequence generation and learning mechanisms used by recurrent neural networks in the nervous system is an important problem that has been studied extensively. However, most of the models proposed in the literature are either not compatible with neuroanatomy and neurophysiology experimental findings, or are not robust to noise and rely on fine tuning of the parameters. In this work, we propose a novel model of sequence learning and generation that is based on the interactions among multiple asymmetrically coupled winner-take-all (WTA) circuits.

Imaging of cerebrovascular pathology in animal models of Alzheimer's disease.

In Alzheimer's disease (AD), vascular pathology may interact with neurodegeneration and thus aggravate cognitive decline. As the relationship between these two processes is poorly understood, research has been increasingly focused on understanding the link between cerebrovascular alterations and AD. This has at last been spurred by the engineering of transgenic animals, which display pathological features of AD and develop cerebral amyloid angiopathy to various degrees.

Broad-band Gaussian noise is most effective in improving motor performance and is most pleasant.

Modern attempts to improve human performance focus on stochastic resonance (SR). SR is a phenomenon in non-linear systems characterized by a response increase of the system induced by a particular level of input noise. Recently, we reported that an optimum level of 0-15 Hz Gaussian noise applied to the human index finger improved static isometric force compensation.

Biologically-inspired adaptive obstacle negotiation behavior of hexapod robots.

Neurobiological studies have shown that insects are able to adapt leg movements and posture for obstacle negotiation in changing environments. Moreover, the distance to an obstacle where an insect begins to climb is found to be a major parameter for successful obstacle negotiation. Inspired by these findings, we present an adaptive neural control mechanism for obstacle negotiation behavior in hexapod robots.

A robust sound perception model suitable for neuromorphic implementation.

We have recently demonstrated the emergence of dynamic feature sensitivity through exposure to formative stimuli in a real-time neuromorphic system implementing a hybrid analog/digital network of spiking neurons. This network, inspired by models of auditory processing in mammals, includes several mutually connected layers with distance-dependent transmission delays and learning in the form of spike timing dependent plasticity, which effects stimulus-driven changes in the network connectivity. Here we present results that demonstrate that the network is robust to a range of variations in the stimulus pattern, such as are found in naturalistic stimuli and neural responses.

Could magnetic resonance provide in vivo histology?

THE DIAGNOSIS OF A SUSPECTED TUMOR LESION FACES TWO BASIC PROBLEMS: detection and identification of the specific type of tumor. Radiological techniques are commonly used for the detection and localization of solid tumors. Prerequisite is a high intrinsic or enhanced contrast between normal and neoplastic tissue.

Inference of neuronal network spike dynamics and topology from calcium imaging data.

Two-photon calcium imaging enables functional analysis of neuronal circuits by inferring action potential (AP) occurrence ("spike trains") from cellular fluorescence signals. It remains unclear how experimental parameters such as signal-to-noise ratio (SNR) and acquisition rate affect spike inference and whether additional information about network structure can be extracted. Here we present a simulation framework for quantitatively assessing how well spike dynamics and network topology can be inferred from noisy calcium imaging data.

Electroencephalogram approximate entropy influenced by both age and sleep.

The use of information-based measures to assess changes in conscious state is an increasingly popular topic. Though recent results have seemed to justify the merits of such methods, little has been done to investigate the applicability of such measures to children. For our work, we used the approximate entropy (ApEn), a measure previously shown to correlate with changes in conscious state when applied to the electroencephalogram (EEG), and sought to confirm whether previously reported trends in adult ApEn values across wake and sleep were present in children.

Real-time classification and sensor fusion with a spiking deep belief network.

Deep Belief Networks (DBNs) have recently shown impressive performance on a broad range of classification problems. Their generative properties allow better understanding of the performance, and provide a simpler solution for sensor fusion tasks. However, because of their inherent need for feedback and parallel update of large numbers of units, DBNs are expensive to implement on serial computers.

Maturation and integration of adult born hippocampal neurons: signal convergence onto small Rho GTPases.

Adult neurogenesis, restricted to specific regions in the mammalian brain, represents one of the most interesting forms of plasticity in the mature nervous system. Adult-born hippocampal neurons play important roles in certain forms of learning and memory, and altered hippocampal neurogenesis has been associated with a number of neuropsychiatric diseases such as major depression and epilepsy. Newborn neurons go through distinct developmental steps, from a dividing neurogenic precursor to a synaptically integrated mature neuron.

A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models.

Mirror neurons are neurons whose responses to the observation of a motor act resemble responses measured during production of that act. Computationally, mirror neurons have been viewed as evidence for the existence of internal inverse models. Such models, rooted within control theory, map-desired sensory targets onto the motor commands required to generate those targets.