Search Results for author:
Found 21 papers, 8 papers with code
Two-compartment neuronal spiking model expressing brain-state specific apical-amplification, -isolation and -drive regimes
Mounting experimental evidence suggests that brain-state-specific neural mechanisms, supported by connectomic architectures, play a crucial role in integrating past and contextual knowledge with the current, incoming flow of evidence (e. g., from sensory systems).
Runtime Construction of Large-Scale Spiking Neuronal Network Models on GPU Devices
Simulation speed matters for neuroscientific research: this includes not only how quickly the simulated model time of a large-scale spiking neuronal network progresses, but also how long it takes to instantiate the network model in computer memory.
Comparing apples to apples -- Using a modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets
Neuroscience is moving towards a more integrative discipline, where understanding brain function requires consolidating the accumulated evidence seen across experiments, species, and measurement techniques.
NREM and REM: cognitive and energetic gains in thalamo-cortical sleeping and awake spiking model
We demonstrate that sleep has a positive effect on energy consumption and cognitive performance during the post-sleep awake classification task of handwritten digits.
Beyond spiking networks: the computational advantages of dendritic amplification and input segregation
Recent works have proposed that segregation of dendritic input (neurons receive sensory information and higher-order feedback in segregated compartments) and generation of high-frequency bursts of spikes would support error backpropagation in biological neurons.
Towards biologically plausible Dreaming and Planning in recurrent spiking networks
We propose a two-module (agent and model) spiking neural network in which "dreaming" (living new experiences in a model-based simulated environment) significantly boosts learning.
Burst-dependent plasticity and dendritic amplification support target-based learning and hierarchical imitation learning
The brain can learn to solve a wide range of tasks with high temporal and energetic efficiency.
Error-based or target-based? A unifying framework for learning in recurrent spiking networks
Finally, we show that our theoretical formulation suggests protocols to deduce the structure of learning feedback in biological networks.
Simulations Approaching Data: Cortical Slow Waves in Inferred Models of the Whole Hemisphere of Mouse
The model is capable to reproduce most of the features of the non-stationary and non-linear dynamics displayed by the high-resolution recording of the in-vivo mouse brain obtained by wide-field calcium imaging techniques.
Fast simulations of highly-connected spiking cortical models using GPUs
In this work we evaluate the performance of this library on the simulation of a cortical microcircuit model, based on LIF neurons and current-based synapses, and on a balanced network of excitatory and inhibitory neurons, using AdEx neurons and conductance-based synapses.
Thalamo-cortical spiking model of incremental learning combining perception, context and NREM-sleep-mediated noise-resilience
The brain exhibits capabilities of fast incremental learning from few noisy examples, as well as the ability to associate similar memories in autonomously-created categories and to combine contextual hints with sensory perceptions.
Target spiking patterns enable efficient and biologically plausible learning for complex temporal tasks
We propose a novel target-based learning scheme in which the learning rule derived from likelihood maximization is used to mimic a specific spiking pattern that encodes the solution to complex temporal tasks.
Scaling of a large-scale simulation of synchronous slow-wave and asynchronous awake-like activity of a cortical model with long-range interconnections
Cortical synapse organization supports a range of dynamic states on multiple spatial and temporal scales, from synchronous slow wave activity (SWA), characteristic of deep sleep or anesthesia, to fluctuating, asynchronous activity during wakefulness (AW).
Slow Waves Analysis Pipeline for extracting the Features of the Bi-Modality from the Cerebral Cortex of Anesthetized Mice
Cortical slow oscillations are an emergent property of the cortical network, a hallmark of low complexity brain states like sleep, and represent a default activity pattern.
Neurons and Cognition
Real-time cortical simulations: energy and interconnect scaling on distributed systems
We demonstrate the importance of the design of low-latency interconnect for speed and energy consumption.
Analysis and Model of Cortical Slow Waves Acquired with Optical Techniques
Here we combined wide-field fluorescence microscopy and a transgenic mouse model expressing a calcium indicator (GCaMP6f) in excitatory neurons to study SW propagation over the meso-scale under ketamine anesthesia.
Sleep-like slow oscillations improve visual classification through synaptic homeostasis and memory association in a thalamo-cortical model
The occurrence of sleep passed through the evolutionary sieve and is widespread in animal species.
The Brain on Low Power Architectures - Efficient Simulation of Cortical Slow Waves and Asynchronous States
DPSNN can be configured to stress either the networking or the computation features available on the execution platforms.
Gaussian and exponential lateral connectivity on distributed spiking neural network simulation
We measured the impact of long-range exponentially decaying intra-areal lateral connectivity on the scaling and memory occupation of a distributed spiking neural network simulator compared to that of short-range Gaussian decays.
EURETILE D7.3 - Dynamic DAL benchmark coding, measurements on MPI version of DPSNN-STDP (distributed plastic spiking neural net) and improvements to other DAL codes
The EURETILE project required the selection and coding of a set of dedicated benchmarks.
EURETILE 2010-2012 summary: first three years of activity of the European Reference Tiled Experiment
Furthermore, EURETILE investigates and implements the innovations for equipping the elementary HW tile with high-bandwidth, low-latency brain-like inter-tile communication emulating 3 levels of connection hierarchy, namely neural columns, cortical areas and cortex, and develops a dedicated cortical simulation benchmark: DPSNN-STDP (Distributed Polychronous Spiking Neural Net with synaptic Spiking Time Dependent Plasticity).
