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Spiny stellate cells. A, Light micrograph of spiny stellate cell surrounded by dLGN axons. B, LM reconstruction of the spiny stellate cell in A, with dendrites in yellow and contacts with the dLGN boutons and axons represented by red dots. The white dots are all of the dLGN boutons judged by LM that surrounded the neuron's dendrites. This neuron had a simple receptive field, 0.25° 0.1° (length width), direction preference, and ocular dominance group 2. C-E, LM reconstruction of the dendrite and soma (black) of the remaining three spiny stellate neurons, with LM contacts between dendrites and dLGN axons and boutons shown in red. Layers (3, 4, 5) are shown to the left. F-H, Outline of coronal sections with arrows indicating the location of each neuron; outlined in red are the contralateral axonal patches in layer 4 and the regions of weak thalamic labeling are outlined in blue, representing the ocular dominance patch of the ipsilateral eye. C, G, Neuron Cat1804 -P4C4 responded preferentially to the ipsilateral eye. D, G, Neuron Cat1804 -P3C4. E, H, Neuron Cat2003-S28C1. F, Neuron Cat0904 -P4C2 responded preferentially to the contralateral eye.
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In the cat's visual cortex, the responses of simple cells seem to be totally determined by their thalamic input, yet only a few percent of the excitatory synapses in layer 4 arise from the thalamus. To resolve this discrepancy between structure and function, we used correlated light and electron microscopy to search individual spiny stellate cells...
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... Labeled axons were found in areas 17 and 18, where they formed arbors mostly in layers 4 and 6. Axons labeled by dLGN injections always formed patches in area 17 that represent the contralateral eye. As documented previously ( LeVay et al., 1978), some thalamic boutons were also found in the adjacent regions representing the ipsilateral eye (Fig. 1). Part of the ipsilateral label is attributable to thalamic boutons formed between ocular dominance clusters (Freund et al., 1985a;Humphrey et al., 1985), and in some cases also to spillover of the injection site to lamina ...
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... made extracellular and intracellular recordings from the four orientation-tuned SSs, and in three cases plotted their RFs. The neuron shown in Figure 1 A, B (C0904P4C2) had a simple receptive field that is typical of SSs ( Martin and Whitteridge, 1984), and its ocular dominance matched the SS's location in a cluster of dLGN axons representing the contralateral eye. A SS dominated by the ipsilateral eye is shown in Figure 1C; as expected for its ocular dominance, it lies outside the main cluster of labeled dLGN axons. ...
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... neuron shown in Figure 1 A, B (C0904P4C2) had a simple receptive field that is typical of SSs ( Martin and Whitteridge, 1984), and its ocular dominance matched the SS's location in a cluster of dLGN axons representing the contralateral eye. A SS dominated by the ipsilateral eye is shown in Figure 1C; as expected for its ocular dominance, it lies outside the main cluster of labeled dLGN axons. ...
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... SS whose dendrites had the most LM contacts from the dLGN axons is shown in Figure 1 A. A reconstruction of the same neuron is shown embedded in the cloud of all the labeled dLGN boutons (white dots) that surrounded it (Fig. 1 B). The sites of LM contacts with the dendrite of the SS are indicated by the red dots. ...
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... SS whose dendrites had the most LM contacts from the dLGN axons is shown in Figure 1 A. A reconstruction of the same neuron is shown embedded in the cloud of all the labeled dLGN boutons (white dots) that surrounded it (Fig. 1 B). The sites of LM contacts with the dendrite of the SS are indicated by the red dots. The locations of the dLGN LM contacts with the remaining SSs are also shown in Figure 1 and in the dendrograms of Figure 3A. LM contacts were made with all the dendrites of all the neurons both proximally and distally. There was no apparent preference ...
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... sites of LM contacts with the dendrite of the SS are indicated by the red dots. The locations of the dLGN LM contacts with the remaining SSs are also shown in Figure 1 and in the dendrograms of Figure 3A. LM contacts were made with all the dendrites of all the neurons both proximally and distally. ...
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... all of the spine LM contacts. But which of the unverified contacts are synapses? We avoided this problem by generating all possible combinations of 55% of the unverified LM spine contacts. Each of these Table 1. A, First, we calculate the total number of LM contacts (red dots, n c ) with the dendrites (yellow) of the spiny stellate cell (see also Fig. 1A). B, Second, we verify what the probability is of an LM contact to be an EM-verified synapse (see also Figs. 4, 5). C, Third, we find what the density is of dLGN synapses in the vicinity of the labeled spiny stellate dendrite using the physical disector method. Shown is a disector counting frame. Synapses present in both the reference ...
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Citations
... Previous studies have computed proximities from skeletons of simulated models (Udvary et al., 2022), or man-ually traced data (Mishchenko et al., 2010;Kasthuri et al., 2015), with a similar logic. Proximities are necessary but not sufficient for the formation of a synapse (Peters and Feldman, 1976;Brown and Hestrin, 2009;Mishchenko et al., 2010;Costa and Martin, 2011), and so the "proximity graph" can serve as a valuable null distribution for comparing potential connectivity with synaptic connectivity between neurons: Instead of looking at synapse counts between cells which are dependent on the geometry and completeness of the neuropil, proximities make it possible to calculate "conversion rates"the fraction of proximities which resulted in actual synaptic connections. NEURD also provides functions to compute presyn skeletal walk -the distance from a synapse to the soma of the presynaptic neuron along the axon, and and postsyn skeletal walk -the distance from synapse to soma along the postsynaptic dendrite. ...
We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution. Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML). Automated segmentation methods can now yield exceptionally accurate reconstructions of cells, but despite this accuracy, laborious post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons produced by these segmentations contain detailed morphological information, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting information about these features can require substantial effort to piece together existing tools into custom workflows. Building on existing open-source software for mesh manipulation, here we present "NEURD", a software package that decomposes each meshed neuron into a compact and extensively-annotated graph representation. With these feature-rich graphs, we implement workflows for state-of-the art automated post-hoc proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and other features that can enable many downstream analyses of neural morphology and connectivity. NEURD can make these new massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.
... Note that the derivation of Eq. (22) assumes only a to be small and does not depend on the scaling relation between a and K. On the other hand, the fact that CV K * increases linearly with a makes the state emerging in networks of conductance-based neurons with a ~ 1/ log(K) significantly more robust to connection fluctuations than that emerging with a ~ K −α , for which CV K * K −α , and with current-based neurons, where CV K * 1/ K [56]. Note that, while in randomly connected networks CV K 1/ K, a larger degree of heterogeneity is observed in cortical networks [50,[56][57][58][59][60][61][62]. Our results show that networks of conductancebased neurons could potentially be much more robust to such heterogeneities than networks of current-based neurons. ...
... However, in networks with structural heterogeneity, with connection heterogeneity larger than 1/ K, the variability in mean input currents produces significant departures from the asynchronous irregular state, with large fractions of neurons that become silent or fire regularly [56]. This problem is relevant in cortical networks [56], where significant heterogeneity of in-degrees has been reported [50,[57][58][59][60][61][62], and different mechanisms have been proposed to solve it [56]. Here, we showed that networks of conductance-based neurons also generate irregular activity without any need for fine-tuning and, furthermore, can support irregular activity with substantial structural heterogeneity, up to the order of 1/ log(K). ...
Cortical neurons are characterized by irregular firing and a broad distribution of rates. The balanced state model explains these observations with a cancellation of mean excitatory and inhibitory currents, which makes fluctuations drive firing. In networks of neurons with current-based synapses, the balanced state emerges dynamically if coupling is strong, i.e., if the mean number of synapses per neuron K is large and synaptic efficacy is of the order of 1/sqrt[K]. When synapses are conductance-based, current fluctuations are suppressed when coupling is strong, questioning the applicability of the balanced state idea to biological neural networks. We analyze networks of strongly coupled conductance-based neurons and show that asynchronous irregular activity and broad distributions of rates emerge if synaptic efficacy is of the order of 1/log(K). In such networks, unlike in the standard balanced state model, current fluctuations are small and firing is maintained by a drift-diffusion balance. This balance emerges dynamically, without fine-tuning, if inputs are smaller than a critical value, which depends on synaptic time constants and coupling strength, and is significantly more robust to connection heterogeneities than the classical balanced state model. Our analysis makes experimentally testable predictions of how the network response properties should evolve as input increases.
... Excitatory and inhibitory interneurons are locally [26] and based on this fact, we consider an excitatory and an inhibitory connection between excitatory interneurons and fast inhibitory interneurons. Thalamus is globally connected to the excitatory interneurons by the thalamic relay nucleus, and the thalamic relay nucleus is regarded as an excitatory population [27]. Here, we consider an excitatory connection from the thalamic relay nucleus population to the excitatory interneurons. ...
Growing evidence suggests that excitatory neurons in the brain play a significant role in seizure generation. Nonetheless, spiny stellate cells are cortical excitatory non-pyramidal neurons in the brain which their basic role in seizure occurrence is not well understood. In the present research, we study the critical role of spiny stellate cells or the excitatory interneurons (EI), for the first time, in epileptic seizure generation using an extended neural mass model inspired by Liu and Wang model in 2017. Applying bifurcation analysis on this modified model, we investigated the rich dynamics corresponding to the epileptic seizure onset and transition between interictal and ictal states caused by EI. Our results indicate that the transition between states is corresponding to a supercritical Hopf bifurcation which shapes the preictal activity in the model and suggests why before seizure onset, the amplitude and frequency of neural activities increase gradually. Moreover, we showed that 1) the altered function of GABAergic and glutamatergic receptors of EI can cause seizure, and 2) the pathway between the thalamic relay nucleus and EI facilitates the transition from interictal to the ictal activity by decreasing the preictal period. Thereafter, we considered both sensory and cortical periodic inputs to study model responses to various harmonic stimulations. Bifurcation analysis of the model in this case suggests that the initial stage of the brain might be the main cause for the transition between interictal and ictal states as the stimulus frequency changes. The extended thalamocortical model shows also that the amplitude jump phenomenon and nonlinear resonance behavior result from the preictal stage of the brain. These results can be considered as a step forward to a deeper understanding of the mechanisms underlying the transition from normal brain activities to epileptic activities.
... This process was repeated for 37 Gabor patches rotated from 0 • to 180 • at steps of 5 • . The model has been then sized considering real data from vision literature [55][56][57], as summarized in Tables 2 and 3 and detailed in Appendix B. ...
... In detail, the retinotopic cells of LGN section merely receive one-to-one connections from the retina and connect to spiny stellate neurons (SS) in layer IV within V1 [55]. This was implemented using a strong input current of 15,000.0 ...
Since the first half of the twentieth century, numerous studies have been conducted on how
the visual cortex encodes basic image features. One of the hallmarks of basic feature extraction is
the phenomenon of orientation selectivity, of which the underlying neuronal-level computational
mechanisms remain partially unclear despite being intensively investigated. In this work we present
a reduced visual system model (RVSM) of the first level of scene analysis, involving the retina, the
lateral geniculate nucleus and the primary visual cortex (V1), showing orientation selectivity. The
detection core of the RVSM is the neuromorphic spike-decoding structure MNSD, which is able to
learn and recognize parallel spike sequences and considerably resembles the neuronal microcircuits of
V1 in both topology and operation. This structure is equipped with plasticity of intrinsic excitability to
embed recent findings about V1 operation. The RVSM, which embeds 81 groups of MNSD arranged
in 4 oriented columns, is tested using sets of rotated Gabor patches as input. Finally, synthetic visual
evoked activity generated by the RVSM is compared with real neurophysiological signal from V1
area: (1) postsynaptic activity of human subjects obtained by magnetoencephalography and (2)
spiking activity of macaques obtained by multi-tetrode arrays. The system is implemented using
the NEST simulator. The results attest to a good level of resemblance between the model response
and real neurophysiological recordings. As the RVSM is available online, and the model parameters
can be customized by the user, we propose it as a tool to elucidate the computational mechanisms
underlying orientation selectivity.
... 35% of synapses from Layer 4 cells were formed on the Layer 2/3 neurons. In addition, layer 4 cells received on average 10 additional thalamo-cortical synapses 96 . The synapses were drawn probabilistically with replacement (with functional and geometrical biases described below). ...
The neural encoding of visual features in primary visual cortex (V1) is well understood, with strong correlates to low-level perception, making V1 a strong candidate for vision restoration through neuroprosthetics. However, the functional relevance of neural dynamics evoked through external stimulation directly imposed at the cortical level is poorly understood. Furthermore, protocols for designing cortical stimulation patterns that would induce a naturalistic perception of the encoded stimuli have not yet been established. Here, we demonstrate a proof of concept by solving these issues through a computational model, combining (1) a large-scale spiking neural network model of cat V1 and (2) a virtual prosthetic system transcoding the visual input into tailored light-stimulation patterns which drive in situ the optogenetically modified cortical tissue. Using such virtual experiments, we design a protocol for translating simple Fourier contrasted stimuli (gratings) into activation patterns of the optogenetic matrix stimulator. We then quantify the relationship between spatial configuration of the imposed light pattern and the induced cortical activity. Our simulations in the absence of visual drive (simulated blindness) show that optogenetic stimulation with a spatial resolution as low as 100 μm, and light intensity as weak as 1016 photons/s/cm2 is sufficient to evoke activity patterns in V1 close to those evoked by normal vision.
... Note that the derivation of Eq. (11) only assumes a to be small and does not depend on the scaling relation between a and K. On the other hand, the fact that CV * K increases linearly with a makes the state emerging in networks of conductance-based neurons with a ∼ 1/ log(K) significantly more robust to connection fluctuations than that emerging with a ∼ K −α , for which CV * K ∼ K −α , and with current-based neurons, where CV * K ∼ 1/ √ K [45]. Note that, while in randomly connected networks CV K ∼ 1/ √ K, a larger degree of heterogeneity has been observed in cortical networks [39,[45][46][47][48][49]. Our results show that networks of conductance-based neurons could potentially be much more robust to such heterogeneities than networks of current-based neurons. ...
... However, in networks with structural heterogeneity, with connection heterogeneity larger than 1/ √ K, the variability in mean input currents produces significant departures from the asynchronous irregular state, with large fractions of neurons that become silent or fire regularly [45]. This problem is relevant in cortical networks [45], where significant heterogeneity of in-degrees as been reported [39,[46][47][48][49], and different mechanisms have been proposed to solve it [45]. Here we showed that networks of conductance-based neurons also generate irregular activity without any need for finite tuning, and furthermore can support irregular activity with substantial structural heterogeneity, up to order 1/ log(K). ...
Cortical neurons are characterized by irregular firing and a broad distribution of rates. The balanced state model explains these observations with a cancellation of mean excitatory and inhibitory currents, which makes fluctuations drive firing. In networks of neurons with current-based synapses, the balanced state emerges dynamically if coupling is strong, i.e. if the mean number of synapses per neuron $K$ is large and synaptic efficacy is of order $1/\sqrt{K}$. When synapses are conductance-based, current fluctuations are suppressed when coupling is strong, questioning the applicability of the balanced state idea to biological neural networks. We analyze networks of strongly coupled conductance-based neurons and show that asynchronous irregular activity and broad distributions of rates emerge if synapses are of order $1/\log(K)$. In such networks, unlike in the standard balanced state model, current fluctuations are small and firing is maintained by a drift-diffusion balance. This balance emerges dynamically, without fine tuning, if inputs are smaller than a critical value, which depends on synaptic time constants and coupling strength, and is significantly more robust to connection heterogeneities than the classical balanced state model. Our analysis makes experimentally testable predictions of how the network response properties should evolve as input increases.
... As the model neuron becomes more realistic, it deviates from this ideal trend due to noise sources within the model ( Fig. 9) but is still significant. Direct measurements of LGN axon synapses on spiny stellate cell dendrites confirm this configuration as realistic with a broad average around five synapses per axon per cell, mostly on separate dendritic branches (Peters and Payne, 1993;Alonso et al., 2001;da Costa and Martin, 2011). ...
Reliable timing of cortical spikes in response to visual events is crucial in representing visual inputs to the brain. Spikes in the primary visual cortex (V1) need to occur at the same time within a repeated visual stimulus. Two classical mechanisms are employed by the cortex to enhance reliable timing. First, cortical neurons respond reliably to a restricted set of stimuli through their preference for certain patterns of membrane potential due to their intrinsic properties. Second, intracortical networking of excitatory and inhibitory neurons induces lateral inhibition that, through the timing and strength of IPSCs and EPSCs, produces sparse and reliably timed cortical neuron spike trains to be transmitted downstream. Here, we describe a third mechanism that, through preferential thalamocortical synaptic connectivity, enhances the trial-to-trial timing precision of cortical spikes in the presence of spike train variability within each trial that is introduced between LGN neurons in the retino-thalamic pathway. Applying experimentally recorded LGN spike trains from the anesthetized cat to a detailed model of a spiny stellate V1 neuron, we found that output spike timing precision improved with increasing numbers of convergent LGN inputs. The improvement was consistent with the predicted proportionality of [Formula: see text] for n LGN source neurons. We also found connectivity configurations that maximize reliability and that generate V1 cell output spike trains quantitatively similar to the experimental recordings. Our findings suggest a general principle, namely intra-trial variability among converging inputs, that increases stimulus response precision and is widely applicable to synaptically connected spiking neurons.SIGNIFICANCE STATEMENT The early visual pathway of the cat is favorable for studying the effects of trial-to-trial variability of synaptic inputs and intra-trial variability of thalamocortical connectivity on information transmission into the visual cortex. We have used a detailed model to show that there are preferred combinations of the number of thalamic afferents and the number of synapses per afferent that maximize the output reliability and spike-timing precision of cortical neurons. This provides additional insights into how synchrony in thalamic spike trains can reduce trial-to-trial variability to produce highly reliable reporting of sensory events to the cortex. The same principles may apply to other converging pathways where temporally jittered spike trains can reliably drive the downstream neuron and improve temporal precision.
... This may reflect differences in the balance between cortical (mainly to apical dendrites) and thalamic (mainly to basal dendrites) inputs into layer III pyramidal neurons of these three primary areas. Again, the functional background of this morphological difference is unknown; however, due to the nearly absence of spiny stellate thalamorecipient neurons in layer IV of the A1 compared to the V1 and S1 (Smith and Populin, 2001;Staiger et al., 2004;da Costa and Martin, 2011) it was concluded that layer IIIb pyramidal neurons in A1 are the main recipients of thalamocortical inputs (Winer, 2011). Thus, longer distal basal dendrites may reflect this specific function. ...
Multisensory integration in primary auditory (A1), visual (V1), and somatosensory cortex (S1) is substantially mediated by their direct interconnections and by thalamic inputs across the sensory modalities. We have previously shown in rodents (Mongolian gerbils) that during postnatal development, the anatomical and functional strengths of these crossmodal and also of sensory matched connections are determined by early auditory, somatosensory, and visual experience. Because supragranular layer III pyramidal neurons are major targets of corticocortical and thalamocortical connections, we investigated in this follow-up study how the loss of early sensory experience changes their dendritic morphology. Gerbils were sensory deprived early in development by either bilateral sciatic nerve transection at postnatal day (P) 5, ototoxic inner hair cell damage at P10, or eye enucleation at P10. Sholl and branch order analyses of Golgi-stained layer III pyramidal neurons at P28, which demarcates the end of the sensory critical period in this species, revealed that visual and somatosensory deprivation leads to a general increase of apical and basal dendritic branching in A1, V1, and S1. In contrast, dendritic branching, particularly of apical dendrites, decreased in all three areas following auditory deprivation. Generally, the number of spines, and consequently spine density, along the apical and basal dendrites decreased in both sensory deprived and non-deprived cortical areas. Therefore, we conclude that the loss of early sensory experience induces a refinement of corticocortical crossmodal and other cortical and thalamic connections by pruning of dendritic spines at the end of the critical period. Based on present and previous own results and findings from the literature, we propose a scenario for multisensory development following early sensory loss.
... Irrespective of whether anterograde labeling is achieved by conventional tracers or vector-mediated reporter expression, the identification of terminal appositions under light microscopy remains a challenge because of their small size (Burette et al., 2015). The development of vectors that selectively label presynaptic terminals (e.g., synaptophysin-GFP or synaptophysin-mRuby) makes disambiguation of terminals from axons and fibers easier (Li et al., 2010;Oh et al., 2014;Beier et al., 2015;Lerner et al., 2015), but the fact remains that <50% of "close appositions" constitute functional synapses (Pilowsky et al., 1992;Descarries and Mechawar, 2000;da Costa and Martin, 2011), and so their presence needs to be interpreted with caution. In recent years a number of elegant approaches have been developed to circumvent this limitation, the ultimate of which is GRASP (GFP Reconstitution Across Synaptic Partners), a dual vector system where each vector drives the expression of one half of a GFP-derived dimer that only becomes fluorescent when both halves bind. ...
The mammalian nervous system is comprised of a seemingly infinitely complex network of specialized synaptic connections that coordinate the flow of information through it. The field of connectomics seeks to map the structure that underlies brain function at resolutions that range from the ultrastructural, which examines the organization of individual synapses that impinge upon a neuron, to the macroscopic, which examines gross connectivity between large brain regions. At the mesoscopic level, distant and local connections between neuronal populations are identified, providing insights into circuit-level architecture. Although neural tract tracing techniques have been available to experimental neuroscientists for many decades, considerable methodological advances have been made in the last 20 years due to synergies between the fields of molecular biology, virology, microscopy, computer science and genetics. As a consequence, investigators now enjoy an unprecedented toolbox of reagents that can be directed against selected subpopulations of neurons to identify their efferent and afferent connectomes. Unfortunately, the intersectional nature of this progress presents newcomers to the field with a daunting array of technologies that have emerged from disciplines they may not be familiar with. This review outlines the current state of mesoscale connectomic approaches, from data collection to analysis, written for the novice to this field. A brief history of neuroanatomy is followed by an assessment of the techniques used by contemporary neuroscientists to resolve mesoscale organization, such as conventional and viral tracers, and methods of selecting for sub-populations of neurons. We consider some weaknesses and bottlenecks of the most widely used approaches for the analysis and dissemination of tracing data and explore the trajectories that rapidly developing neuroanatomy technologies are likely to take.
... To simulate thalamocortical background input to the modelled cell, one excitatory synapse was placed on each compartment and the frequency of activation of each one of those synapses was adjusted according to the distance of that compartment to the soma (Fig 1B). This is equivalent in the NEURON simulation environment to modelling 470 spines distributed as a Gaussian with respect to their position in the dendritic tree [19] and randomly activated at 4 Hz (the average output frequency we measured in LGN cells [12]), but is less computationally intensive. Thalamocortical synapses were modelled with a conductance time course of the To study energetic efficiency at thalamocortical synapses, we adapted a model of a L4SS cell [15]. ...
... (B) Corticocortical input density (red and blue) for each type of synapse per μm 2 of dendrite, as a function of distance from the soma over all dendrites. Thalamocortical input density followed a Gaussian probability distribution with respect to distance from the soma (orange; Gaussian with a mean of 83.6 μm and a standard deviation of 28.3 μm, transformed to show density per μm 2 ; [19]). (C) The individual synaptic conductance values were the same for all synapses in one class. ...
... To evaluate the contribution of an individual thalamic cell projecting onto the modelled cell, three synapses were added, clustered onto the same dendritic branch, approximately 78 μm away from the soma [19] and typically spaced by 18 μm (see Fig 1A and 1B for details; located on compartments a3_122, a3_121 and a3_12 at positions 0.5, 0.2 and 0.9 respectively, see file l4sscell.hoc in https://senselab.med.yale.edu/ModelDB/showmodel.cshtml?model= 146565 for details). These synapses were modelled using the same parameters as for other thalamocortical synapses. ...
We have previously shown that the physiological size of postsynaptic currents maximises energy efficiency rather than information transfer across the retinothalamic relay synapse. Here, we investigate information transmission and postsynaptic energy use at the next synapse along the visual pathway: from relay neurons in the thalamus to spiny stellate cells in layer 4 of the primary visual cortex (L4SS). Using both multicompartment Hodgkin-Huxley-type simulations and electrophysiological recordings in rodent brain slices, we find that increasing or decreasing the postsynaptic conductance of the set of thalamocortical inputs to one L4SS cell decreases the energy efficiency of information transmission from a single thalamocortical input. This result is obtained in the presence of random background input to the L4SS cell from excitatory and inhibitory corticocortical connections, which were simulated (both excitatory and inhibitory) or injected experimentally using dynamic-clamp (excitatory only). Thus, energy efficiency is not a unique property of strong relay synapses: even at the relatively weak thalamocortical synapse, each of which contributes minimally to the output firing of the L4SS cell, evolutionarily-selected postsynaptic properties appear to maximise the information transmitted per energy used.
