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Modulation of the input/output function of rat piriform cortex pyramidal cells
Abstract
1. In transverse brain slice preparations of rat piriform cortex, we characterized the repetitive firing properties of layer II pyramidal cells in control conditions (n = 78) and during perfusion of the cholinergic agonist carbachol (n = 26), with the ultimate goal of developing realistic computational simulations of the cholinergic modulation of the input/output function of these neurons. The response of neurons to prolonged (1 s) intracellular current injections was examined at a full range of current injection amplitudes, providing three-dimensional plots of firing frequency versus current amplitude versus time. 2. All neurons showed adaptation in response to intracellular current injection, with repetitive generation of action potentials at frequencies that were highest at the onset of the pulse and that decreased considerably thereafter. Substantial differences were observed between cells with regard to their rates of adaptation and the maximal number of action potentials they could generate during the current pulse. 3. The adaptation characteristics of each neuron were quantified by plotting the number of action potentials generated in 1 s as a function of the normalized current injection amplitude and measuring the area beneath this plot of the number of spikes versus current injection amplitude (S-I plot). This value was termed S-I value and allowed neurons to be plotted on a continuum including neurons showing strong adaptation (S-I value < 8.0) and neurons showing weak adaptation (S-I value > 8.0). The group showing weak adaptation contained 36% of the cells in control solution and 93.8% of the cells in 20 microM carbachol. 4. Neurons showing strong adaptation did not differ significantly from neurons showing weak adaptation in control conditions in measurements of resting potential, input resistance, threshold, and spike amplitude. Only a small difference was found in frequencies of firing measured soon after pulse onset (after 100 ms). This implies that differences in S-I values are primarily due to different rates of adaptation in later parts of the response. 5. Perfusion with solution containing the cholinergic agonist carbachol (2–100 microM) or 0 Ca2+ and 200 microM cadmium resulted in a substantial increase in the S-I values of neurons showing strong adaptation but had only a small effect on their initial firing rates. The effect on weakly adapting cells was smaller. In the presence of 20 microM carbachol, neurons showed a distribution shifted predominantly toward weak adaptation (n = 26).(ABSTRACT TRUNCATED AT 400 WORDS)
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“The spiking response of cortical neurons to sensory input is further enhanced by muscarinic receptors causing a decrease in potassium conductances including resting conductances (Krnjevik et al., 1971; Cole and Nicoll, 1984; Barkai and Hasselmo, 1994; Gulledge et al., 2009) the M current (Madison et al., 1987) and the calcium-dependent potassium current (Madison and Nicoll, 1984; Schwindt et al.,”
From Modes and models of forebrain cholinergic neuromodulation of cognition (2011) · cites this paper as Barkai and Hasselmo, 1994
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A large-scale simulation of the piriform cortex by a cell automaton-based network model
Claverol, E. T., Brown, A. D., & Chad, J. E. (2002) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2002.801986 · Cited by 12
Protopapas, A. D., & Bower, J. M. (2000) · Brain Research · doi:10.1016/s0006-8993(00)02070-9 · Cited by 12
Spike Coding in Pyramidal Cells of the Piriform Cortex of Rat
Protopapas, A. D., & Bower, J. M. (2001) · Journal of Neurophysiology · doi:10.1152/jn.2001.86.3.1504 · Cited by 12
“The medium consisted of (in mM): 26 NaHCO3, 124 NaCl, 5 KCl, 1.2 KH2PO4, 2.4 CaCl2, 1.3 MgSO4, and 10 dextrose (Barkai and Hasselmo 1994; Tseng and Haberly 1989).”
BackgroundResultInfluentialInternal Cholinergic Regulation of Learning and Recall in a Model of Olfactory Processing
de Almeida, L., Idiart, M., Dean, O. R., Devore, S., Smith, D. M., & Linster, C. (2016) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2016.00256 · Cited by 11
“…and inhibitory synaptic transmission as well as synaptic plasticity (Williams and Constanti, 1988; Tseng and Haberly, 1989; Hasselmo et al., 1992; Barkai and Hasselmo, 1994; Barkai et al., 1994; Hasselmo and Barkai, 1995; Hasselmo and Cekic, 1996; Patil et al., 1998; Patil and Hasselmo, 1999;…”
BackgroundHasselmo, M. E., Alexander, A. S., Hoyland, A., Robinson, J. C., Bezaire, M. J., Chapman, G. W., Saudargienė, A., Carstensen, L. C., & Dannenberg, H. (2020) · Neuroscience · doi:10.1016/j.neuroscience.2020.03.048 · Cited by 11
“…memory function, with a focus on how cholinergic enhancement of spiking to afferent input and cholinergic presynaptic inhibition of modifiable recurrent synapses could enhance encoding relative to retrieval or consolidation dynamics (Barkai et al., 1994; Hasselmo et al., 1995; Hasselmo, 2006).”
InfluentialSelf-citationModulation of Neuronal Adaptation and Cortical Associative Memory Function.
Hasselmo, M. E., Barkai, E., Horwitz, G. D., et al. (1994) · Springer eBooks · doi:10.1007/978-1-4615-2714-5_46 · Cited by 10
Self-citationSemantic refractory access disorders
Warrington, E. K., & Crutch, S. J. (2007) · Cambridge University Press eBooks · doi:10.1017/cbo9780511544965.002 · Cited by 10
A model of the hippocampus combining self-organization and associative memory function
Hasselmo, M. E., Schnell, E., Berke, J. D., & Barkai, E. (1994) · neural information processing systems · Cited by 10
Self-citationCognitive computation using neural representations of time and space in the Laplace domainPreprint
Howard, M. W., & Hasselmo, M. E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.11668 · Cited by 19
“…computational studies have suggested a number of possible mechanisms (Silver, 2010), including active dendritic computation (Mehaffey, Doiron, Maler, & Turner, 2005; Poirazi, Brannon, & Mel, 2003), and neuromodulatory agents such as acetylcholine (Barkai & Hasselmo, 1994; Fransén et al., 2006).”
BackgroundSelf-citationBeerendonk, L., Mejías, J. F., Nuiten, S. A., de Gee, J. W., Fahrenfort, J. J., & van Gaal, S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.28.550956 · Cited by 10
Quattrochi, J. J., & Hobson, J. A. (1999) · Journal of Sleep Research · doi:10.1046/j.1365-2869.1999.00173.x · Cited by 9
Spatio-temporal cholinergic modulation in cultured networks of rat cortical neurons: Evoked activity
Tateno, T., Jimbo, Y., & Robinson, H. P. C. (2005) · Neuroscience · doi:10.1016/j.neuroscience.2005.04.055 · Cited by 8
Hewitt, L. T., Ordemann, G. J., & Brager, D. H. (2021) · Physiological Reports · doi:10.14814/phy2.14848 · Cited by 7
“Similar to our observation in stratum oriens interneurons, cortical neurons in the piriform cortex display heterogeneous SFA (Barkai & Hasselmo, 1994).”
MethodResultImproving Associative Memory in a Network of Spiking Neurons
Hunter, R., Cobb, S., & Graham, B. P. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-87559-8_66 · Cited by 8
“It can also be assumed that estimates of the memory capacity of biological networks can reasonably be based on the capacity of artificial models using simple computing units (Barkai and Hasselmo, 1994).”
BackgroundThe Role of Hierarchical Dynamical Functions in Coding for Episodic Memory and Cognition
Dannenberg, H., Alexander, A. S., Robinson, J. C., & Hasselmo, M. E. (2019) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01439 · Cited by 7
“…are the intrinsic dynamics of neurons that include the spike frequency accomodation (or adaptation) caused by calcium-activated potassium currents (Barkai & Hasselmo, 1994; Connors, Gutnick, & Prince, 1982), the resonance and rebound spiking caused by hyperpolarization activated cation currents…”
Self-citationDynamics of Storage and Recall in Hippocampal Associative Memory Networks
Graham, B. P. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-27862-7_1 · Cited by 7
“In experimental work with rat piriform cortex and hippocampus, they have demonstrated that cholinergic input selectively suppresses layer-specific or recurrent excitation, while promoting plasticity in the same pathway [ 4 , 24, 27, 28].”
BackgroundDehaqani, A. A., Michelon, F., Patella, P., Petrucco, L., Piasini, E., & Iurilli, G. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114013 · Cited by 6
Alternating dominance of NMDA and AMPA for learning and recall: a computer model
Chover, J., Haberly, L. B., & Lytton, W. W. (2001) · Neuroreport · doi:10.1097/00001756-200108080-00043 · Cited by 5
A dynamic model of learning in the septo-hippocampal system
Rokers, B., Myers, C. E., & Gluck, M. A. (2000) · Neurocomputing · doi:10.1016/s0925-2312(00)00205-8 · Cited by 5
Still Looking for the Memories: Molecules and Synaptic Plasticity
Bhalla, U. S. (2013) · Springer eBooks · doi:10.1007/978-1-4614-1424-7_9 · Cited by 4
Dynamical Modulation of Hippocampal Replay Sequences through Firing Rate AdaptationPreprint
Ji, Z., Chu, T., Dong, X., Yu, C., Bush, D., Burgess, N., & Wu, S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.13.612895 · Cited by 4
“…dopamine, and noradrenaline across different brain states [Samanta et al., 2020], which potentially affect firing rate adaptation [Yoshida et al., 2013, Barkai and Hasselmo, 1994, Madison and Nicoll, 1984], we examine how replay dynamics vary within the same animal during different brain states.”
BackgroundDynamical modulation of hippocampal replay through firing rate adaptation
Ji, Z., Chu, T., Dong, X., Yu, C., Bush, D., Burgess, N., & Wu, S. (2026) · Nature Communications · doi:10.1038/s41467-025-68042-3 · Cited by 12
Impact of optogenetic pulse design on CA3 learning and replay: A neural model
Wilmerding, L. K., Yazdanbakhsh, A., & Hasselmo, M. E. (2022) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100208 · Cited by 4
“, 2008) and other cortical structures (Barkai and Hasselmo, 1994).”
BackgroundInfluentialSelf-citationModulatory Influences on the Hippocampus and Entorhinal Cortex
Gupta, K., & Hasselmo, M. E. (2014) · Springer eBooks · doi:10.1007/978-3-7091-1292-2_7 · Cited by 3
Self-citationBergman, R. E., Vanier, M. C., Horwitz, G. D., Bower, J. M., & Hasselmo, M. E. (1993) · Springer eBooks · doi:10.1007/978-1-4615-3254-5_42 · Cited by 3
Self-citationHasselmo, M. E., & Linster, C. (1999) · Cerebral cortex · doi:10.1007/978-1-4615-4903-1_11 · Cited by 3
Self-citationBolding, K. A., Nagappan, S., Han, B.-X., Wang, F., & Franks, K. M. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/694331 · Cited by 3
“However, these and other major observations regarding associative functions and odor-evoked activation patterns in PCx were obtained in either ex vivo 28,29 or anesthetized preparations 20,30 .”
BackgroundA generalized neuronal activation function derived from ion-channel characteristics
Cartling, B. G. (1995) · Network Computation in Neural Systems · doi:10.1088/0954-898x/6/3/005 · Cited by 3
Mednikova, Y. S., Karnup, S. V., & Zhadin, M. N. (2003) · Neuroscience and Behavioral Physiology · doi:10.1023/a:1023930703926 · Cited by 3
Effects of muscarinic and nicotinic receptors on contextual modulation in macaque area V1
Herrero, J. L., & Thiele, A. (2021) · Scientific Reports · doi:10.1038/s41598-021-88044-7 · Cited by 2
Apprentissage olfactif: les systèmes de mémoire chez le rat
Chaillan, F. A., Roman, F. S., & Soumireu-Mourat, B. (1998) · Comptes Rendus de l Académie des Sciences - Series III - Sciences de la Vie · doi:10.1016/s0764-4469(97)89815-0 · Cited by 2
The Functional Roles of Attention
Kentridge, R. W., & Brogaard, B. “. (2017) · Routledge eBooks · doi:10.4324/9781315733029-14 · Cited by 2
A Model of Changes in Inferotemporal Activity during a Delayed Match-To-Sample Task
Sohal, V. S., & Hasselmo, M. E. (1997) · Springer eBooks · doi:10.1007/978-1-4757-9800-5_131 · Cited by 2
Self-citationShao, K., Ramírez-Villegas, J. F., Logothetis, N. K., & Besserve, M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.16.432817 · Cited by 2
“As 510 for the cortical ’critical parameters’, we followed their choice of the adaptation strength of the 511 sodium-dependent potassium current ḡKNa, but changed the other to the maximum firing rate of 512 Pyr neurons Qmax p (Barkai and Hasselmo, 1994; Disney et al., 2007; Soma et al., 2012; Steriade et al., 513 2001…”
BackgroundFunction of the Olfactory Cortex
Wilson, D. A., & East, B. S. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-809324-5.23774-7 · Cited by 1
Майоров, В. И. (2003) · Neuroscience and Behavioral Physiology · doi:10.1023/a:1023978619856 · Cited by 1
“Since it is dif ficult to evaluate the volume in which ions entering cells are distributed, changes in calcium concentration [Ca] are generally determined “with a precision up to the coefficient of proportionality”[ 8 , 15, 17, 22, 23] (see, however, [12, 24]), to obtain appropriate dynamics of ion currents dependent…”
BackgroundFunctional Magnetic Resonance Imaging and Computational Modeling
Stern, C. E., & Hasselmo, M. E. (1997) · Springer eBooks · doi:10.1007/978-1-4757-9800-5_133 · Cited by 1
Self-citationConditional Activation for Diverse Neurons in Heterogeneous NetworksPreprint
Lee, A., Lam, B., Li, W., Lee, H., Chen, W. H., Chang, M.-F., & Wang, K. L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.05006 · Cited by 2
“Chemical messages such as neurotransmitters and neuromodulators target receptors on a neuron leading to changes in various features [15][16].”
BackgroundComputation in the Olfactory System
Linster, C. (2017) · doi:10.1002/9781119159193.ch14 · Cited by 1
An Analysis of the Adaptive Behavior of Piriform Cortex Pyramidal Cells
Crook, S. M., & Ermentrout, G. B. (1997) · Springer eBooks · doi:10.1007/978-1-4757-9800-5_11 · Cited by 1
“Barkai and Hasselmo (1994) describe the adaptation characteristics of represen-tative layer II pyramidal cells from piriform cortex, provide a measure for classifyingthese cells as showing either strong or weak adaptation, and develop a biophysicalmodel to demonstrate the adaptation characteristics…”
BackgroundSohal, V. S., & Hasselmo, M. E. (1998) · Springer eBooks · doi:10.1007/978-1-4615-4831-7_87
Self-citationModeling of Olfactory Processing
Linster, C., & Cleland, T. A. (2008) · Elsevier eBooks · doi:10.1016/b978-012370880-9.00121-3
Hasselmo, M. E., & Cekić, M. (1995)
Self-citationSimulação computacional do sistema olfativo de vertebrados.
de Souza, F. M. S. (2002) · doi:10.11606/d.59.2002.tde-14092002-092930
Neural Unit Sensitive to Modulation
Anatoli Gorchetchnikov, A., & Cripps, A. (2000) · Perspectives in neural computing · doi:10.1007/978-1-4471-0513-8_36
Ghosh, A. (2016) · Memorial University Research Repository (Memorial University)
Ljubojevic, V. (2015) · TSpace (University of Toronto)
Olfactory Cortical Associative Memory Models
de Almeida, L. (2013) · Springer eBooks · doi:10.1007/978-1-4614-7320-6_618-3
Ljubojevic, V. (2010) · TSpace (University of Toronto)
A Morpholino Strategy to Assess TSC Gene Function in Zebrafish
Baraban, S. C. (2006) · Defense Technical Information Center (DTIC)
Modeling of Olfactory Processing
Linster, C., & Cleland, T. A. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-809324-5.24153-9
Olfactory Cortical Associative Memory Models
de Almeida, L. (2022) · Springer eBooks · doi:10.1007/978-1-0716-1006-0_618
Linster, C., Wolf, D., & Kelsch, W. (2025) · eNeuro · doi:10.1523/eneuro.0143-25.2025 · Cited by 1
“The internal dynamics of pyramidal cells (Barkai and Hasselmo, 1994; McGinley and Westbrook, 2011), paired with enhanced intracortical interactions in response to odor-induced plasticity, leads to the emergence of beta oscillations, which have been observed in the olfactory networks after odor…”
Kalabushev, S. N., Воронков, Д. Н., & Mednikova, Y. S. (2025) · BIOPHYSICS · doi:10.1134/s0006350925700344
“The same type of activation is characteristic of stimulation of neurons by electric current [3, 4] and for the action of other dicarboxylic amino acids.”
Leroux, M. (2023) · theses.fr (ABES) · doi:10.70675/d51b586bz96daz4ecbzbac2zc54c3576aef2
Hasselmo, M. E. (2013) · Springer eBooks · doi:10.1007/978-1-4614-1424-7_10
Self-citationDistribution of mRNAs encoding CRF receptors in brain and pituitary of rat and mouse
Van Pett, K., Viau, V., Bittencourt, J. C., Chan, R. K. W., Li, H.-Y., Arias, C., Prins, G. S., Perrin, M., Vale, W., & Sawchenko, P. E. (2000) · The Journal of Comparative Neurology · doi:10.1002/1096-9861(20001211)428:2
Metadata
- Authors
- Barkai, E., & Hasselmo, M. E.
- Affiliations
- Harvard University (US)
- Venue
- Journal of Neurophysiology
- Publisher
- American Physiological Society
- Published
- 1994-08-01
- Volume
- 72
- Issue
- 2
- Pages
- 644-658
- ISSN
- 0022-3077 (ISSN-L)
- 1522-1598
- OpenAlex
- W66960383
- Cited by
- 158
- References
- 0
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 4 · 2023: 2 · 2022: 3 · 2021: 5 · 2020: 3 · 2019: 4 · 2018: 1 · 2017: 2 · 2016: 5 · 2015: 5 · 2014: 3 · 2013: 10 · 2012: 3
- FWCI
- 2.18
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Neurophysiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Endocrine and Autonomic Systems › Neuroscience of respiration and sleep
- Keywords
- piriform cortex, pyramidal neurons, cholinergic modulation, carbachol, neural adaptation, input-output function, repetitive firing, action potential frequency, current injection, layer II
- MeSH terms
- Acetylcholine, Afferent Pathways, Animals, Association Learning, Cerebral Cortex, Cholinergic Fibers, Electric Stimulation, Female, Membrane Potentials, Nerve Net, Neural Inhibition, Synaptic Transmission, Neurons, Olfactory Pathways, Mental Recall, Neural Networks, Computer, Rats, Sprague-Dawley, Pyramidal Cells, Rats
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