Transform-invariant visual representations in self-organizing spiking neural networks

• Main Authors: Benjamin eEvans, Simon eStringer
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2012-07-01
• Series: Frontiers in Computational Neuroscience
• Subjects: Visual Perception; inferior temporal cortex; integrate and fire; continuous transformation learning; Spike-Time Dependent Plasticity; spiking neural net
• Online Access: http://journal.frontiersin.org/Journal/10.3389/fncom.2012.00046/full

The ventral visual pathway achieves object and face recognition by building transform-invariant representations from elementary visual features. In previous computer simulation studies with rate-coded neural networks, the development of transform invariant representations has been demonstrated using either of two biologically plausible learning mechanisms, Trace learning and Continuous Transformation (CT) learning. However, it has not previously been investigated how transform invariant representations may be learned in a more biologically accurate spiking neural network. A key issue is how the synaptic connection strengths in such a spiking network might self-organize through Spike-Time Dependent Plasticity (STDP) where the change in synaptic strength is dependent on the relative times of the spikes emitted by the pre- and postsynaptic neurons rather than simply correlated activity driving changes in synaptic efficacy. Here we present simulations with conductance-based integrate-and-fire (IF) neurons using a STDP learning rule to address these gaps in our understanding. It is demonstrated that with the appropriate selection of model pa- rameters and training regime, the spiking network model can utilize either Trace-like or CT-like learning mechanisms to achieve transform-invariant representations.