2,772 research outputs found
Storage of phase-coded patterns via STDP in fully-connected and sparse network: a study of the network capacity
We study the storage and retrieval of phase-coded patterns as stable
dynamical attractors in recurrent neural networks, for both an analog and a
integrate-and-fire spiking model. The synaptic strength is determined by a
learning rule based on spike-time-dependent plasticity, with an asymmetric time
window depending on the relative timing between pre- and post-synaptic
activity. We store multiple patterns and study the network capacity.
For the analog model, we find that the network capacity scales linearly with
the network size, and that both capacity and the oscillation frequency of the
retrieval state depend on the asymmetry of the learning time window. In
addition to fully-connected networks, we study sparse networks, where each
neuron is connected only to a small number z << N of other neurons. Connections
can be short range, between neighboring neurons placed on a regular lattice, or
long range, between randomly chosen pairs of neurons. We find that a small
fraction of long range connections is able to amplify the capacity of the
network. This imply that a small-world-network topology is optimal, as a
compromise between the cost of long range connections and the capacity
increase.
Also in the spiking integrate and fire model the crucial result of storing
and retrieval of multiple phase-coded patterns is observed. The capacity of the
fully-connected spiking network is investigated, together with the relation
between oscillation frequency of retrieval state and window asymmetry
Design and Implementation of BCM Rule Based on Spike-Timing Dependent Plasticity
The Bienenstock-Cooper-Munro (BCM) and Spike Timing-Dependent Plasticity
(STDP) rules are two experimentally verified form of synaptic plasticity where
the alteration of synaptic weight depends upon the rate and the timing of pre-
and post-synaptic firing of action potentials, respectively. Previous studies
have reported that under specific conditions, i.e. when a random train of
Poissonian distributed spikes are used as inputs, and weight changes occur
according to STDP, it has been shown that the BCM rule is an emergent property.
Here, the applied STDP rule can be either classical pair-based STDP rule, or
the more powerful triplet-based STDP rule. In this paper, we demonstrate the
use of two distinct VLSI circuit implementations of STDP to examine whether BCM
learning is an emergent property of STDP. These circuits are stimulated with
random Poissonian spike trains. The first circuit implements the classical
pair-based STDP, while the second circuit realizes a previously described
triplet-based STDP rule. These two circuits are simulated using 0.35 um CMOS
standard model in HSpice simulator. Simulation results demonstrate that the
proposed triplet-based STDP circuit significantly produces the threshold-based
behaviour of the BCM. Also, the results testify to similar behaviour for the
VLSI circuit for pair-based STDP in generating the BCM
Correlation-based model of artificially induced plasticity in motor cortex by a bidirectional brain-computer interface
Experiments show that spike-triggered stimulation performed with
Bidirectional Brain-Computer-Interfaces (BBCI) can artificially strengthen
connections between separate neural sites in motor cortex (MC). What are the
neuronal mechanisms responsible for these changes and how does targeted
stimulation by a BBCI shape population-level synaptic connectivity? The present
work describes a recurrent neural network model with probabilistic spiking
mechanisms and plastic synapses capable of capturing both neural and synaptic
activity statistics relevant to BBCI conditioning protocols. When spikes from a
neuron recorded at one MC site trigger stimuli at a second target site after a
fixed delay, the connections between sites are strengthened for spike-stimulus
delays consistent with experimentally derived spike time dependent plasticity
(STDP) rules. However, the relationship between STDP mechanisms at the level of
networks, and their modification with neural implants remains poorly
understood. Using our model, we successfully reproduces key experimental
results and use analytical derivations, along with novel experimental data. We
then derive optimal operational regimes for BBCIs, and formulate predictions
concerning the efficacy of spike-triggered stimulation in different regimes of
cortical activity.Comment: 35 pages, 9 figure
Emergence of Functional Specificity in Balanced Networks with Synaptic Plasticity
In rodent visual cortex, synaptic connections between orientation-selective neurons are unspecific at the time of eye opening, and become to some degree functionally specific only later during development. An explanation for this two-stage process was proposed in terms of Hebbian plasticity based on visual experience that would eventually enhance connections between neurons with similar response features. For this to work, however, two conditions must be satisfied: First, orientation selective neuronal responses must exist before specific recurrent synaptic connections can be established. Second, Hebbian learning must be compatible with the recurrent network dynamics contributing to orientation selectivity, and the resulting specific connectivity must remain stable for unspecific background activity. Previous studies have mainly focused on very simple models, where the receptive fields of neurons were essentially determined by feedforward mechanisms, and where the recurrent network was small, lacking the complex recurrent dynamics of large-scale networks of excitatory and inhibitory neurons. Here we studied the emergence of functionally specific connectivity in large-scale recurrent networks with synaptic plasticity. Our results show that balanced random networks, which already exhibit highly selective responses at eye opening, can develop feature-specific connectivity if appropriate rules of synaptic plasticity are invoked within and between excitatory and inhibitory populations. If these conditions are met, the initial orientation selectivity guides the process of Hebbian learning and, as a result, functionally specific and a surplus of bidirectional connections emerge. Our results thus demonstrate the cooperation of synaptic plasticity and recurrent dynamics in large-scale functional networks with realistic receptive fields, highlight the role of inhibition as a critical element in this process, and paves the road for further computational studies of sensory processing in neocortical network models equipped with synaptic plasticity
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