Cellular Basis of Working Memory at a High Spatial Resolution
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1
RIKEN Brain Science Institute, Japan
Since Patricia Goldman-Rakic proposed the cellular mechanism of working memory (Goldman-Rakic, 1995), a lot of studies have been done to substantiate that idea. Considerable advance has been made by computational modeling, in particular, on how irregular neuronal spiking or slow voltage-dependent NMDA receptor-mediated current could contribute to realization and stability of the selective persistent activity, the putative neural correlate of working memory (Amit & Brunel, 1997; Lisman et al., 1998; Wang, 1999). Contrary to such temporal aspects, much less attention has been drawn on how spatial aspects, namely, spatially extended dendritic branches of individual neurons could affect the property of the persistent activity. It was probably because experimentally examining thin dendritic branches was so demanding that there was no clear evidence against the prevailing view that the dendritic branch is just a connector that simply integrates the synaptic inputs. However, recent in vitro electrophysiological studies have revealed that individual thin dendritic branches of the cortical pyramidal cells can actually impose thresholding, amplifying, and saturating operations in a location-specific manner (Polsky et al., 2004), supporting a new picture of the pyramidal cell as a two-layer artificial neural network proposed in a detailed modeling study (Poirazi et al., 2003). Therefore, whether and how such a spatial property of the single neuron affects the behavior of an entire neural circuit would now be of great interest. In order to address this issue, I have constructed a model of the working memory circuit consisting of a rate-coding neural unit that has multiple dendritic branches, each of which has an independent threshold for activation, and have examined the behavior of the model by simulations and dynamical system analyses (Morita et al., 2007; Morita, 2008). Given that the input to the neuron is non-uniformly distributed over the branches, as the input increases, the ratio of the branches that exceed the local threshold is expected to increase so that the output of the neuron, which was assumed to be roughly proportional to the number of the activated branches, should increase at an accelerated pace until saturation occurs. Consequently, the initial part of the neuronal input-output relationship becomes convex downward, ensuring that there appears a stable steady state where every neuron has small but nonzero positive activity when the external input to the circuit has a low signal-to-background ratio. This would have some similarity to the previous result that the temporally fluctuating combined excitatory and inhibitory input can stabilize the low-frequency spontaneous firing state (Amit & Brunel, 1997), considering that such a input fluctuation can also make the initial part of the input-output relationship convex downward (Shu et al., 2003). It would thus be suggested that the low-frequency firing of a cell in the working memory circuit may be caused not only by the temporal fluctuation of the input at the soma but also by the suprathreshold activity of a small number of dendritic branches. In addition, if a simultaneous increase in excitation and inhibition can be canceled out at the dendritic branches of the individual neurons apart from their somata, the circuit would have an ability to form a selective persistent activity depending on the signal-to-background ratio, but insensitively to the absolute intensity, of the external input. The model provides possible ways of experimental verification, as well as implications on the animal behavior.
References
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3. Lisman JE, Fellous JM, Wang XJ. A role for NMDA-receptor channels in working memory. (1998) Nat Neurosci 1:273-275.
4. Morita K. Possible role of dendritic compartmentalization in the spatial working memory circuit. (2008) J Neurosci 28:7699-7724.
5. Morita K, Okada M, Aihara K. Selectivity and stability via dendritic nonlinearity. (2007) Neural Comput 19:1798-1853.
6. Poirazi P, Brannon T, Mel BW. Pyramidal neuron as two-layer neural network. (2003) Neuron 37:989-999.
7. Polsky A, Mel BW, Schiller J. Computational subunits in thin dendrites of pyramidal cells. (2004) Nat Neurosci 7:621-627.
8. Shu Y, Hasenstaub A, Badoual M, Bal T, McCormick DA. Barrages of synaptic activity control the gain and sensitivity of cortical neurons. (2003) J Neurosci 23:10388-10401.
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Conference:
Bernstein Symposium 2008, Munich, Germany, 8 Oct - 10 Oct, 2008.
Presentation Type:
Poster Presentation
Topic:
All Abstracts
Citation:
Morita
K
(2008). Cellular Basis of Working Memory at a High Spatial Resolution.
Front. Comput. Neurosci.
Conference Abstract:
Bernstein Symposium 2008.
doi: 10.3389/conf.neuro.10.2008.01.094
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Received:
17 Nov 2008;
Published Online:
17 Nov 2008.
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Correspondence:
Kenji Morita, RIKEN Brain Science Institute, Chiba, Japan, morita@p.u-tokyo.ac.jp