10
N. Andreakos et al.
1’ performance was excellent across all conditions, whereas ‘Model’s 2’ performance
was consistently the worst. A key finding of ours is that the number of ‘active cells per
pattern’ has a massive effect on the recall quality of the network regardless of how many
patterns are stored in it. As the number of dedicated cells representing a memory (‘active
cells per pattern’) decrease, the memory capacity of the CA1-PC network increases, so
interference effects between stored patterns decrease, and mean recall quality increases.
Another key finding of ours is that increased firing frequency response of a presynaptic
inhibitory cell (BSC) inhibiting a network of PCs has a better success at removing spurious activity at the network level and thus improving recall quality than an increased
synaptic efficacy of a presynaptic inhibitory cell (BSC) on a postsynaptic PC while
keeping its presynaptic firing rate fixed.
Acknowledgements. This work was supported in part by EU Horizon 2020 through Project
ULTRACEPT under Grant 778062.
References
1. Cutsuridis, V., Cobb, S., Graham, B.P.: Encoding and retrieval in a model of the hippocampal
CA1 microcircuit. Hippocampus 20, 423–446 (2010)
2. Megias, M., Emri, Z.S., Freund, T.F., Gulyas, A.I.: Total number and distribution of inhibitory
and excitatory synapses on hippocampal CA1 pyramidal cells. Neuroscience 102(3), 527–540
(2001)
3. Gulyas, A.I., Megias, M., Emri, Z., Freund, T.F.: Total number and ratio of excitatory and
inhibitory synapses converging onto single interneurons of different types in the CA1 areas
of the rat hippocampus. J. Neurosci. 19(22), 10082–10097 (1999)
4. Poirazi, P., Brannon, T., Mel, B.W.: Arithmetic of subthreshold synaptic summation in a
model of CA1 pyramidal cell. Neuron 37, 977–987 (2003)
5. Poirazi, P., Brannon, T., Mel, B.W.: Pyramidal neuron as a 2-layer neural network. Neuron
37, 989–999 (2003)
6. Santhakumar, V., Aradi, I., Soltetz, I.: Role of mossy fiber sprouting and mossy cell loss in
hyperexcitability: a network model of the dentate gyrus incorporating cell types and axonal
topography. J. Neurophysiol. 93, 437–453 (2005)
7. Buhl, E.H., Szilágyi, T., Halasy, K., Somogyi, P.: Physiological properties of anatomically
identified basket and bistratified cells in the CA1 area of the rat hippocampus in vitro.
Hippocampus 6(3), 294–305 (1996)
8. Hines, M.L., Carnevale, T.: The NEURON simulation environment. Neural Comput. 9, 1179–
1209 (1997)
9. Amaral, D., Lavenex, P.: Hippocampal neuroanatomy. In: Andersen, P., Morris, R., Amaral,
D., Bliss, T., O’Keefe, J. (eds.) The Hippocampus Book, pp. 37–114. Oxford University Press,
Oxford (2007)
10. Andersen, P., Morris, R., Amaral, D., Bliss, T., O’Keefe, J.: The Hippocampus Book. Oxford
University Press, Oxford (2007)
11. Buhl, E.H., Halasy, K., Somogyi, P.: Diverse sources of hippocampal unitary inhibitory
postsynaptic potentials and the number of synaptic release sites. Nature 368, 823–828 (1994)
12. Cutsuridis, V.: Improving the recall performance of a brain mimetic microcircuit model. Cogn.
Comput. 11, 644–655 (2019). https://doi.org/10.1007/s12559-019-09658-8
N. Andreakos et al.
1’ performance was excellent across all conditions, whereas ‘Model’s 2’ performance
was consistently the worst. A key finding of ours is that the number of ‘active cells per
pattern’ has a massive effect on the recall quality of the network regardless of how many
patterns are stored in it. As the number of dedicated cells representing a memory (‘active
cells per pattern’) decrease, the memory capacity of the CA1-PC network increases, so
interference effects between stored patterns decrease, and mean recall quality increases.
Another key finding of ours is that increased firing frequency response of a presynaptic
inhibitory cell (BSC) inhibiting a network of PCs has a better success at removing spurious activity at the network level and thus improving recall quality than an increased
synaptic efficacy of a presynaptic inhibitory cell (BSC) on a postsynaptic PC while
keeping its presynaptic firing rate fixed.
Acknowledgements. This work was supported in part by EU Horizon 2020 through Project
ULTRACEPT under Grant 778062.
References
1. Cutsuridis, V., Cobb, S., Graham, B.P.: Encoding and retrieval in a model of the hippocampal
CA1 microcircuit. Hippocampus 20, 423–446 (2010)
2. Megias, M., Emri, Z.S., Freund, T.F., Gulyas, A.I.: Total number and distribution of inhibitory
and excitatory synapses on hippocampal CA1 pyramidal cells. Neuroscience 102(3), 527–540
(2001)
3. Gulyas, A.I., Megias, M., Emri, Z., Freund, T.F.: Total number and ratio of excitatory and
inhibitory synapses converging onto single interneurons of different types in the CA1 areas
of the rat hippocampus. J. Neurosci. 19(22), 10082–10097 (1999)
4. Poirazi, P., Brannon, T., Mel, B.W.: Arithmetic of subthreshold synaptic summation in a
model of CA1 pyramidal cell. Neuron 37, 977–987 (2003)
5. Poirazi, P., Brannon, T., Mel, B.W.: Pyramidal neuron as a 2-layer neural network. Neuron
37, 989–999 (2003)
6. Santhakumar, V., Aradi, I., Soltetz, I.: Role of mossy fiber sprouting and mossy cell loss in
hyperexcitability: a network model of the dentate gyrus incorporating cell types and axonal
topography. J. Neurophysiol. 93, 437–453 (2005)
7. Buhl, E.H., Szilágyi, T., Halasy, K., Somogyi, P.: Physiological properties of anatomically
identified basket and bistratified cells in the CA1 area of the rat hippocampus in vitro.
Hippocampus 6(3), 294–305 (1996)
8. Hines, M.L., Carnevale, T.: The NEURON simulation environment. Neural Comput. 9, 1179–
1209 (1997)
9. Amaral, D., Lavenex, P.: Hippocampal neuroanatomy. In: Andersen, P., Morris, R., Amaral,
D., Bliss, T., O’Keefe, J. (eds.) The Hippocampus Book, pp. 37–114. Oxford University Press,
Oxford (2007)
10. Andersen, P., Morris, R., Amaral, D., Bliss, T., O’Keefe, J.: The Hippocampus Book. Oxford
University Press, Oxford (2007)
11. Buhl, E.H., Halasy, K., Somogyi, P.: Diverse sources of hippocampal unitary inhibitory
postsynaptic potentials and the number of synaptic release sites. Nature 368, 823–828 (1994)
12. Cutsuridis, V.: Improving the recall performance of a brain mimetic microcircuit model. Cogn.
Comput. 11, 644–655 (2019). https://doi.org/10.1007/s12559-019-09658-8