Optimality of sparse olfactory representations is not affected by network plasticity

Abstract

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.

What the odor is not: Estimation by elimination

“…Similar to this decoder, projections from the olfactory bulb to the cortex seem to be statistically random [65] rather than structured, and give rise to a sparse, distributed representation of odors in cortex [17], as opposed to a literal decoding of odorant concentrations. Some authors have proposed that the random projections to cortex are a mechanism for creating sparse, high dimensional representations suitable for downstream linear classification [21, 39, 65], or are evidence for compressive sensing in olfaction [18, 25]. Others have suggested that compressive sensing occurs at the receptors [19, 27], and that the random projections reformat the compressed data for downstream decoding [27].…”

“…Population models suggest that odor identity and intensity could be represented in dynamical response patterns [31, 32], where different odors activate distinct attractor network patterns in a winnerless competition [22, 30, 33], or in transient [34] or oscillatory activity [35]. Finally, population activity could be a low-dimensional projection of odor space [36, 37] evolving in space and time to decorrelate odors [38] to maximally separate sparse representations of similar odors [21].…”

A spiking neural program for sensorimotor control during foraging in flying insects

“…Experimental (39) and theoretical (20,56) studies in the fruit fly suggest that inhibitory feedback through the anterior paired lateral (APL) neuron improves population sparseness and learning in the KC population. In the adult fly, it likely receives input in both the calyx and the lobes of the MB, and it is thought to widely inhibit KCs and possibly PN synaptic boutons that are presynaptic to KCs in the calyx.…”

“…For PNs and LNs SFA has been turned off and KCs are set to produce fast and strong adaptation currents (18,84). The property of temporal sparseness can also be achieved by an alternative implementation through feedback inhibition as proposed by (56,85).…”

A spiking neural program for sensory-motor control during foraging in flying insects

“…For PNs and LNs SFA has been turned off and KCs are set to produce fast and strong adaptation currents (18,66). The property of temporal sparseness can also be achieved by an alternative implementation through feedback inhibition as proposed by (53) and (67).…”