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Self-Organized Near-Zero-Lag Synchronization Induced by Spike-Timing Dependent Plasticity in Cortical Populations.

Matias FS, Carelli PV, Mirasso CR, Copelli M - PLoS ONE (2015)

Bottom Line: We show that STDP can promote auto-organized zero-lag synchronization in unidirectionally coupled neuronal populations.We also find synchronization regimes with negative phase difference (AS) that are stable against plasticity.Finally, we show that the interplay between negative phase difference and STDP provides limited synaptic weight distribution without the need of imposing artificial boundaries.

View Article: PubMed Central - PubMed

Affiliation: Instituto de Física, Universidade Federal de Alagoas, Maceió AL 57072-970, Brazil.

ABSTRACT
Several cognitive tasks related to learning and memory exhibit synchronization of macroscopic cortical areas together with synaptic plasticity at neuronal level. Therefore, there is a growing effort among computational neuroscientists to understand the underlying mechanisms relating synchrony and plasticity in the brain. Here we numerically study the interplay between spike-timing dependent plasticity (STDP) and anticipated synchronization (AS). AS emerges when a dominant flux of information from one area to another is accompanied by a negative time lag (or phase). This means that the receiver region pulses before the sender does. In this paper we study the interplay between different synchronization regimes and STDP at the level of three-neuron microcircuits as well as cortical populations. We show that STDP can promote auto-organized zero-lag synchronization in unidirectionally coupled neuronal populations. We also find synchronization regimes with negative phase difference (AS) that are stable against plasticity. Finally, we show that the interplay between negative phase difference and STDP provides limited synaptic weight distribution without the need of imposing artificial boundaries.

No MeSH data available.


Synaptic weight distributions in the presence of STDP rules.(a) Histogram of the gMS values. Inset: AS provides limited weight distribution even without an upper boundary. However, in the DS regime some synapses can grow unlimited. (b) Time evolution of four randomly chosen synaptic weights starting from different initial conditions in the AS and DS regimes. Each color represents a different simulation in which all initial synaptic conductances  were the same. The inhibitory conductances in the Slave-Interneuron population is gIS = 4 nS in the AS regime, while gIS = 16 nS in the DS regime.
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pone.0140504.g006: Synaptic weight distributions in the presence of STDP rules.(a) Histogram of the gMS values. Inset: AS provides limited weight distribution even without an upper boundary. However, in the DS regime some synapses can grow unlimited. (b) Time evolution of four randomly chosen synaptic weights starting from different initial conditions in the AS and DS regimes. Each color represents a different simulation in which all initial synaptic conductances were the same. The inhibitory conductances in the Slave-Interneuron population is gIS = 4 nS in the AS regime, while gIS = 16 nS in the DS regime.

Mentions: A remarkable outcome of our model is related to the synaptic weight distribution when the system reaches an AS regime via STDP. As a result of the dynamical interaction between AS and STDP, the weight distribution obeys the three key properties required for biophysical reliability [46] as shown in Fig 6(a). First, the shape of the distribution is stable. Although each synapse can individually change in time, the distribution of all synaptic weights maintains a similar pattern along time. Second, it is diverse; it does not concentrate all the values at the boundaries. Third, it is limited i.e. there are no infinitely large synapses. More interestingly, synaptic weights do not explode even without an arbitrarily chosen boundary.


Self-Organized Near-Zero-Lag Synchronization Induced by Spike-Timing Dependent Plasticity in Cortical Populations.

Matias FS, Carelli PV, Mirasso CR, Copelli M - PLoS ONE (2015)

Synaptic weight distributions in the presence of STDP rules.(a) Histogram of the gMS values. Inset: AS provides limited weight distribution even without an upper boundary. However, in the DS regime some synapses can grow unlimited. (b) Time evolution of four randomly chosen synaptic weights starting from different initial conditions in the AS and DS regimes. Each color represents a different simulation in which all initial synaptic conductances  were the same. The inhibitory conductances in the Slave-Interneuron population is gIS = 4 nS in the AS regime, while gIS = 16 nS in the DS regime.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4608682&req=5

pone.0140504.g006: Synaptic weight distributions in the presence of STDP rules.(a) Histogram of the gMS values. Inset: AS provides limited weight distribution even without an upper boundary. However, in the DS regime some synapses can grow unlimited. (b) Time evolution of four randomly chosen synaptic weights starting from different initial conditions in the AS and DS regimes. Each color represents a different simulation in which all initial synaptic conductances were the same. The inhibitory conductances in the Slave-Interneuron population is gIS = 4 nS in the AS regime, while gIS = 16 nS in the DS regime.
Mentions: A remarkable outcome of our model is related to the synaptic weight distribution when the system reaches an AS regime via STDP. As a result of the dynamical interaction between AS and STDP, the weight distribution obeys the three key properties required for biophysical reliability [46] as shown in Fig 6(a). First, the shape of the distribution is stable. Although each synapse can individually change in time, the distribution of all synaptic weights maintains a similar pattern along time. Second, it is diverse; it does not concentrate all the values at the boundaries. Third, it is limited i.e. there are no infinitely large synapses. More interestingly, synaptic weights do not explode even without an arbitrarily chosen boundary.

Bottom Line: We show that STDP can promote auto-organized zero-lag synchronization in unidirectionally coupled neuronal populations.We also find synchronization regimes with negative phase difference (AS) that are stable against plasticity.Finally, we show that the interplay between negative phase difference and STDP provides limited synaptic weight distribution without the need of imposing artificial boundaries.

View Article: PubMed Central - PubMed

Affiliation: Instituto de Física, Universidade Federal de Alagoas, Maceió AL 57072-970, Brazil.

ABSTRACT
Several cognitive tasks related to learning and memory exhibit synchronization of macroscopic cortical areas together with synaptic plasticity at neuronal level. Therefore, there is a growing effort among computational neuroscientists to understand the underlying mechanisms relating synchrony and plasticity in the brain. Here we numerically study the interplay between spike-timing dependent plasticity (STDP) and anticipated synchronization (AS). AS emerges when a dominant flux of information from one area to another is accompanied by a negative time lag (or phase). This means that the receiver region pulses before the sender does. In this paper we study the interplay between different synchronization regimes and STDP at the level of three-neuron microcircuits as well as cortical populations. We show that STDP can promote auto-organized zero-lag synchronization in unidirectionally coupled neuronal populations. We also find synchronization regimes with negative phase difference (AS) that are stable against plasticity. Finally, we show that the interplay between negative phase difference and STDP provides limited synaptic weight distribution without the need of imposing artificial boundaries.

No MeSH data available.