Archive for November, 2010

Synaptic inputs that are often active together are strengthened during learning so that statistical regularities in synaptic input lead to the post-synaptic neuron being selective to particular stimuli whilst also rendering it invariant to accidental features.Neural hierarchies allow neurons at higher level to capture information gained by many neurons at lower levels. A stimulus that drives a neuron at a high-level in a network hierarchy will almost always be part of a (visual or other) scene together with other stimuli. So although invariance aids object recognition when there are changes to irrelevant aspects of the stimulus, it also causes a problem because a given stimulus will never cover the complete receptive field of a high-level neuron but leave room for competing stimuli. This selective efficacy of subsets of a neuron’s input may be aided if converging neuronal inputs to higher-level neurons are functionally segmented and if only a relevant segment is selected at a time. Pascal Fries believes that whist connectivity provides selectivity and invariance, synchronisation provides the required segmentation and selection of a segment.

Gamma-band synchronization (a group or groups of neurons pulse firing together at the rate of 40-80 Hz) can emerge in a network of excitatory and inhibitory neurons. Inhibitory neurons provide shunting inhibition that stops other neurons from firing. This provides windows for synchrony at the moment inhibition wears off. Excitatory signals can then take advantage. Gamma band oscillations are sufficiently regular to allow prediction of the next excitability peak. As long as the travelling time from the sending to the receiving group is also reliable, their communication windows for input and output are open at the same times. Conduction delays between neurons are typically and order of magnitude shorter than the cycle length of the oscillation allowing sending and receiving to occur within one excitability peak. Packages of spikes can therefore arrive at other neuronal groups in precise synchronization and enhance their impact. Rhythmic inhibition therefore provides rhythmic modulation of excitatory input gain. Fries considers the mechanistic consequences of neuronal oscillations and calls this hypothesis ‘communication through coherence’.

Coincidence detection and rhythmic gain modulation create an exclusive communication link between a target group and a strongly synchronised source group. If there is a synchronization among the neurons in groups A and among the neurons in group B but not between A and B then a down stream group C will either synchronise to A or B but not both at the same time. Strong and precise gamma band synchronisation within group A will trigger many spikes in C and entrain C to the rhythm of A. Once entrained a winner-takes-all effect occur as the result of input gain. A competitive advantage is therefore given to one group of neurons.(read here for more details)

Uhlhaas et al note that the fast switching between synchronized and de-synchronized states observed in the data seems at odds with: the coupling strength that can be achieved through synaptic plasticity, the speed of changes in the functional topology, and mechanisms that could cause changes in transmission delays. Hence they conclude that the most likely option for the modulation of synchrony is to change the dynamical states of the coupled neuronal populations, such as the balance between excitation and inhibition. So in addition to the firing rates, precise timing of individual discharges may be used to gate transmission and synaptic plasticity, to selectively route activity across the cortical network and to define particular relations in distributed activity patterns.

Singer notes that such synchro­nous events are statistically improbable so their information content is high. Therefore they are likely to be very effective in eliciting responses in target populations. For the system to work, individual cells need to be able to rapidly change their synchronisation partners if new associations are required due to a change in Gestalt properties of the scene, and if more than one object is present in a scene several distinct assemblies should form.


Masquelier et al found evidence in support of the belief that spike timing dependant plasticity (STDP) makes the post-synaptic neuron respond more quickly. In their model multiple afferents converge upon a single post-synaptic neuron. Interestingly their work does not demand that a pattern to be learnt be present in all spike volleys. Distractor spike volleys are not only present in between presentations of the learned pattern, but in addition a constant population firing rate is effective throughout all the stimuli to ensure that the what network learns is not a side effect of conditions other that the coincidence of the pattern to be learned being repeated. Confirming earlier conclusions STDP first of all leads to an overall weakening of synapses, but by reinforcing the synaptic connections with the afferents that took part in firing the neuron when the pattern to be learned was present it then increases the probability that the neuron fires again next time the pattern is presented. After only 70 pattern presentations the neuron stops discharging outside of the pattern presentation. Though at first chance determines which part of the pattern the neuron becomes selective to, by reinforcing the connections to pre-synaptic neurons that fired slightly before the post-synaptic neuron the post-synaptic neuron learns to discharge earlier on presentation of the desired stimulus.

Masquelier et al have extended their model to make it respond to multiple patterns by using multiple post-synaptic neurons with inhibitory connections between them. In this case, the first neuron to fire inhibit others so it only one of the post-synaptic neurons to respond to each stimuli. However, because of the simplicity of this feed forward model and because additive STDP creates a bimodal weight distribution (see this post) distributed around 0 and MAX, in this case MAX being equal to 1, afferents are effectively turned on or off. One can only conclude that STDP is just becoming selective to particular inputs that happen to correspond to part of the stimulus to be learned that are good at identifying the desired stimulus and not the distractor. Network structure only is what is providing the computational power here. Further interesting work would proceed by studying more complex structures than simple feed-forward mechanisms by introducing reciprocal connections. I shall report on these later. For now, off to the pub 🙂