*Corresponding author.

Neurocomputing 38}40 (2001) 601}606

Entorhinal neuronal activity during delayed matchingtasks may depend upon muscarinic-induced

non-speci"c cation current I(CANM)

Erik FranseH n�, Angel A. Alonso�, Michael E. Hasselmo��*�Department of Numerical Analysis and Computer Science, Royal Institute of Technology,

SE-100 44 Stockholm, Sweden�Department of Neurology and Neurosurgery, Montreal Neurological Institute and McGill University,

Montreal, QC H3A, Canada�Department of Psychology, Boston University, 64 Cummington St., Boston, MA 02215, USA

Abstract

Biophysical compartmental models of stellate, pyramidal-like and interneurons in layer IIof the rat entorhinal cortex were used to explore cellular and synaptic components involved inneuronal responses to stimuli in a delayed match to sample (DMS) task. Simulations demon-strate that the muscarinic receptor-induced non-speci"c cation current, I(CANM), couldcontribute to these phenomena. Facilitation of I(CANM) by calcium in#ux during spikesinduced by the sample stimulus can cause enhanced responses for matches as well as delayactivity. In a network, lateral inhibition can produce match suppression, and in conjunctionwith stimulus selective/non-selective cells produce non-match enhancement and sup-pression. � 2001 Elsevier Science B.V. All rights reserved.

Keywords: Biophysical simulation; Entorhinal cortex; Working memory

1. Introduction

During a DMS task, neuronal "ring patterns of principal cells of entorhinal layer IImay display a variety of "ring patterns [7]. In the previous models [2}4], we showedhow a muscarine-activated calcium-dependent non-speci"c cation current [5,6] couldcontribute to the maintainance of delay-related persistent activity and match en-hancement, as well as match suppression.

0925-2312/01/$ - see front matter � 2001 Elsevier Science B.V. All rights reserved.PII: S 0 9 2 5 - 2 3 1 2 ( 0 1 ) 0 0 4 4 3 - X

Fig. 1. Left: There is one stellate cell and one interneuron and there are two non-stellate (pyramidal like)cells. The network also has 2 cells representing input to layer II. One of the pyramidal cells has input fromonly one input cell, representing an odor speci"c cell, and the other pyramidal cell has input from bothinput cells, representing an odor non-speci"c cell. The interneuron has no input from the input cells. Right:Cue-activity. The "gure shows simulated activity produced by each of the cell types in the example network.The simulated time is 3.8 s and the soma membrane potential of the cells is shown. The input is a matchstimulus, input cell A (stimA) is activated in sample as well as in match, and input cell B (stimB) is notactivated.

In the present model, the in#uences on match suppression of IPSPs evoked bylateral inhibition was studied further. Lateral inhibition could cause match sup-pression due to activation of interneurons by cells showing match enhancement. Thiselevated drive of the interneurons might increase their "ring rates, thereby increasingthe inhibition on their target cells. Furthermore, non-match enhancement and sup-pression has been studied in a network with stimulus selective as well as non-selectivecells. We have also studied possible mechanisms behind repetition suppression.Results are shown for a model with a slow component of an sAHP.

2. Cell and network model

The cell models use Hodgkin}Huxley representations of intrinsic currents and 1-Ddi!usion models of the intracellular calcium. Simulations were done using the simula-tion package GENESIS. The number of compartments are 7 for the stellate cell, 6 forthe pyramidal cell and 6 for the interneuron. The connectivity is shown left, in Fig. 1.Principally, all EC cells are connected to all the other ones. In the di!erent examplesonly the conductance of the connection varies. On the right of Fig. 1, a samplesimulation of a DMS experiment is shown. The stellate cell (stel) shows cue-relatedactivity (no increase or decrease in "ring at match relative to sample) due to balancedexcitatory and inhibitory input. Both pyramidal cells show subthreshold depolariz-ation during the delay period and match enhancement. The interneuron (int) receivessigni"cant input from the pyramidal cells and thus increases its "ring at match.

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Fig. 2. Delay activity. The pyramidal cells show depolarizations during the 2.4 s delay, one subthresholdand one suprathreshold. The simulated time is 3.8 s and the soma membrane potential of the cells is shown.Left: The input is a match stimulus, input cell stimA is activated in sample as well as in match, and input cellstimB is not activated. Right: The stimulus is a non-match with stimA is activated as sample and stimB isactivated as non-match stimulus.

3. Delay activity

During the delay period between sample and test stimulus, some cells displayelevated "ring rates. In Fig. 2, examples of delay-related activity are shown. Onthe left, the stimulus selective pyramidal cell (pyrA) displays stimulus-inducedsuprathreshold delay activity. The stimulus non-selective pyramidal cell (pyrAB)displays stimulus-induced subthreshold depolarization during the delay period.The stellate cell displays transient delay activity. On the right, the cell pyrA dis-plays delay activity which is terminated at the match presentation. This is due tothe increased inhibition from the interneuron and the lack of direct input fromstimB.

4. Match

In Fig. 3, we show examples of match-related activity. On the left, the stellate celldisplays match enhancement originating from the increased input from the pyramidalcells. The two pyramidal cells show subthreshold depolarization during the delayperiod as well as match enhancement at test stimulus. On the right, the stellate celldisplays match suppression originating from the strong input from the interneuron.The two pyramidal cells show subthreshold depolarization during the delay periodas well as match enhancement at test stimulus. The interneuron is driven by thepyramidal cells.

E. Franse&n et al. / Neurocomputing 38}40 (2001) 601}606 603

Fig. 3. The activation of I(CANM) in the pyramidal cells persist during the delay. The simulated time is3.8 s and the soma membrane potential of the cells is shown. In both "gures, the input is a match stimulus,input cell stimA is activated in sample as well as in match, and input cell stimB is not activated. Left: Matchenhancement. Right: Match suppression.

Fig. 4. The simulated time is 3.8 s and the soma membrane potential of the cells is shown. The stimulusselective cell pyrA shows sample activation and the stimulus non-selective cell pyrAB shows sample as wellas test stimulus non-speci"c enhancement. In both "gures, the input is a non-match stimulus, input cellstimA is activated in sample, and input cell stimB is activated at test. Left: Non-match enhancement. Right:Non-match suppression.

5. Non-match

In Fig. 4, we show examples of non-match-related activity. The di!erential activa-tion of the odor-speci"c cell pyrA and the odor non-speci"c cell pyrAB plays a keyrole. On the left, the stellate cell displays non-match enhancement originatingfrom the increased input from the pyramidal cell pyrAB paired with decreasedinhibition from the interneuron. The interneuron has strongest input from the cell

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Fig. 5. Repetition suppression by a slow AHP. The time constant of the sAHP is 1.2 s. The regular AHPused in all simulations has a time constant of 100 ms. The "gure shows simulated activity produced bya stellate cell. The simulated time is 6.9 s and the soma membrane potential of the cell is shown. Thestimulation is the same at all three times, and follows the same temporal pattern as before.

pyrA. On the right, the stellate cell displays non-match suppression originatingfrom the increased input from the interneuron despite the increased input frompyramidal cell pyrAB. The interneuron has strongest input from the cellpyrAB.

6. Repetition suppression

Repetition suppression has been suggested to be a passive process a!ecting allstimuli following a sample. In Fig. 5, an example of repetition suppression is shown.An additional slow AHP has been added, in accordance with experimental data [1].After the initial sample, a slow AHP outlasts the delay making the following testproduce fewer spikes.

7. Discussion

This work suggests that the cation current I(CANM) could play a role in theworkingmemory function of entorhinal cortex.We are currently evaluating the modelin a more complete network of layer II of entorhinal cortex.

Acknowledgements

This work was supported by a grant from the Human Frontier Science Programand grant NIH MH61492.

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References

[1] A.A. Alonso, R. Klink, Di!erential electroresponsiveness of stellate and pyramidal-like cells of medialentorhinal cortex layer II, J. Neurophysiol. 70 (1993) 128}143.

[2] E. FranseH n, A.A. Alonso,M.E. Hasselmo, Intrinsic properties of rat entorhinal cells relevant to workingmemory, Soc. Neurosci. Abstr. 25 (1999) 725.6.

[3] E. FranseH n, A.A. Alonso, M.E. Hasselmo, Cellular and synaptic mechanisms of match enhancementand supression in DMS working memory tasks involving entorhinal cortex, Soc. Neurosci. 26 (2000)596.6.

[4] M.E. Hasselmo, E. FranseH n, C.T. Dickson, A.A. Alonso, Computational modeling of entorhinal cortex,Ann. NY Acad. Sci. 911 (2000) 418}446.

[5] R. Klink, A. Alonso, Muscarinic modulation of the oscillatory and repetitive "ring properties ofentorhinal cortex layer II neurons, J. Neurophysiol. 77 (1997) 1813}1828.

[6] R. Klink, A.A. Alonso, Ionic mechanisms of muscarinic depolarization in entorhinal cortex layer IIneurons, J. Neurophysiol. 77 (1997) 1829}1843.

[7] B.J. Young, T. Otto, G.D. Fox, H. Eichenbaum, Memory representation within the parahippocampalregion, J. Neurosci. 17 (1997) 5183}5195.

Erik FranseH n is an assistant professor at the Department of Numerical Analysisand Computer Science at the Royal Institute of Technology, Stockholm, Sweden.He was a post-doctoral fellow at the Department of Psychology at HarvardUniversity during 1997}1998. He received his Ph.D. in Computer Science from theRoyal Institute of Technology in 1996. He received his B.Sc. in Physics fromUppsala University, Sweden in 1987. His research interests include mathematicalmodeling and biophysical simulation of neurons and networks of neurons. Hestudies the ionic basis of cellular and network properties and the functional role ofneuromodulators.

Michael E Hasselmo is an Associate Professor and Director of Graduate Studies in the Department ofPsychology at Boston University, and a member of the Program in Neurosciences and Center for Bio-Dynamics. He was on the faculty in the Department of Psychology at Harvard University during1991}1998, was a post-doctoral fellow at Caltech during 1988}1991, and studied for his D.Phil. on a Rhodesscholarship at Oxford University during 1984}1987. Research in his laboratory focuses on both physiolo-gical e!ects of neuromodulatory substances, as well as computational modeling and behavioral analysis ofthe role of neuromodulation in cortical function.

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