A spike based learning rule for generation of invariant representations

Long-Range Monosynaptic Inputs Targeting Apical and Basal Dendrites of Primary Motor Cortex Deep Output Neurons

The primary motor cortex (M1) integrates various long-range signals from other brain regions for the learning and execution of goal-directed movements. How the different inputs target the distinct apical and basal dendrites of M1 pyramidal neurons is crucial in understanding the functions of M1, but the detailed connectivity pattern is still largely unknown. Here, by combining cre-dependent rabies virus tracing, layer-specific chemical retrograde tracing, optogenetic stimulation, and electrophysiological recording, we mapped all long-range monosynaptic inputs to M1 deep output neurons in layer 5 (L5) in mice. We revealed that most upstream areas innervate both dendritic compartments concurrently. These include the sensory cortices, higher motor cortices, sensory and motor thalamus, association cortices, as well as many subcortical nuclei. Furthermore, the dichotomous inputs arise mostly from spatially segregated neuronal subpopulations within an upstream nucleus, and even in the case of an individual cortical layer. Therefore, these input areas could serve as both feedforward and feedback sources albeit via different subpopulations. Taken together, our findings revealed a previously unknown and highly intricate synaptic input pattern of M1L5 neurons, which implicates that the dendritic computations carried out by these neurons during motor execution or learning are far more complicated than we currently understand.

Inter-synaptic learning of combination rules in a cortical network model

Selecting responses in working memory while processing combinations of stimuli depends strongly on their relations stored in long-term memory. However, the learning of XOR-like combinations of stimuli and responses according to complex rules raises the issue of the non-linear separability of the responses within the space of stimuli. One proposed solution is to add neurons that perform a stage of non-linear processing between the stimuli and responses, at the cost of increasing the network size. Based on the non-linear integration of synaptic inputs within dendritic compartments, we propose here an inter-synaptic (IS) learning algorithm that determines the probability of potentiating/depressing each synapse as a function of the co-activity of the other synapses within the same dendrite. The IS learning is effective with random connectivity and without either a priori wiring or additional neurons. Our results show that IS learning generates efficacy values that are sufficient for the processing of XOR-like combinations, on the basis of the sole correlational structure of the stimuli and responses. We analyze the types of dendrites involved in terms of the number of synapses from pre-synaptic neurons coding for the stimuli and responses. The synaptic efficacy values obtained show that different dendrites specialize in the detection of different combinations of stimuli. The resulting behavior of the cortical network model is analyzed as a function of inter-synaptic vs. Hebbian learning. Combinatorial priming effects show that the retrospective activity of neurons coding for the stimuli trigger XOR-like combination-selective prospective activity of neurons coding for the expected response. The synergistic effects of inter-synaptic learning and of mixed-coding neurons are simulated. The results show that, although each mechanism is sufficient by itself, their combined effects improve the performance of the network.

Cortical region interactions and the functional role of apical dendrites

The basal and distal apical dendrites of pyramidal cells occupy distinct cortical layers and are targeted by axons originating in different cortical regions. Hence, apical and basal dendrites receive information from distinct sources. Physiological evidence suggests that this anatomically observed segregation of input sources may have functional significance. This possibility has been explored in various connectionist models that employ neurons with functionally distinct apical and basal compartments. A neuron in which separate sets of inputs can be integrated independently has the potential to operate in a variety of ways not possible for the conventional neuron model, in which all inputs are treated equally. This article thus considers how functionally distinct apical and basal dendrites can contribute to the information-processing capacities of single neurons and, in particular, how information from different cortical regions could have disparate effects on neural activity and learning.