Physiology and anatomy of synaptic connections between thick tufted pyramidal neurones in the developing rat neocortex.

Markram, Henry; Lübke, Joachim H. R.; Frotscher, Michael; Roth, Arnd; Sakmann, Bert

doi:10.1113/jphysiol.1997.sp022031

Cited by 921 publications

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Section: Ionic Mechanisms That Regulate Spike Firing Threshold In Pfcmentioning

confidence: 99%

“…21, NO. 2 Prefrontal Dopamine Electrophysiology and Schizophrenia 171 rocally connected cells within the same cortical region, at least in primate PFC (Levitt et al 1993; Lewis and Anderson 1995; Kritzer and Goldman-Rakic et al 1995;Pucak et al 1996), and in rat somatosensory cortex (Thompson and Deuchars 1997;Markram 1997;Markram et al 1997b). If such an organization also exists within the prelimbic rat PFC, postsynaptic D1/5 receptor activation may bias layer V-VI neurons to respond preferentially to inputs from these cells within local reciprocally connected neighboring cortical neurons; for example, similar to those present in the PFC "stripes" of primates (Lewis and Anderson 1995;Pucak et al 1996).…”

mentioning

confidence: 99%

“…Such local activity, confined within the PFC, may be operational during working memory processing, because the PFC must rely on sustained firing to "hold" information in the absence of continuous presence of previously presented sensory cues (Amit 1995; Lewis and Anderson 1995; Seamans 1996 Durstewitz et al (1999). Paired-cell recordings (Thompson and Deuchars 1997;Markram 1997;Markram et al 1997b) from interacting pyramidal PFC neurons, and a study of the effects induced by dopamine are critically needed to test this hypothesis.It should be noted that, in addition to the postsynaptic D1/5 receptor effects, recent in vitro electrophysiolocial studies have shown that dopamine, or a D1/5 agonist (SKF81297), can also exert presynaptic inhibitions of glutamatergic layer V-VI afferents in some layer V-VI rat PFC pyramidal neurons (Law-Tho et al 1994;Yang et al 1996b; Gulledge and Jaffe 1996). It is not yet known under what in vivo conditions dopamine would activate presynaptic D1/5 receptors preferentially to supress excitatory layer V-VI afferent transmission, or under what other conditions dopamine would activate postsynaptic D1/5 receptors to augment depolarizing inputs.…”

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confidence: 99%

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Developing a Neuronal Model for the Pathophysiology of Schizophrenia Based on the Nature of Electrophysiological Actions of Dopamine in the Prefrontal Cortex

Yang

Seamans

Gorelova

1999

Neuropsychopharmacology

164

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This review covers some recent findings of the electrophysiological mechanisms through which mesocortical dopamine modulates prefrontal cortical neurons. Dopamine has been shown to modulate several ionic conductances located along the soma-dendritic axis of prefrontal cortical pyramidal neurons. These ionic currents include high-voltage-activated calcium currents and slowly inactivating NaReceived May 12, 1998; revised May 27, 1998; accepted May 29, 1998. 162 C.R. Yang et al. N EUROPSYCHOPHARMACOLOGY 1999 -VOL . 21 , NO Schizophrenia strikes one in one hundred people worldwide, regardless of cultural or racial origins. As the illness progresses and if it remains unattended, patients are frequently trapped in psychological, social, and economic devastation (Gottesman 1991;Jablensky 1995). Currently, our incomplete understanding of the neurobiological bases of schizophrenia suggests that defects in the genetic controls of brain development in such limbic regions (including temporal lobe structures such as hippocampus and the amygdala) as well as the prefrontal cortex (PFC) lead to cell loss or deformation, cytoarchitectural disorganization, and abnormal innervation in these brain regions (Roberts and Bruton 1990;Stevens 1992; Bogerts 1993;Shapiro 1993; Akbarian et al. 1993 Akbarian et al. , 1996Ross and Pearlson 1996;Weinberger 1996; Karayiorgou and Gogos 1997; Lewis 1997;Selemon et al. 1995Selemon et al. , 1998.Some results from recent imaging studies of brains from living schizophrenics have suggested that there are defective functional communications between the interconnected cortical (PFC and cingulate cortex) and limbic subcortical structures (thalamus, striatum, and temporal lobe limbic structures)(see reviews of Liddle 1996; Pfefferbaum and Marsh 1995; Andreasen 1997; Heckers et al. 1998). Findings from these studies suggest that in schizophrenics, abnormal recruitment of several interconnected cortical and subcortical structures may underlie such symptom clusters as psychomotor poverty, thought disorganization, and reality distortion (Liddle et al. 1992; Liddle 1996; Fletcher 1998; Heckers et al. 1998).As noted in Figure 1, the PFC receives converging limbic, association cortical, and mesocortical dopamine inputs. These inputs interact in the PFC and are involved functionally in high-level cognitive processes (Fuster 1995). Among the many brain regions that PFC output innervates, two important subcortical regions are emphasized in this review. These are the nucleus accumbens (where mesoaccumbens dopamine neurons terminate) and the ventral tegmental area (VTA, where the midbrain dopamine neurons reside) Groenewegen et al. 1990; Berendse et al. 1992a Berendse et al. , 1992bSesack and Pickel 1992;Gorelova and Yang 1997b). Several of the interconnected limbic, cortical, and subcortical structures known to be affected in schizophrenia are targets of the ascending midbrain dopamine systems that normally provide functional modulation of neurotransmission (Björklund and Lindvall 1984;Mogenson et ...

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Section: Ionic Mechanisms That Regulate Spike Firing Threshold In Pfcmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Developing a Neuronal Model for the Pathophysiology of Schizophrenia Based on the Nature of Electrophysiological Actions of Dopamine in the Prefrontal Cortex

Yang

Seamans

Gorelova

1999

Neuropsychopharmacology

164

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“…1d), and synaptic transmission is typically unreliable [10][11][12][13] . Furthermore, most of these dendritic inputs are not directly related to ongoing sensory stimulation; rather, they reflect spatiotemporally structured internal activity.…”

Section: Introductionmentioning

confidence: 99%

Regulation of spike timing in visual cortical circuits

2008

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A train of action potentials (a spike train) can carry information in both the average firing rate and the pattern of spikes in the train. But can such a spike-pattern code be supported by cortical circuits? Neurons in vitro produce a spike pattern in response to the injection of a fluctuating current. However, cortical neurons in vivo are modulated by local oscillatory neuronal activity and by top-down inputs. In a cortical circuit, precise spike patterns thus reflect the interaction between internally generated activity and sensory information encoded by input spike trains. We review the evidence for precise and reliable spike timing in the cortex and discuss its computational role.Reliability and precision are two different quantities. When you make an appointment with your friend, she can either keep the appointment or not show up at all. If she does show up, she might or might not be on time. The former uncertainty is related to reliability, whereas the latter is related to precision. When the same stimulus waveform is repeatedly injected at the soma of a neuron in vitro (FIG. 1a), a similar spike train is obtained on each trial 1,2 (FIG. 1b). When approximately the same number of spikes occur on each trial the neuron is said to be reliable, whereas when the spikes occur almost at the same time across trials it is said to be precise (FIG. 1c). For a single neuron, the potential information content of precise and reliable spike times is many times larger than that which is contained in the firing rate, which is averaged across a typical interval of a hundred milliseconds [3][4][5][6] . The information contained in spike timing is available immediately, rather than after an averaging period. Furthermore, the timing of patterns of spikes can potentially transmit even more information than the timing of the individual constituent spikes 3,7 . The potential relevance of spike patterns becomes apparent when we consider neurons at the population level: when a group of similar neurons (a 'pool') produces precise and reliable spike trains, the neurons they project to receive volleys of synchronous spikes 8,9 . This opens up the possibility of communicating between different cortical areas through synchronous spike volleys.In contrast to the in vitro situation described above, in the intact cortex most excitatory synaptic inputs arrive at the dendrites rather than at the soma (FIG. 1d), and synaptic transmission is typically unreliable [10][11][12][13] . Furthermore, most of these dendritic inputs are not directly related to ongoing sensory stimulation; rather, they reflect spatiotemporally structured internal activity. Therefore, when the same stimulus is presented repeatedly, the resulting spike trains are usually neither precise nor reliable when they are aligned to the stimulus onset 6 . Instead, HHMI Author ManuscriptHHMI Author Manuscript HHMI Author Manuscript neural activity in vivo might be dominated by internally generated complex reverberations or rhythmic oscillations, and precise and reliable sp...

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“…Cells were targeted under infrared differential intereference contrast microscopy and recorded in the current clamp mode using the patch clamp technique. For full experimental procedure see Markram et al (1997). Voltage responses current injection were recorded from the cells.…”

Section: Electrophysiological Recordingsmentioning

confidence: 99%

Evaluating automated parameter constraining procedures of neuron models by experimental and surrogate data

et al. 2008

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Neuron models, in particular conductance-based compartmental models, often have numerous parameters that cannot be directly determined experimentally and must be constrained by an optimization procedure. A common practice in evaluating the utility of such procedures is using a previously developed model to generate surrogate data (e.g., traces of spikes following step current pulses) and then challenging the algorithm to recover the original parameters (e.g., the value of maximal ion channel conductances) that were used to generate the data. In this fashion, the success or failure of the model fitting procedure to find the original parameters can be easily determined. Here we show that some model fitting procedures that provide an excellent fit in the case of such model-to-model comparisons provide ill-balanced results when applied to experimental data. The main reason is that surrogate and experimental data test different aspects of the algorithm's function. When considering model-generated surrogate data, the algorithm is required to locate a perfect solution that is known to exist. In contrast, when considering experimental target data, there is no guarantee that a perfect solution is part of the search space. In this case, the optimization procedure must rank all imperfect approximations and ultimately select the best approximation. This aspect is not tested at all when considering surrogate data since at least one perfect solution is known to exist (the original parameters) making all approximations unnecessary. Furthermore, we demonstrate that distance functions based on extracting a set of features from the target data (such as time-to-firstspike, spike width, spike frequency, etc.)-rather than using the original data (e.g., the whole spike trace) as the target for fitting-are capable of finding imperfect solutions that are good approximations of the experimental data.

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Physiology and anatomy of synaptic connections between thick tufted pyramidal neurones in the developing rat neocortex.

Cited by 921 publications

References 50 publications

Developing a Neuronal Model for the Pathophysiology of Schizophrenia Based on the Nature of Electrophysiological Actions of Dopamine in the Prefrontal Cortex

Developing a Neuronal Model for the Pathophysiology of Schizophrenia Based on the Nature of Electrophysiological Actions of Dopamine in the Prefrontal Cortex

Regulation of spike timing in visual cortical circuits

Evaluating automated parameter constraining procedures of neuron models by experimental and surrogate data

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