Event Abstract

Robust spatial working memory through inhibitory gamma synchrony

  • 1 Stanford University, Department of Neurobiology, United States
  • 2 Stanford University, Department of Bioengineering, United States
  • 3 University of Heidelberg, Kirchhoff Institute for Physics, Germany

Persistent firing and gamma (30-80Hz) oscillations are known to be critically involved in working memory [1,2], yet previous models have ignored or avoided oscillations as being potentially detrimental to working memory [3]. Here, we develop a model in which these gamma oscillations have a direct role in the robust maintenance of persistent information. Current models of working memory [4,5] use a recurrent excitatory-inhibitory framework where the excitatory recurrence leads to persistence, and the inhibition prevents diffusion of the pattern after stimulus offset. However, any heterogeneities in the neurons (firing rates, thresholds etc) tend to cause drift of the stored pattern towards attractor locations (most excitable neurons). Previous attempts to create functional homogeneity in the neural firing rates have used activity-dependent synaptic scaling rules [5], whose timescale (hours to days, [6]) is not reasonable for the rapid, reversible homogeneity required for stable maintenance of information over short durations in working memory. Here we propose a simple mechanism operating at the timescale of milliseconds to seconds that can reversibly create functional homogeneity in the firing landscape: inhibitory gamma synchrony. Upon imposing an external (background) inhibitory rhythm in the gamma range, model pyramidal neurons tend to synchronize to the rhythm, and equalize their firing rates. Simply, this can be explained as follows: Each pyramidal neuron has a small window of opportunity for firing before being shunted (reset) to resting potential by the periodic inhibitory background. This prevents the cumulative expression of firing heterogeneity among neurons over time, as the membrane potentials of all neurons are "reset" at the beginning of every cycle of inhibition. The resulting firing homogeneity may permit persistent, localized firing with little drift, and could facilitate active maintenance of information in working memory. We tested this theory using a neuromorphic chip with 1024 excitatory silicon neurons arranged in a 32x32 grid (Supplementary Figure 2) with 256 interleaved inhibitory neurons. Background inhibitory gamma synchronization was achieved by global recurrence among inhibitory neurons [7]. Local recurrence among excitatory neurons permitted persistent activity. The strong background inhibitory gamma rhythm indeed facilitated homogenization of excitatory neuron firing rates (coefficient of variation was 0.59 vs. 1.06 with strong and weak synchrony, respectively, Supplementary Figure 1). The network, in the strongly synchronous case, also demonstrated stable maintenance of stored patterns with less drift of the centroid (1.64+/-0.16 grid points), compared to the weakly synchronous case (3.28+/-0.43 grid points), despite comparable levels of overall inhibition (Supplementary Figure 2). Thus, our model demonstrates a mechanism by which inhibitory gamma synchrony could facilitate robust maintenance of information in spatial working memory by rapid, reversible homogenization of firing rates.

References

1. Pesaran et al, Nat. Neurosci. 5, 805 (2002).

2. Fuchs et al, Neuron, 53, 591 (2007).

3. Gutkin et al, J. Comput. Neurosci, 11, 121 (2004).

4. Compte et al, Cereb. Cortex 10, 910 (2000).

5. Renart et al, Neuron 38, 473 (2003).

6. Perez-Otano and Ehlers, Trends Neurosci. 28, 229 (2005).

7. Arthur and Boahen, IEEE Trans. Neural Netw. 18, 1815 (2007).

Conference: Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010.

Presentation Type: Oral Presentation

Topic: Oral presentations

Citation: Sridharan D, Millner S, Arthur J and Boahen K (2010). Robust spatial working memory through inhibitory gamma synchrony. Front. Neurosci. Conference Abstract: Computational and Systems Neuroscience 2010. doi: 10.3389/conf.fnins.2010.03.00012

Received: 17 Feb 2010; Published Online: 17 Feb 2010.

* Correspondence: Devarajan Sridharan, Stanford University, Department of Neurobiology, Stanford, CA, United States, dsridhar@stanford.edu

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