| M.E. Hasselmo, E. Schnell, and E. Barkai. Dynamics of learning and recall at excitatory recurrent synapses and cholinergic modulation in hippocampal region CA3. J. Neurosci., 15:5249-5262, 1995. |
....networks have been especially useful for modeling processes within the hippocampal region of the mammalian brain, since this region is known to have the strong recurrent connectivity necessary for attractor dynamics. There are attractorbased models of associative memory storage and recall [13, 14, 11, 17, 9], spatial representations [20, 18, 16] context dependent coding [18, 15, 5, 6, 4] path integration [14, 18, 16] and route learning [2, 19] in the hippocampus. Recently, we have proposed a paradigm called latent attractor computation, which is useful for generating context dependent codes or ....
M.E. Hasselmo, E. Schnell, and E. Barkai. Dynamics of learning and recall at excitatory recurrent synapses and cholinergic modulation in hippocampal region CA3. J. Neurosci., 15:5249--5262, 1995.
....the somata of pyramidal cells [25] Taken together, these findings indicate that acetylcholine is known to alter a wide array of cellular properties, but the question remains as to the resulting effect on network function. The major effort in this direction has been made by Hasselmo and colleagues [4,5,30 34]. In their model, acetylcholine acts to switch between learning and recall of spike frequency patterns by suppressing synaptic transmission at the Schaffer collaterals or the recurrent collaterals of the stratum radiatum in favor of input from entorhinal cortex in the stratum lacunosum moleculare. ....
....of 10 to yield a value for ACh whose effect is diminished by approximately this amount in tissue due to cholinesterase [18] The suppression of excitatory synaptic transmission in the stratum radiatum (s.r. and stratum lacunosum moleculare (s.l. m. is based on the data of Hasselmo and colleagues [33,34]. The logarithmic model E.D. Menschik, L.H. Finkel Artificial Intelligence in Medicine 13 (1998) 99 121 108 g syn g syn =# log( ACh] # (4) gave the best fit to their data yielding #= 0.598, #=2.226 for s.r. and #= 0.176, #=1.352 for s.l. m. In our simulations, this equation is used to ....
Hasselmo ME, Schnell E, Barkai E. Dynamics of learning and recall at excitatory recurrent synapses and cholinergic modulation in rat hippocampal region CA3. J Neurosci 1995;15:5249 -- 62.
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M.E. Hasselmo, E. Schnell, and E. Barkai. Dynamics of learning and recall at excitatory recurrent synapses and cholinergic modulation in hippocampal region CA3. J. Neurosci., 15:5249-5262, 1995.
No context found.
Hasselmo, M.E., Schnell, E. and Barkai, E. (1995) Dynamics of learning and recall at excitatory recurrent synapses and cholinergic modulation in hippocampal region CA3 J. Neurosci. 15, 5249--5262
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Hasselmo, M.E., Schnell, E. & Barkai, E. (1995) Dynamics of learning and recall at excitatory recurrent synapses and cholinergic modulation in rat hippocampal region CA3. Journal of Neuroscience 15(7):5249-5262.
No context found.
Hasselmo, M.E., Schnell, E. & Barkai, E. (1995) Dynamics of learning and recall at excitatory recurrent synapses and cholinergic modulation in rat hippocampal region CA3. Journal of Neuroscience 15(7):5249-5262.
No context found.
Hasselmo, M.E., Schnell, E. & Barkai, E. (1995) Dynamics of learning and recall at excitatory recurrent synapses and cholinergic modulation in rat hippocampal region CA3. Journal of Neuroscience 15(7):5249-5262.
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