Network architecture underlying maximal separation of neuronal representations

Jortner, Ron A

doi:10.3389/fneng.2012.00019

Cited by 10 publications

(12 citation statements)

References 112 publications

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“…The output from the antennal lobe diverges widely to an array of 15,000 model KCs. This pattern of connectivity has been hypothesized to help decrease the overlap between odor representations [24,25]. KC output then converges onto a small group of MBONs.…”

Section: Resultsmentioning

confidence: 99%

Optimality of sparse olfactory representations is not affected by network plasticity

2020

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The neural representation of a stimulus is repeatedly transformed as it moves from the sensory periphery to deeper layers of the nervous system. Sparsening transformations are thought to increase the separation between similar representations, encode stimuli with great specificity, maximize storage capacity of associative memories, and provide an energy efficient instantiation of information in neural circuits. In the insect olfactory system, odors are initially represented in the periphery as a combinatorial code with relatively simple temporal dynamics. Subsequently, in the antennal lobe this representation is transformed into a dense and complex spatiotemporal activity pattern. Next, in the mushroom body Kenyon cells (KCs), the representation is dramatically sparsened. Finally, in mushroom body output neurons (MBONs), the representation takes on a new dense spatiotemporal format. Here, we develop a computational model to simulate this chain of olfactory processing from the receptor neurons to MBONs. We demonstrate that representations of similar odorants are maximally separated, measured by the distance between the corresponding MBON activity vectors, when KC responses are sparse. Sparseness is maintained across variations in odor concentration by adjusting the feedback inhibition that KCs receive from an inhibitory neuron, the Giant GABAergic neuron. Different odor concentrations require different strength and timing of feedback inhibition for optimal processing. Importantly, as observed in vivo, the KC-MBON synapse is highly plastic, and, therefore, changes in synaptic strength after learning can change the balance of excitation and inhibition, potentially leading to changes in the distance between MBON activity vectors of two odorants for the same level of KC population sparseness. Thus, what is an optimal degree of sparseness before odor learning, could be rendered sub-optimal post learning. Here, we show, however, that synaptic weight changes caused by spike timing dependent plasticity increase the distance between the odor representations from the perspective of MBONs. A level of sparseness that was optimal before learning remains optimal post-learning.

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Section: Resultsmentioning

confidence: 99%

Optimality of sparse olfactory representations is not affected by network plasticity

2020

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“…The output from the antennal lobe diverged widely to an array of 15,000 model KCs. This pattern of connectivity has been hypothesized to help decrease the overlap between odor representations (18,19). KC output then converged onto a small group of MBONs.…”

Section: Resultsmentioning

confidence: 99%

Optimal sparse olfactory representations persist in a plastic network

Assisi

Stopfer

Bazhenov

2019

Preprint

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18The neural representation of a stimulus is repeatedly transformed as it moves from 19 the sensory periphery to deeper layers of the nervous system. Sparsening 20 transformations are thought to increase the separation between similar 21 representations, encode stimuli with great specificity, maximize storage capacity as 22 weight changes caused by spike timing dependent plasticity increase the distance 42 between the odor representations from the perspective of MBONs and do not lead to 43 a concomitant change in the optimal sparseness. 44 45 Author Summary 46 Kenyon cells (KCs) of the mushroom body represent odors as a sparse code. When 47 viewed from the perspective of follower neurons, mushroom body output neurons 48 (MBONs), an optimal level of KC sparseness maximally separates the 49 representations of odors. However, the KC-MBON synapse is highly plastic and may 50 be potentiated or depressed by odor-driven experience that could, in turn, perturb 51 the optimality formed by pre-synaptic circuits. Contrary to this expectation, we show 52 that synaptic plasticity based on spike timing of pre-and postsynaptic neurons 53 improves the ability of the system to distinguish between the representations of 54 similar odors while preserving the optimality determined by pre-synaptic circuits. 55 56 representations? Second, if an optimal sparseness exists, does plasticity at the KC-131 MBON synapse alter it, making post-learning odor representations sub-optimal? To 132 address these questions, we constructed a computational model of the locust 133 olfactory system consisting of the olfactory receptor neurons, the antennal lobe 134 network of PNs and local inhibitory interneurons, the KCs of the mushroom body, 135and a layer of MBONs (Figure 1a). The model antennal lobe network generates 136 many of the key responses previously documented in the locust (16,17). The output

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“…Analytical model. The analytical model was based on the framework provided by Jortner 75 , using binary responses for PNs and KCs, and a binary matrix to represent the PN-KC connections. A KC is considered to have a response if its net input crosses a threshold.…”

Section: Methodsmentioning

confidence: 99%

Multiple network properties overcome random connectivity to enable stereotypic sensory responses

et al. 2020

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Connections between neuronal populations may be genetically hardwired or random. In the insect olfactory system, projection neurons of the antennal lobe connect randomly to Kenyon cells of the mushroom body. Consequently, while the odor responses of the projection neurons are stereotyped across individuals, the responses of the Kenyon cells are variable. Surprisingly, downstream of Kenyon cells, mushroom body output neurons show stereotypy in their responses. We found that the stereotypy is enabled by the convergence of inputs from many Kenyon cells onto an output neuron, and does not require learning. The stereotypy emerges in the total response of the Kenyon cell population using multiple odor-specific features of the projection neuron responses, benefits from the nonlinearity in the transfer function, depends on the convergence:randomness ratio, and is constrained by sparseness. Together, our results reveal the fundamental mechanisms and constraints with which convergence enables stereotypy in sensory responses despite random connectivity.

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Network architecture underlying maximal separation of neuronal representations

Cited by 10 publications

References 112 publications

Optimality of sparse olfactory representations is not affected by network plasticity

Optimality of sparse olfactory representations is not affected by network plasticity

Optimal sparse olfactory representations persist in a plastic network

Multiple network properties overcome random connectivity to enable stereotypic sensory responses

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