A retrograde mechanism coordinates memory allocation across brain regions

Memories engage ensembles of neurons across different brain regions within a memory system. However, it is unclear whether the allocation of a memory to these ensembles is coordinated across brain regions. To address this question, we used CREB expression to bias memory allocation in one brain region, and rabies retrograde tracing to test memory allocation in connected presynaptic neurons in the other brain regions. We find that biasing allocation of CTA memory in the basolateral amygdala (BLA) also biases memory allocation in presynaptic neurons of the insular cortex (IC). By manipulating the allocation of CTA memory to specific neurons in both BLA and IC, we found that we increased their connectivity and enhanced CTA memory performance. These results, which are corroborated by mathematical simulations and by studies with auditory fear conditioning, demonstrate that a retrograde mechanism coordinates the allocation of memories across different brain regions.

Learning with Precise Spike Times: A New Decoding Algorithm for Liquid State Machines

There is extensive evidence that biological neural networks encode information in the precise timing of the spikes generated and transmitted by neurons, which offers several advantages over rate-based codes. Here we adopt a vector space formulation of spike train sequences and introduce a new liquid state machine (LSM) network architecture and a new forward orthogonal regression algorithm to learn an input-output signal mapping or to decode the brain activity. The proposed algorithm uses precise spike timing to select the presynaptic neurons relevant to each learning task. We show that using precise spike timing to train the LSM and selecting the readout presynaptic neurons leads to a significant increase in performance on binary classification tasks, in decoding neural activity from multielectrode array recordings, as well as in a speech recognition task, compared with what is achieved using the standard architecture and training methods.

Excitatory and inhibitory intracortical circuits for orientation and direction selectivity

The computations performed by a neuron arise from the functional properties of the circuits providing its synaptic inputs. A prime example of these computations is the selectivity of primary visual cortex (V1) for orientation and motion direction. V1 neurons in layer 2/3 (L2/3) receive input mostly from intracortical circuits1, which involve excitation2-9 and inhibition10-12. To understand how an L2/3 neuron achieves its selectivity, therefore, one must characterize the functional organization of both its excitatory and inhibitory presynaptic ensembles. Here we establish this organization, and show how it predicts orientation selectivity and reveals a new cortical circuit for direction selectivity. We identified the presynaptic partners of pyramidal neurons in mouse V1 through rabies monosynaptic tracing1,13, and imaged the functional properties of the postsynaptic neuron and of its presynaptic ensemble. Excitatory presynaptic neurons were predominantly tuned to the postsynaptic neuron’s preferred orientation. Excitation and inhibition described an inverted Mexican hat, with inhibitory presynaptic neurons densest near the postsynaptic neuron and excitatory ones distributed more distally. Excitation and inhibition also differed in laminar origin: inhibitory presynaptic neurons concentrated in L2/3 while excitatory ones dominated in L4. The distribution of excitatory neurons in visual space was coaxial with the postsynaptic neuron’s preferred orientation and lay upstream of the neuron’s preferred direction. Inhibitory presynaptic neurons, instead, clustered more symmetrically around the postsynaptic neuron and favoured locations downstream of its preferred direction. These results demonstrate that L2/3 neurons obtain orientation selectivity from co-tuned neurons in L4 and beyond, and enhance it by contrasting an elongated excitatory input with a concentric inhibitory input. Moreover, L2/3 neurons can obtain direction selectivity through visually offset14 excitation and inhibition. These circuit motifs resemble those seen in the thalamocortical pathway15-20 and in direction selective cells in the retina21,22, suggesting that they are canonical across brain regions.

Optimizing optogenetic stimulation protocols in auditory corticofugal neurons based on closed-loop spike feedback

Optogenetics provides a means to probe functional connections between brain areas. By activating a set of presynaptic neurons and recording the activity from a downstream brain area, one can establish the sign and strength of a feedforward connection. One challenge is that there are virtually limitless patterns that can be used to stimulate a presynaptic brain area. Functional influences on downstream brain areas can depend not just on whether presynaptic neurons were activated, but how they were activated. Corticofugal axons from the auditory cortex (ACtx) heavily innervate the auditory tectum, the inferior colliculus (IC). Despite the anatomical weight of this connection, optogenetic activation of ACtx neurons produced only modest changes in the IC neuron firing rates. To determine whether different modes of cortical activation could more faithfully reveal the strength of feedforward connectivity, we employed a closed-loop evolutionary optimization procedure that tailored voltage command signals to the laser based on firing rate variations recorded from single units in the IC of awake male and female mice. Within minutes, the evolutionary search procedure converged on ACtx stimulation configurations that produced more effective and widespread enhancement of IC unit activity than generic activation parameters. Cortical modulation of midbrain spiking was bi-directional, as the evolutionary search procedure could be programmed to converge on activation patterns that suppressed or enhanced sound-evoked IC firing rate. These findings demonstrate that the feedforward influence between brain areas can vary both in sign and degree depending on how presynaptic neurons are activated in time.

Information Maximization Explains the Sparseness of Presynaptic Neural Response

In a sensory neural network, where a population of presynaptic neurons sends information to a downstream neuron, maximizing information transmission depends on utilizing the full operating range of the output of the postsynaptic neuron. Because the convergence of presynaptic inputs naturally biases higher outputs, a sparse input distribution would counter such bias and optimize information transmission.

Glial and neuronal Semaphorin signaling instruct the development of a functional myotopic map for Drosophila walking

Motoneurons developmentally acquire appropriate cellular architectures that ensure connections with postsynaptic muscles and presynaptic neurons. In Drosophila, leg motoneurons are organized as a myotopic map, where their dendritic domains represent the muscle field. Here, we investigate mechanisms underlying development of aspects of this myotopic map, required for walking. A behavioral screen identified roles for Semaphorins (Sema) and Plexins (Plex) in walking behavior. Deciphering this phenotype, we show that PlexA/Sema1a mediates motoneuron axon branching in ways that differ in the proximal femur and distal tibia, based on motoneuronal birth order. Importantly, we show a novel role for glia in positioning dendrites of specific motoneurons; PlexB/Sema2a is required for dendritic positioning of late-born motoneurons but not early-born motoneurons. These findings indicate that communication within motoneurons and between glia and motoneurons, mediated by the combined action of different Plexin/Semaphorin signaling systems, are required for the formation of a functional myotopic map.