9,323 research outputs found
Emergence of Connectivity Motifs in Networks of Model Neurons with Short- and Long-term Plastic Synapses
Recent evidence in rodent cerebral cortex and olfactory bulb suggests that short-term dynamics of excitatory synaptic transmission is correlated to stereotypical connectivity motifs. It was observed that neurons with short-term facilitating synapses form predominantly reciprocal pairwise connections, while neurons with short-term depressing synapses form unidirectional pairwise connections. The cause of these structural differences in synaptic microcircuits is unknown. We propose that these connectivity motifs emerge from the interactions between short-term synaptic dynamics (SD) and long-term spike-timing dependent plasticity (STDP). While the impact of STDP on SD was shown in vitro, the mutual interactions between STDP and SD in large networks are still the subject of intense research. We formulate a computational model by combining SD and STDP, which captures faithfully short- and long-term dependence on both spike times and frequency. As a proof of concept, we simulate recurrent networks of spiking neurons with random initial connection efficacies and where synapses are either all short-term facilitating or all depressing. For identical background inputs, and as a direct consequence of internally generated activity, we find that networks with depressing synapses evolve unidirectional connectivity motifs, while networks with facilitating synapses evolve reciprocal connectivity motifs. This holds for heterogeneous networks including both facilitating and depressing synapses. Our study highlights the conditions under which SD-STDP might the correlation between facilitation and reciprocal connectivity motifs, as well as between depression and unidirectional motifs. We further suggest experiments for the validation of the proposed mechanism
Input-specific maturation of synaptic dynamics of parvalbumin interneurons in primary visual cortex
Cortical networks consist of local recurrent circuits and long-range pathways from other brain areas. Parvalbumin-positive interneurons (PVNs) regulate the dynamic operation of local ensembles as well as the temporal precision of afferent signals. The synaptic recruitment of PVNs that support these circuit operations is not well-understood. Here we demonstrate that the synaptic dynamics of PVN recruitment in mouse visual cortex are customized according to input source with distinct maturation profiles. Whereas the long-range inputs to PVNs show strong short-term depression throughout postnatal maturation, local inputs from nearby pyramidal neurons progressively lose such depression. This enhanced local recruitment depends on PVN-mediated reciprocal inhibition and results from both pre- and postsynaptic mechanisms, including calcium-permeable AMPA receptors at PVN postsynaptic sites. Although short-term depression of long-range inputs is well-suited for afferent signal detection, the robust dynamics of local inputs may facilitate rapid and proportional PVN recruitment in regulating local circuit operations
A three-threshold learning rule approaches the maximal capacity of recurrent neural networks
Understanding the theoretical foundations of how memories are encoded and
retrieved in neural populations is a central challenge in neuroscience. A
popular theoretical scenario for modeling memory function is the attractor
neural network scenario, whose prototype is the Hopfield model. The model has a
poor storage capacity, compared with the capacity achieved with perceptron
learning algorithms. Here, by transforming the perceptron learning rule, we
present an online learning rule for a recurrent neural network that achieves
near-maximal storage capacity without an explicit supervisory error signal,
relying only upon locally accessible information. The fully-connected network
consists of excitatory binary neurons with plastic recurrent connections and
non-plastic inhibitory feedback stabilizing the network dynamics; the memory
patterns are presented online as strong afferent currents, producing a bimodal
distribution for the neuron synaptic inputs. Synapses corresponding to active
inputs are modified as a function of the value of the local fields with respect
to three thresholds. Above the highest threshold, and below the lowest
threshold, no plasticity occurs. In between these two thresholds,
potentiation/depression occurs when the local field is above/below an
intermediate threshold. We simulated and analyzed a network of binary neurons
implementing this rule and measured its storage capacity for different sizes of
the basins of attraction. The storage capacity obtained through numerical
simulations is shown to be close to the value predicted by analytical
calculations. We also measured the dependence of capacity on the strength of
external inputs. Finally, we quantified the statistics of the resulting
synaptic connectivity matrix, and found that both the fraction of zero weight
synapses and the degree of symmetry of the weight matrix increase with the
number of stored patterns.Comment: 24 pages, 10 figures, to be published in PLOS Computational Biolog
Signal processing in local neuronal circuits based on activity-dependent noise and competition
We study the characteristics of weak signal detection by a recurrent neuronal
network with plastic synaptic coupling. It is shown that in the presence of an
asynchronous component in synaptic transmission, the network acquires
selectivity with respect to the frequency of weak periodic stimuli. For
non-periodic frequency-modulated stimuli, the response is quantified by the
mutual information between input (signal) and output (network's activity), and
is optimized by synaptic depression. Introducing correlations in signal
structure resulted in the decrease of input-output mutual information. Our
results suggest that in neural systems with plastic connectivity, information
is not merely carried passively by the signal; rather, the information content
of the signal itself might determine the mode of its processing by a local
neuronal circuit.Comment: 15 pages, 4 pages, in press for "Chaos
Learning to Discriminate Through Long-Term Changes of Dynamical Synaptic Transmission
Short-term synaptic plasticity is modulated by long-term synaptic
changes. There is, however, no general agreement on the computational
role of this interaction. Here, we derive a learning rule for the release
probability and the maximal synaptic conductance in a circuit model
with combined recurrent and feedforward connections that allows learning
to discriminate among natural inputs. Short-term synaptic plasticity
thereby provides a nonlinear expansion of the input space of a linear
classifier, whereas the random recurrent network serves to decorrelate
the expanded input space. Computer simulations reveal that the twofold
increase in the number of input dimensions through short-term synaptic
plasticity improves the performance of a standard perceptron up to 100%.
The distributions of release probabilities and maximal synaptic conductances
at the capacity limit strongly depend on the balance between excitation
and inhibition. The model also suggests a new computational
interpretation of spikes evoked by stimuli outside the classical receptive
field. These neuronal activitiesmay reflect decorrelation of the expanded
stimulus space by intracortical synaptic connections
Dynamical Synapses Enhance Neural Information Processing: Gracefulness, Accuracy and Mobility
Experimental data have revealed that neuronal connection efficacy exhibits
two forms of short-term plasticity, namely, short-term depression (STD) and
short-term facilitation (STF). They have time constants residing between fast
neural signaling and rapid learning, and may serve as substrates for neural
systems manipulating temporal information on relevant time scales. The present
study investigates the impact of STD and STF on the dynamics of continuous
attractor neural networks (CANNs) and their potential roles in neural
information processing. We find that STD endows the network with slow-decaying
plateau behaviors-the network that is initially being stimulated to an active
state decays to a silent state very slowly on the time scale of STD rather than
on the time scale of neural signaling. This provides a mechanism for neural
systems to hold sensory memory easily and shut off persistent activities
gracefully. With STF, we find that the network can hold a memory trace of
external inputs in the facilitated neuronal interactions, which provides a way
to stabilize the network response to noisy inputs, leading to improved accuracy
in population decoding. Furthermore, we find that STD increases the mobility of
the network states. The increased mobility enhances the tracking performance of
the network in response to time-varying stimuli, leading to anticipative neural
responses. In general, we find that STD and STP tend to have opposite effects
on network dynamics and complementary computational advantages, suggesting that
the brain may employ a strategy of weighting them differentially depending on
the computational purpose.Comment: 40 pages, 17 figure
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