Multilaminar networks of cortical neurons integrate common inputs from sensory thalamus

Morgenstern, Nicolás A.; Bourg, Jacques; Petreanu, Leopoldo

doi:10.1038/nn.4339

Cited by 65 publications

(83 citation statements)

References 49 publications

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“…Combining this method with laser photostimulation of remote slice loci, in rat V1, it was possible to show that pairs of connected pyramidal cells in layer 2/3 had a greater probability of receiving joint input from any given stimulation site in layer 4, and/or from nearby sites in layer 2/3, compared to unconnected pairs (Yoshimura et al, 2005). In a similar vein (and as discussed in Figure 4B ) geniculate afferents are also more likely to co-terminate on connected pairs of pyramidal neurons in layers 4 and 3 (Morgenstern et al, 2016). Together these studies imply the existence of separate, mutually exclusive translaminar networks, although they are blind toward the featural specificity of any such network.…”

Section: Recurrent Processing Within the Forward Pathwaymentioning

confidence: 65%

“…Each form of linear summation of prediction error signals, here from the LGN (refer to Section 9) is germane to the synthesis of an expectation unit, consistent with the prevalence of simple oriented receptive fields in layer 4 of mouse V1 (Niell and Stryker, 2008). Terminal arborisations of single geniculate axons favor contacts with connected excitatory neuron pairs in layers 4 and 3: this is true for presynaptic/postsynaptic pairs in L4/L4 and L4/L3, but not L3/L3; L3/L4 connected pairs are absent (Morgenstern et al, 2016). The modified gPC template shown here interprets the L4/L4 pair receiving joint input as communicating expectation units, and the L4/L3 pair as an input error unit connecting to an expectation unit.…”

Section: The Forward Pathway Through Layer 4 and Layermentioning

confidence: 99%

“…The modified gPC template ( Figure 4B ) requires neither of the onward circuit elements to show excitatory (pyramid to-pyramid) feedback which, as noted above, is negligible in layer 3-to-4 transmission (Thomson and Lamy, 2007; Morgenstern et al, 2016) – an important criterion for recognizing layer 4 as a laminar functional subunit (Shipp, 2007). Now, given the density of geniculate input to layer 4 and lower layer 3 (Kageyama et al, 1990; Morgenstern et al, 2016) and its consequent overlap with forward projecting output neurons (Johnson and Burkhalter, 1994; Petrof et al, 2012), the anatomy of rodent V1 raises the theoretical possibility of a minimal monosynaptic transmission in the forward pathway. This has yet to be demonstrated; the formulations of the gPC template in Figure 4B predict that the minimal forward transmission in the mouse is also disynaptic, or trisynaptic.…”

Section: The Forward Pathway Through Layer 4 and Layermentioning

confidence: 99%

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Neural Elements for Predictive Coding

2016

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Predictive coding theories of sensory brain function interpret the hierarchical construction of the cerebral cortex as a Bayesian, generative model capable of predicting the sensory data consistent with any given percept. Predictions are fed backward in the hierarchy and reciprocated by prediction error in the forward direction, acting to modify the representation of the outside world at increasing levels of abstraction, and so to optimize the nature of perception over a series of iterations. This accounts for many ‘illusory’ instances of perception where what is seen (heard, etc.) is unduly influenced by what is expected, based on past experience. This simple conception, the hierarchical exchange of prediction and prediction error, confronts a rich cortical microcircuitry that is yet to be fully documented. This article presents the view that, in the current state of theory and practice, it is profitable to begin a two-way exchange: that predictive coding theory can support an understanding of cortical microcircuit function, and prompt particular aspects of future investigation, whilst existing knowledge of microcircuitry can, in return, influence theoretical development. As an example, a neural inference arising from the earliest formulations of predictive coding is that the source populations of forward and backward pathways should be completely separate, given their functional distinction; this aspect of circuitry – that neurons with extrinsically bifurcating axons do not project in both directions – has only recently been confirmed. Here, the computational architecture prescribed by a generalized (free-energy) formulation of predictive coding is combined with the classic ‘canonical microcircuit’ and the laminar architecture of hierarchical extrinsic connectivity to produce a template schematic, that is further examined in the light of (a) updates in the microcircuitry of primate visual cortex, and (b) rapid technical advances made possible by transgenic neural engineering in the mouse. The exercise highlights a number of recurring themes, amongst them the consideration of interneuron diversity as a spur to theoretical development and the potential for specifying a pyramidal neuron’s function by its individual ‘connectome,’ combining its extrinsic projection (forward, backward or subcortical) with evaluation of its intrinsic network (e.g., unidirectional versus bidirectional connections with other pyramidal neurons).

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Section: Recurrent Processing Within the Forward Pathwaymentioning

confidence: 65%

Section: The Forward Pathway Through Layer 4 and Layermentioning

confidence: 99%

Section: The Forward Pathway Through Layer 4 and Layermentioning

confidence: 99%

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Neural Elements for Predictive Coding

2016

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“…Recent work has shown that cortical networks are more structured than simple Erdős-Rényi networks (e.g., [48–53]). One feature of cortical networks is a broad spread of neurons’ in- and out-degree distributions (i.e., the distributions of the number of synaptic inputs that each neuron receives or sends); another is broadly spread synaptic weights.…”

Section: Resultsmentioning

confidence: 99%

Linking structure and activity in nonlinear spiking networks

Ocker¹,

Josić²,

Shea‐Brown³

et al. 2017

PLoS Comput Biol

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Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of structure-driven activity has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing. Here, we overcome this limitation and present a new relationship between connectivity and activity in networks of nonlinear spiking neurons by developing a diagrammatic fluctuation expansion based on statistical field theory. We explicitly show how recurrent network structure produces pairwise and higher-order correlated activity, and how nonlinearities impact the networks’ spiking activity. Our findings open new avenues to investigating how single-neuron nonlinearities—including those of different cell types—combine with connectivity to shape population activity and function.

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“…This provided a measure of both the strength and location of monosynaptic CC inputs. We then compared the strength of monosynaptic FF or FB inputs in defined dendritic compartments in pairs of neurons projecting to different areas ( Figure 2E-G) 16,17,24,25 .…”

Section: Mapping the Cell-type Specificity Of CC Connectionsmentioning

confidence: 99%

Laminar-specific cortico-cortical loops in mouse visual cortex

Young

Belbut

Baeta

et al. 2019

Preprint

Self Cite

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Many theories propose recurrent interactions across the cortical hierarchy, but it is unclear if cortical circuits are selectively wired to implement looped computations. Using subcellular channelrhodopsin-2-assisted circuit mapping in mouse visual cortex, we compared feedforward (FF) or feedback (FB) cortico-cortical input to cells projecting back to the input source (looped neurons) with cells projecting to a different cortical or subcortical area (non-looped neurons). Despite having different laminar innervation patterns, FF and FB afferents showed similar cell-type selectivity, making stronger connections with looped neurons versus nonlooped neurons in layer (L) 5 and L6, but not in L2/3. FB inputs preferentially innervated the apical tufts of looped L5 neurons, but not their perisomatic dendrites. Our results reveal that interareal cortical connections are selectively wired into monosynaptic excitatory loops involving L6 and the apical dendrites of L5 neurons, supporting a role of these circuit elements in hierarchical recurrent computations. Results Neurons with different projection patterns are intermingled in visual areasPrimary visual cortex (V1), the lowest-order area of mouse visual cortex, sends FF projections to the higher-order lateromedial (LM) and anteromedial (AM) visual areas, which in turn reciprocate with FB connections [17][18][19] . Using dual injections of retrograde tracers, we measured the laminar distribution of different projection neurons in V1 and in LM (Figure 1). In each experiment, we compared the laminar distribution of LM-or V1-projecting IT neurons with either IT neurons projecting to AM, PT neurons projecting to the superior colliculus (SC), or CT neurons projecting to the dorsal lateral geniculate nucleus (dLGN) or to the lateral posterior nucleus (LP) of the thalamus. In each case, the different projection neurons were closely intermingled ( Figure 1A,B). In both V1 and LM, IT neurons were distributed across all layers except L1, including L4 19,20 , indicating that FF and FB projections originate from neurons spanning most of the cortical depth. In contrast, PT and CT neurons were confined to L5 and L5/6, respectively, as previously described 4 (Figure 1A,B). In both V1 and LM, we found double-labeled IT neurons in L2-6 after injecting tracers in two different cortical areas, indicating that subpopulations of ascending and descending projection neurons have diverging axons innervating more

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Multilaminar networks of cortical neurons integrate common inputs from sensory thalamus

Cited by 65 publications

References 49 publications

Neural Elements for Predictive Coding

Neural Elements for Predictive Coding

Linking structure and activity in nonlinear spiking networks

Laminar-specific cortico-cortical loops in mouse visual cortex

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