Despite a wealth of anatomical and electrophysiological data, network
mechanisms underlying the role of striatum in cognitive and motor
functions have remained obscure. To understand the functional properties
of this network it is important to characterize how incoming feedforward
excitatory inputs interact with the ongoing activity dynamics. Recent
experimental data [1-3] and computational models [4] suggest that purely
inhibitory recurrent connectivity of striatal neurons could support
transient neuronal assemblies.
We found that the activity dynamics in networks with a Gamma-shaped
connectivity profile were largely determined by the mean and variance of
the external input: weak external input or high input variance induced
unstructured asynchronous-irregular activity (AI), whereas stronger
external inputs or low input variance induced stable ‘winner-takes-all’
(WTA) dynamics. In an input regime close to the noise threshold the
activity organized into unstable, transient spatial bumps, referring to
as a ‘transient activity’ state (TA), resembling the experimentally
observed neuronal clusters [5]. Finally, we showed that a small
asymmetry in the spatial structure of the recurrent connectivity or in
the synaptic strengths was able to make the activity bump pattern moving
in one direction [6]. Such moving patterns could form the basis of
sequence generation in the striatum.
In summary, we conclude that a Gamma-shaped spatial connectivity
provides the striatal network with a rich dynamical repertoire, enabling
the bump activity to produce task related activity sequences.
Acknowledgements
This work was funded in parts by the NeuroSeeker Foundation, the EU
Erasmus PhD program NeuroTime, and the Carl-Zeiss Foundatation. All
simulations were carried out with NEST (http://www.nest-initiative.org).
References
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10.1523/jneurosci.4791-12.2013
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10.1523/jneurosci.3226-11.2011
[3] Lopez-Huerta et al. (2013) JNeurosci 33(11): 4964-75 doi:
10.1523/jneurosci.4721-12.2013
[4] Ponzi & Wickens (2010) JNeurosci 30(17):5894-911 doi:
10.1523/jneurosci.5540-09.2010
[5] Barbera et al. (2016) Neuron 92(1):203-213 doi:
10.1016/j.neuron.2016.08.037
[6] Koetter, R., & Wickens, J. (1995). JComp Neurosci, 2(3), 195-214.
pmid: 8521287
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