Structural plasticity improves stimulus encoding in a working memory model
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1
Frankfurt Institute for Advanced Studies, Germany
Instead of being fixed, hard-wired structures, cortical networks are capable of significant reorganization. As we learn new skills or adapt to changes in the environment, brain structure changes as well (Yoshida et al 2003, Hihara et al, 2006)- existing synapses are eliminated and new synapses are grown. These structural changes can sometimes be homeostatic, maintaining the stability of the system, while in other cases they may play an important role in shaping the network function (Zito and Svoboda, 2002). Moreover, it was suggested that such cooperative synaptic formation is important for explaining the statistics of synaptic connections observed in rat cortex, which could not emerge by random sparse connections alone (Fares and Stepanyants, 2009). How such activity dependent structural plasticity affects the function of the network remains unclear, however. Here, we investigate this question for a sparsely connected recurrent neural network, trained to perform a delayed match to sample task. As we have done in a previous model (Savin and Triesch, 2009), an output layer reads out the activity in the network providing a behavioral response, which yields a corresponding reward. Then, reward-modulated STDP (Izhikevich, 2003) shapes the synapses within the recurrent network and those connecting to the motor layer. In addition, structural plasticity is implemented in two steps. First, very weak synapses are pruned, as dendritic spines are known to retract in absence of synaptic activity (Lamprecht and LeDoux, 2004). Second, new synapses are grown between neurons which exhibit correlated activity, but are not yet synaptically connected. Moreover, the two processes are balanced, such that the overall connectivity of the network is preserved. When comparing networks implementing structural plasticity to networks with fixed random connectivity, we see that the performance can be significantly improved by network reorganization. The sparseness of the connectivity matrix, with values similar to those observed in the cortex, ensures appropriate dynamics for the network, but makes the performance critically dependent on the particular instance of the fixed weight matrix. In contrast, the activity-dependent synaptic reorganization will correct a ’bad’ initial choice of weights, such that the network can encode the input stimuli more reliably. Interestingly, the Fano factor for the distribution of incoming synapses is small, resembling values reported for cortical networks, as seen in (Fares and Stepanyants, 2009). Our results suggest that activity-dependent structural plasticity could play an important role in optimizing the sparse cortical connectivity to best encode information.
Conference:
Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010.
Presentation Type:
Poster Presentation
Topic:
Poster session II
Citation:
Savin
C and
Triesch
J
(2010). Structural plasticity improves stimulus encoding in a working memory model.
Front. Neurosci.
Conference Abstract:
Computational and Systems Neuroscience 2010.
doi: 10.3389/conf.fnins.2010.03.00231
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Received:
04 Mar 2010;
Published Online:
04 Mar 2010.
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Correspondence:
Cristina Savin, Frankfurt Institute for Advanced Studies, Frankfurt, Germany, savin@fias.uni-frankfurt.de