An adaptable neuromorphic model of orientation selectivity based on floating gate dynamics

Abstract

The biggest challenge that the neuromorphic community faces today is to build systems that can be considered truly cognitive. Adaptation and self-organization are the two basic principles that underlie any cognitive function that the brain performs. If we can replicate this behavior in hardware, we move a step closer to our goal of having cognitive neuromorphic systems. Adaptive feature selectivity is a mechanism by which nature optimizes resources so as to have greater acuity for more abundant features. Developing neuromorphic feature maps can help design generic machines that can emulate this adaptive behavior. Most neuromorphic models that have attempted to build self-organizing systems, follow the approach of modeling abstract theoretical frameworks in hardware. While this is good from a modeling and analysis perspective, it may not lead to the most efficient hardware. On the other hand, exploiting hardware dynamics to build adaptive systems rather than forcing the hardware to behave like mathematical equations, seems to be a more robust methodology when it comes to developing actual hardware for real world applications. In this paper we use a novel time-staggered Winner Take All circuit, that exploits the adaptation dynamics of floating gate transistors, to model an adaptive cortical cell that demonstrates Orientation Selectivity, a well-known biological phenomenon observed in the visual cortex. The cell performs competitive learning, refining its weights in response to input patterns resembling different oriented bars, becoming selective to a particular oriented pattern. Different analysis performed on the cell such as orientation tuning, application of abnormal inputs, response to spatial frequency and periodic patterns reveal close similarity between our cell and its biological counterpart. Embedded in a RC grid, these cells interact diffusively exhibiting cluster formation, making way for adaptively building orientation selective maps in silicon.

Emergent spatial synaptic structure from diffusive plasticity

“…As the BCM learning rule is equivalent to a biologically realistic triplet STDP rule for rate‐based patterns (Gjorgjieva et al ., ), we would expect qualitatively similar results in a network of spiking neurons. The consequences of diffusive neurotransmitters mediating other forms of Hebbian or homeostatic plasticity have been explored previously by Kohonen (; Smith et al ., ; Bhaumik & Mathur, ; Yin et al ., ; Savin et al ., ; Gupta & Markan, ; Sweeney et al ., ). Although we remain agnostic to the identity of the diffusive neurotransmitter which mediates synaptic plasticity in our proposed dBCM learning rule, there are a number of candidates which have been previously identified.…”

Research topic: neuromorphic engineering systems and applications. A snapshot of neuromorphic systems engineering

“…Gupta and Markan ( 2014 ), report on a FG adaptive system for investigating self-organization of image patterns. They describe adaptive feature selectivity as a mechanism by which nature optimizes resources so as to have greater acuity for more abundant features.…”

“…Two papers are on the hot topic (based on largest number of views) of event-driven computation in deep belief networks (DBNs) (O'Connor et al, 2013 ; Neftci et al, 2014 ). Two papers use floating gate technology for neuromorphic analog circuits (Gupta and Markan, 2014 ; Marr and Hasler, 2014 ). The collection is rounded out by papers on central pattern generators (Ambroise et al, 2013 ), neural fields for cognitive architectures (Sandamirskaya, 2014 ), sound perception (Coath et al, 2014 ), polychronous spiking networks (Wang et al, 2014 ), and automatic parameter tuning for large network simulations (Carlson et al, 2014 ).…”

Adaptive Neuromorphic Circuit for Stereoscopic Disparity Using Ocular Dominance Map

“…Another digital approach proposed by [ 13 ] uses a modified phase based technique to create a SOC using FPGAs that can be used in embedded systems. While digital approaches are known for their accuracy and speed, it has been emphasized that analog approaches more closely replicate the computations in the brain [ 14 ] and are ideal when it comes to emulating local computations in the brain [ 15 ]. Some purely analog models based on sparse disparity computations are there in literature, for example, [ 16 , 17 ].…”

“…In this context, the work presented in this paper explores a novel approach, using purely analog hardware, to build a disparity selective neuron, which is closer to biology, since it leverages the organization of ocular dominance columns to create an adaptive cell based on time-staggered Winner Take All competition implemented using floating gate pMOS dynamics [ 32 ]. Floating gate based analog hardware emulates synaptic dynamics very closely and has been used in various neuromorphic applications for introducing adaptation [ 33 ] and long-term memory [ 15 , 32 , 34 ]. It has also been used by us to create adaptive feature maps for ocular dominance and orientation selectivity [ 15 , 32 ].…”