The development of spiking neural network simulation software is a critical component enabling the modeling systems and biologically inspired algorithms. Existing frameworks support wide range functionality, abstraction levels, hardware devices, yet are typically not suitable for rapid prototyping or application to problems in domain machine learning. In this paper, we describe new Python package networks, specifically geared towards learning reinforcement Our software, called...

Neocortical structures typically only support slow acquisition of declarative memory; however, learning through fast mapping may facilitate rapid learning-induced cortical plasticity and hippocampal-independent integration novel associations into existing semantic networks. During the meaning new words concepts is inferred, durable are incidentally formed, a process thought to early childhood’s exuberant learning. The anterior temporal lobe, memory hub, critically such We investigated...

The human voice signal carries much information in addition to direct linguistic semantic information. This can be perceived by computational systems. In this work, we show that early diagnosis of Parkinson's disease is possible solely from the signal. contrast earlier work which showed done using hand-calculated features speech (such as formants) annotated professional therapists. paper, review and a differential produced directly analog itself. addition, differentiation made between seven...

We present a system comprising hybridization of self-organized map (SOM) properties with spiking neural networks (SNNs) that retain many the features SOMs. Networks are trained in an unsupervised manner to learn lattice filters via excitatory-inhibitory interactions among populations neurons. develop and test various inhibition strategies, such as growing inter-neuron distance two distinct levels inhibition. The quality learning algorithm is evaluated using examples known labels. Several...

Living systems face both environmental complexity and limited access to free-energy resources. Survival under these conditions requires a control system that can activate, or deploy, available perception action resources in context specific way. In this Part I, we introduce the principle (FEP) idea of active inference as Bayesian prediction-error minimization, show how problem arises systems. We then review classical quantum formulations FEP, with former being limit latter. accompanying II,...

Using two distinct data sets (from the USA and Germany) of healthy controls patients with early or mild stages Parkinson's disease, we show that machine learning tools can be used for diagnosis disease from speech data. This could potentially applicable before physical symptoms appear. In addition, while training phase process one country reused in other; different features dominate each country; presumably because languages differences. Three results are presented: (i) separate testing by...

Living systems face both environmental complexity and limited access to free-energy resources. Survival under these conditions requires a control system that can activate, or deploy, available perception action resources in context specific way. In Part I, we introduced the principle (FEP) idea of active inference as Bayesian prediction-error minimization, show how problem arises systems. We then review classical quantum formulations FEP, with former being limit latter. this accompanying II,...

Excitability—a threshold-governed transient in transmembrane voltage—is a fundamental physiological process that controls the function of heart, endocrine, muscles, and neuronal tissues. The 1950s Hodgkin Huxley explicit formulation provides mathematical framework for understanding excitability, as consequence properties voltage-gated sodium potassium channels. Hodgkin–Huxley model is more sensitive to parametric variations protein densities kinetics than biological systems whose...

Living systems face both environmental complexity and limited access to free-energy resources. Survival under these conditions requires a control system that can activate, or deploy, available perception action resources in context specific way. We show here when are described as executing active inference driven by the principle (and hence be considered Bayesian prediction-error minimizers), their flow always represented tensor networks (TNs). how TNs implmented within general framework of...

Artificial neural networks (ANNs) have been successfully trained to perform a wide range of sensory-motor behaviors. In contrast, the performance spiking neuronal network (SNN) models similar behaviors remains relatively suboptimal. this work, we aimed push field SNNs forward by exploring potential different learning mechanisms achieve optimal performance. We solve CartPole reinforcement (RL) control problem using two operating at timescales: (1) spike-timing-dependent (STDP-RL) and (2)...

Whole-genome sequencing has revealed that TP53, NOTCH1, ATM, SF3B1, BIRC3, ABL, NXF1, BCR, and ZAP70 are often mutated in CLL, but not consistently across all CLL patients. This paper employs a statistical thermodynamics approach combination with the systems biology of protein–protein interaction networks to identify most significant participant proteins cancerous transformation. Betti number (a topology complexity) estimates highlight protein hierarchy, primarily Wnt pathway known for...

Recent models of spiking neuronal networks have been trained to perform behaviors in static environments using a variety learning rules, with varying degrees biological realism. Most these not tested dynamic visual where must make predictions on future states and adjust their behavior accordingly. The rules are often treated as black boxes, little analysis circuit architectures mechanisms supporting optimal performance. Here we developed visual/motor network them play virtual racket-ball...

We show that real valued continuous functions can be recognized in a reliable way, with good generalization ability using an adapted version of the Liquid State Machine (LSM) receives direct input. Furthermore this system works without necessity preliminary extraction signal processing features. This avoids discretization and encoding has plagued earlier attempts on process. is effective simulated designed to have properties physical trace human speech. The main changes basic liquid state...

There is growing need for multichannel electrophysiological systems that record from and interact with neuronal in near real-time. Such are needed, example, closed loop, / optogenetic experimentation vivo a variety of other preparations, or developing testing neuro-prosthetic devices, to name few. Furthermore, there such be inexpensive, reliable, user friendly, easy set-up, open expandable, possess long life cycles face rapidly changing computing environments. Finally, they should provide...

A common view in the neuroscience community is that memory encoded connection strength between neurons. This perception led artificial neural network models to focus on weights as key variables modulate learning. In this paper, we present a prototype for weightless spiking networks can perform simple classification task. The stored timing neurons, rather than of connection, and trained using Hebbian Spike Timing Dependent Plasticity (STDP), which modulates delays connection.

A novel fMRI classification method designed for rapid event related experiments is described and applied to the of loud reading isolated words in Hebrew. Three comparisons different grammatical complexity were performed: (i) versus asterisks (ii) "with diacritics without diacritics" (iii) root no root". We discuss most difficult task and, comparison, easiest one. Earlier work using more standard techniques (machine learning statistical) succeeded fully only simplest these tasks (i), but...

This experiment was designed to see if information related linguistic characteristics of read text can be deduced from fMRI data via machine learning techniques. Individuals were scanned while reading the size words in loud reading. Three experiments performed corresponding different degrees grammatical complexity that is during reading: (1) and pseudo-words presented subjects; (2) with diacritical marking without markings (3) Hebrew root subjects. The working hypothesis more complex needed...

This work proposes a model-free approach to fMRI-based brain mapping where the BOLD response is learnt from data rather than assumed in advance. For each voxel, paired sequence of stimuli and fMRI recording given supervised learning process. The result voxel-wise model expected related set stimuli. Differently standard techniques, voxel relevance assessed by fitting an hemodynamic function, we argue that relevant voxels can be filtered according prediction accuracy model. In this present...