Synaptic depression increases the selectivity of a neuron to its preferred pattern and binarizes the neural code

Modeling fast stimulus-response association learning along the occipito-parieto-frontal pathway following rule instructions

On the basis of instructions, humans are able to set up associations between sensory and motor areas of the brain separated by several neuronal relays, within a few seconds. This paper proposes a model of fast learning along the dorsal pathway, from primary visual areas to pre-motor cortex. A new synaptic learning rule is proposed where synaptic efficacies converge rapidly toward a specific value determined by the number of active inputs of a neuron, respecting a principle of resource limitation in terms of total synaptic input efficacy available to a neuron. The efficacies are stable with regards to repeated arrival of spikes in a spike train. This rule reproduces the inverse relationship between initial and final synaptic efficacy observed in long-term potentiation (LTP) experiments. Simulations of learning experiments are conducted in a multilayer network of leaky integrate-and-fire (LIF) spiking neuron models. It is proposed that cortical feedback connections convey a top-down learning-enabling signal that guides bottom-up learning in "hidden" neurons that are not directly exposed to input or output activity. Simulations of repeated presentation of the same stimulus-response pair, show that, under conditions of fast learning with probabilistic synaptic transmission, the networks tend to recruit a new sub-network at each presentation to represent the association, rather than re-using a previously trained one. This increasing allocation of neural resources results in progressively shorter execution times, in line with experimentally observed reduction in response time with practice. This article is part of a Special Issue entitled: Neural Coding.

Determination of the fraction of active inputs required by a neuron to fire

What fraction of the inputs to a neuron in the primary visual cortex (V1) need to be active for that neuron to reach its firing threshold? The paper describes a numerical method for estimating the selectivity of visual neurons, in terms of the required fraction of active excitatory inputs, from standard data produced by intracellular electro-physiological recordings. The method also provides an estimate of the relative strength of the feedforward inhibition in a push-pull model of the inputs to V1 simple cells. The method is tested on two V1 cells described in Carandini and Ferster [Carandini, M., Ferster, D., 2000. Membrane potential and firing rate in cat primary visual cortex. J. Neurosci. 20, 470-484]. The results indicate that the maximum strength of feedforward inhibition is around 30% of the maximum strength of feedforward excitation. The two V1 neurons investigated fire if more than around 40% of their excitatory LGN inputs are active.

Analog VLSI implementation of resonate-and-fire neuron

We propose an analog integrated circuit that implements a resonate-and-fire neuron (RFN) model based on the Lotka-Volterra (LV) system. The RFN model is a spiking neuron model that has second-order membrane dynamics, and thus exhibits fast damped subthreshold oscillation, resulting in the coincidence detection, frequency preference, and post-inhibitory rebound. The RFN circuit has been derived from the LV system to mimic such dynamical behavior of the RFN model. Through circuit simulations, we demonstrate that the RFN circuit can act as a coincidence detector and a band-pass filter at circuit level even in the presence of additive white noise and background random activity. These results show that our circuit is expected to be useful for very large-scale integration (VLSI) implementation of functional spiking neural networks.