The connectivity degree controls the difficulty in reservoir design of random boolean networks.

Reservoir Computing (RC) is a paradigm in artificial intelligence where a recurrent neural network (RNN) is used to process temporal data, leveraging the inherent dynamical properties of the reservoir to perform complex computations. In the realm of RC, the excitatory-inhibitory balance has been shown to be pivotal for driving the dynamics and performance of Echo State Networks (ESN) and, more recently, Random Boolean Network (RBN). However, the relationship between and other parameters of the network is still poorly understood.

Structural plasticity for neuromorphic networks with electropolymerized dendritic PEDOT connections.

Neural networks are powerful tools for solving complex problems, but finding the right network topology for a given task remains an open question. Biology uses neurogenesis and structural plasticity to solve this problem. Advanced neural network algorithms are mostly relying on synaptic plasticity and learning.

Excitatory/inhibitory balance emerges as a key factor for RBN performance, overriding attractor dynamics.

Reservoir computing provides a time and cost-efficient alternative to traditional learning methods. Critical regimes, known as the "edge of chaos," have been found to optimize computational performance in binary neural networks. However, little attention has been devoted to studying reservoir-to-reservoir variability when investigating the link between connectivity, dynamics, and performance.

Learning to see via epiretinal implant stimulationwith model-based deep reinforcement learning.

Objective: Diseases such as age-related macular degeneration and retinitis pigmentosa cause the degradation of the photoreceptor layer. One approach to restore vision is to electrically stimulate the surviving retinal ganglion cells with a microelectrode array such as epiretinal implants. Epiretinal implants are known to generate visible anisotropic shapes elongated along the axon fascicles of neighboring retinal ganglion cells.

Voltage-dependent synaptic plasticity: Unsupervised probabilistic Hebbian plasticity rule based on neurons membrane potential.

This study proposes voltage-dependent-synaptic plasticity (VDSP), a novel brain-inspired unsupervised local learning rule for the online implementation of Hebb's plasticity mechanism on neuromorphic hardware. The proposed VDSP learning rule updates the synaptic conductance on the spike of the postsynaptic neuron only, which reduces by a factor of two the number of updates with respect to standard spike timing dependent plasticity (STDP). This update is dependent on the membrane potential of the presynaptic neuron, which is readily available as part of neuron implementation and hence does not require additional memory for storage.

NAAQA: A Neural Architecture for Acoustic Question Answering.

The goal of the Acoustic Question Answering (AQA) task is to answer a free-form text question about the content of an acoustic scene. It was inspired by the Visual Question Answering (VQA) task. In this paper, based on the previously introduced CLEAR dataset, we propose a new benchmark for AQA, namely CLEAR2, that emphasizes the specific challenges of acoustic inputs.

A frequency-band coupling model of EEG signals can capture features from an input audio stimulus.

Despite decades of research, the features of an input audio stimulus that are encoded in an electroencephalogram (EEG) are still not clearly identified. We wish to investigate whether a frequency-band coupling model that estimates the cortical neural activity from EEGs can capture the important features of an input audio stimulus. To do so, EEG recordings were acquired from 8 subjects during a listening task where the vowels a, i and u were randomly presented.

Sound Induces Change in Orientation Preference of V1 Neurons: Audio-Visual Cross-Influence.

In the cortex, demarcated unimodal sensory regions often respond to unforeseen sensory stimuli and exhibit plasticity. The goal of the current investigation was to test evoked responses of primary visual cortex (V1) neurons when an adapting auditory stimulus is applied in isolation. Using extracellular recordings in anesthetized cats, we demonstrate that, unlike the prevailing observation of only slight modulations in the firing rates of the neurons, sound imposition in isolation entirely shifted the peaks of orientation tuning curves of neurons in both supra- and infragranular layers of V1.

Comparative effects of adaptation on layers II-III and V-VI neurons in cat V1.

V1 is fundamentally grouped into columns that descend from layers II-III to V-VI. Neurons inherent to visual cortex are capable of adapting to changes in the incoming stimuli that drive the cortical plasticity. A principle feature called orientation selectivity can be altered by the presentation of non-optimal stimulus called 'adapter'.

Interplay of orientation selectivity and the power of low- and high-gamma bands in the cat primary visual cortex.

Gamma oscillations are ubiquitous in brain and are believed to be inevitable for information processing in brain. Here, we report that distinct bands (low, 30-40Hz and high gamma, 60-80Hz) of stimulus-triggered gamma oscillations are systematically linked to the orientation selectivity index (OSI) of neurons in the cat primary visual cortex. The gamma-power is high for the highly selective neurons in the low-gamma band, whereas it is high for the broadly selective neurons in the high-gamma band.

Improving neural decoding in the central auditory system using bio-inspired spectro-temporal representations and a generalized bilinear model.

We study the impact of different encoding models and spectro-temporal representations on the accuracy of Bayesian decoding of neural activity recorded from the central auditory system. Two encoding models, a generalized linear model (GLM) and a generalized bilinear model (GBM), are compared along with three different spectro-temporal representations of the input stimuli: a spectrogram and two bio-inspired representations, i.e.

High noise correlation between the functionally connected neurons in emergent V1 microcircuits.

Neural correlations (noise correlations and cross-correlograms) are widely studied to infer functional connectivity between neurons. High noise correlations between neurons have been reported to increase the encoding accuracy of a neuronal population; however, low noise correlations have also been documented to play a critical role in cortical microcircuits. Therefore, the role of noise correlations in neural encoding is highly debated.

Network-selectivity and stimulus-discrimination in the primary visual cortex: cell-assembly dynamics.

Visual neurons coordinate their responses in relation to the stimulus; however, the complex interplay between a stimulus and the functional dynamics of an assembly still eludes neuroscientists. To this aim, we recorded cell assemblies from multi-electrodes in the primary visual cortex of anaesthetized cats in response to randomly presented sine-wave drifting gratings whose orientation tilted in 22.5° steps.

Summation of connectivity strengths in the visual cortex reveals stability of neuronal microcircuits after plasticity.

Background: Within sensory systems, neurons are continuously affected by environmental stimulation. Recently, we showed that, on cell-pair basis, visual adaptation modulates the connectivity strength between similarly tuned neurons to orientation and we suggested that, on a larger scale, the connectivity strength between neurons forming sub-networks could be maintained after adaptation-induced-plasticity. In the present paper, based on the summation of the connectivity strengths, we sought to examine how, within cell-assemblies, functional connectivity is regulated during an exposure-based adaptation.

Electrophysiological and firing properties of neurons: Categorizing soloists and choristers in primary visual cortex.

Visual processing in the cortex involves various aspects of neuronal properties such as morphological, electrophysiological and molecular. In particular, the neural firing pattern is an important indicator of dynamic circuitry within a neuronal population. Indeed, in microcircuits, neurons act as soloists or choristers wherein the characteristical activity of a 'soloist' differs from the firing pattern of a 'chorister'.

Stimulus-dependent augmented gamma oscillatory activity between the functionally connected cortical neurons in the primary visual cortex.

Neuronal assemblies typically synchronise within the gamma oscillatory band (30-80 Hz) and are fundamental to information processing. Despite numerous investigations, the exact mechanisms and origins of gamma oscillations are yet to be known. Here, through multiunit recordings in the primary visual cortex of cats, we show that the strength of gamma power (20-40 and 60-80 Hz) is significantly stronger between the functionally connected units than between the unconnected units within an assembly.

Reprogramming of orientation columns in visual cortex: a domino effect.

Cortical organization rests upon the fundamental principle that neurons sharing similar properties are co-located. In the visual cortex, neurons are organized into orientation columns. In a column, most neurons respond optimally to the same axis of an oriented edge, that is, the preferred orientation.

Audiovisual correspondence between musical timbre and visual shapes.

This article investigates the cross-modal correspondences between musical timbre and shapes. Previously, such features as pitch, loudness, light intensity, visual size, and color characteristics have mostly been used in studies of audio-visual correspondences. Moreover, in most studies, simple stimuli e.

Event management for large scale event-driven digital hardware spiking neural networks.

The interest in brain-like computation has led to the design of a plethora of innovative neuromorphic systems. Individually, spiking neural networks (SNNs), event-driven simulation and digital hardware neuromorphic systems get a lot of attention. Despite the popularity of event-driven SNNs in software, very few digital hardware architectures are found.

Brain at work: time, sparseness and superposition principles.

Many studies explored mechanisms through which the brain encodes sensory inputs allowing a coherent behavior. The brain could identify stimuli via a hierarchical stream of activity leading to a cardinal neuron responsive to one particular object. The opportunity to record from numerous neurons offered investigators the capability of examining simultaneously the functioning of many cells.

Adaptation-induced plasticity and spike waveforms in cat visual cortex.

Orientation-selective neurons shift their preferred orientation after being adapted to a nonpreferred orientation. These shifts of the peaks of tuning curves may be in the attractive or repulsive direction in relation to the adapter orientation. In anesthetized cats, we recorded evoked electrical responses from the visual cortex in a conventional manner.

Unsupervised Spike Sorting of extracellular electrophysiological recording in subthalamic nucleus of Parkinsonian patients.

The present study demonstrates the application of the Unsupervised Spike Sorting algorithm (USS) to separation of multi-unit recordings and investigation of neuronal activity patterns in the subthalamic nucleus (STN). This nucleus is the main target for deep brain stimulation (DBS) in Parkinsonian patients. The USS comprises a fast unsupervised learning procedure and allows sorting of multiple single units, if any, out of a bioelectric signal.